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US 7921099 B2 Abstract A navigation system is used in combination with a data retrieval system to more effectively locate the correct answers or information in search content. The navigation system provides taxonomies that identify different information categories and sub-categories (facets). Classifiers automatically classify the search content by identifying the content corresponding with associated facets. The facets, in conjunction with the associated classifiers, provide robust classification and correlation between both structured and unstructured content. Rules can be used in conjunction with the facets and classifiers to automatically control different navigation operations. The navigation system can also leverage ontologies to dynamically create taxonomies and provides unique facet expansion and intra-document classification operations.
Claims(13) 1. A computer-implemented method for classifying content, comprising:
(A) receiving, at a processor and as part of performing a query, both structured and unstructured content;
(B) providing a taxonomy having an entire set of preexisting facets associated with different subject categories;
(C) using, by the processor, a structured classifier to classify the structured content with one or more of the preexisting facets in the taxonomy;
(D) using, by the processor, a different unstructured classifier to classify the unstructured content with one or more of the same preexisting facets in the same taxonomy;
(E) displaying, in response to the query, the one or more preexisting facets classified with the structured content and classified with the unstructured content;
(F) associating an ontology with the structured and unstructured content that includes at least one concept having different categories and sub-categories;
(G) inserting a portion of the ontology into the taxonomy;
(H) using the categories and sub-categories for the inserted portion of the ontology as facets for classifying the structured and unstructured content;
(I) conducting the query to return search results;
(J) comparing concept categories in the ontology with data items contained in the search results;
(K) identifying the concept categories associated with the data items from the search results;
(L) dynamically and automatically creating new facets in the taxonomy according to the concept categories in the ontology corresponding with the search results, the dynamically and automatically created new facets being different than any of the preexisting facets that currently exist in the entire taxonomy; and
(M) using the new facets along with the entire set of preexisting facets to classify both the structured and unstructured content.
2. A computer-implemented method for classifying content, comprising:
(A) receiving, at a processor and as part of performing a query, search results including both structured and unstructured content;
(B) providing a taxonomy having an entire set of preexisting facets associated with different subject categories;
(C) using, by the processor, a structured classifier to classify the structured content with one or more particular preexisting facets in the taxonomy;
(D) using, by the processor, a different unstructured classifier to classify the unstructured content with at least one of the particular preexisting facets;
(E) displaying, in response to the query, those ones of the particular preexisting facets that are classified with both the structured content and the unstructured content;
(F) associating an ontology with the structured and unstructured content that includes at least one concept having different categories and sub-categories;
(G) inserting a portion of the ontology into the taxonomy;
(H) using ones of the subject categories that correspond to the inserted portion of the ontology as facets for classifying the structured and unstructured content;
(I) comparing data items contained in the search results to the ontology;
(J) identifying a particular concept of the ontology according to the comparison;
(K) dynamically and automatically creating a new facet in the taxonomy according to the identified concept, the new facet being different than the preexisting facets of the set; and
(L) using the new facet along with the preexisting facets of the set to classify both the structured and unstructured content.
3. A computer-implemented method for classifying content, comprising:
(A) receiving, at a processor and as part of performing a query, search results including both structured and unstructured content;
(B) providing a taxonomy having an entire set of preexisting facets associated with different subject categories;
(C) using, by the processor, a structured classifier to classify the structured content with one or more particular preexisting facets in the taxonomy;
(D) using, by the processor, a different unstructured classifier to classify the unstructured content with at least one of the particular preexisting facets;
(E) displaying, in response to the query, those ones of the particular preexisting facets that are classified with both the structured content and the unstructured content;
(F) comparing data items contained in the search results to an ontology;
(G) identifying a particular concept of the ontology according to the comparison;
(H) dynamically and automatically creating a new facet in the taxonomy according to the identified concept, the new facet being different than the preexisting facets of the set;
(I) using the new facet along with the preexisting facets of the set to classify both the structured and unstructured content;
(J) comparing the facets classified with the query to a list containing a subset of the set of preexisting facets, the facets of the subset associated with advertisements; and
(K) if the comparison indicates a match, displaying a particular one of the advertisements according to a result of the comparison.
4. The computer-implemented method of
building new classifiers for the facets created from the inserted portion of the ontology; and
using the new classifiers to classify the structured and unstructured content with the facets created from the inserted portion of the ontology.
5. The computer-implemented method of
6. The computer-implemented method of
7. The computer-implemented method of
8. The computer-implemented method of
assigning identifiers to data items in both the structured and unstructured content;
using the structured classifier to generate a list of the identifiers for the data items in the structured content associated with the respective particular preexisting facets;
using the unstructured classifier to add the identifiers to the same list for the data items in the unstructured content associated with the respective particular preexisting facets; and
displaying the number of data items in both the structured and unstructured content associated with the displayed preexisting facets by counting the number of identifiers in the list that are possible answers to the query.
9. The computer-implemented method of
identifying data items located in either the structured or unstructured content associated with a selected one of the preexisting facets of the set;
automatically creating new facets that are configured as sub-categories for the selected facet, said newly created facets generated from the data items identified in the structured or unstructured content and not previously existing anywhere in the entire taxonomy prior to receiving the structured or unstructured content;
building a new taxonomy that includes the new facets that are sub-categories underneath the selected facet; and
classifying the data items according to the new taxonomy.
10. The computer-implemented method of
11. The computer-implemented method of
12. The computer-implemented method of
identifying data items located in the structured content associated with a selected one of the preexisting facets of the set;
automatically creating new facets that are configured as sub-categories for the selected facet, said newly created facets generated from the data items identified in the structured content and not previously existing anywhere in the entire taxonomy prior to receiving the structured content;
building a new taxonomy that includes the new facets that are sub-categories underneath the selected facet; and
classifying the data items according to the new taxonomy.
13. The computer-implemented method of
building new classifiers for the facets created from the ontology;
using the new classifiers to classify the content with the facets created from the inserted portion of the ontology.
Description Current search engines receive a search request entered by a user and then identify and present the search results back to the search requester via a User Interface (UI). The search results may be ranked in some order based on, for example, the presence of important words or concepts in the identified documents. Unfortunately, these search and display techniques often do not efficiently organize the information sought by the searcher. For example, the information or document sought by the user may be buried in one of the many different documents or data returned in the search results. The user is then required to manually open and read dozens, if not hundreds, of the data items returned from the search results before identifying the correct, or most pertinent document. In yet another situation, the search requester may not know the correct search terms needed to direct or narrow a search to discover the desired document or information. Thus, the user is relegated to conducting multiple different time-consuming searches attempting to locate the correct group of documents or information. The user has to then wade through all of the results from each search until a particular document or piece of information is discovered. The present invention addresses this and other problems associated with the prior art. A navigation system is used in combination with a data retrieval system to more effectively locate the correct answers or information in search content. The navigation system provides taxonomies that identify different information categories and sub-categories (facets). Classifiers automatically classify the search content by identifying the content corresponding with associated facets. The facets, in conjunction with the associated classifiers, provide robust classification and correlation between both structured and unstructured content. Rules can be used in conjunction with the facets and classifiers to automatically control different navigation operations. The navigation system can also leverage ontologies to dynamically create taxonomies and provides unique facet expansion and intra-document classification operations. The foregoing and other objects, features and advantages of the invention will become more readily apparent from the following detailed description of a preferred embodiment of the invention which proceeds with reference to the accompanying drawings. Other web servers 26 may operate outside of the enterprise 12 and may include associated web files or other web content 28. Examples of content stored in enterprise database 16 and in file server 28 may include HTML web pages, PDF files, Word� documents, structured database information or any other type of electronic content that can contain essentially any type of information. Some of the information may be stored in a structured format referred to generally as structured content. For example, data may be stored in the enterprise database 16 in a preconfigured format specified for enterprise 12. For example, a book or vehicle price list may be considered structured content. Alternatively, other information that is contained in enterprise database 16, or contained on other web servers 26, may be considered non-structured content. This may include HTML web pages, text documents, or any other type of free flowing text or data that is not organized in a preconfigured data format known by the navigation system 20. A search query may be initiated from a terminal 25 through a User Interface (UI) 30. The terminal 25 in one example may be a Personal Computer (PC), laptop computer 24, wireless Personal Digital Assistant (PDA), cellular telephone, or any other wired or wireless device that can access and display content over a packet switched network. In this example, the search query is initiated from the UI 30 and transported over the Internet 22 to the enterprise server 14. The enterprise server 14 operates a data retrieval system 18 that may search for both structured and/or non-structured content either in the enterprise database 16 or web server 26 pursuant to the query initiated in UI 30. A novel navigation system 20 is then used to classify and navigate through the search results provided by the data retrieval system 18. The information as classified by the navigation system 20, along with other taxonomy information, is then displayed on the UI 30. The enterprise server 14 includes one or more processors that are configured to operate the data retrieval system and navigation system 20. The operations performed by the navigation system could be provided by software computer instructions that are stored in a computer readable storage medium, such as memory on server 14. The instructions are then executed by a processor in server 14. It should be understood that the examples presented below are used for illustrative purposes only and the scope of the invention is not limited to any of the specific examples described below. Classification of Content The data retrieval system 18 in The answers 38 may also contain unstructured content 42 that may not have pre-defined data fields. For example, a first book review 42A may have been located somewhere in the enterprise database 16 or on a web server 27 and may describe a book related in some way to the question DRAGON. Similarly, another review 42B may be identified on the enterprise database 16 or on another web server 26 that is somehow also related to the DRAGON question 36. This content 42 is considered unstructured or �free-flowing� because the content is not presented in any particular preconfigured format. For example, the reference to a book related to DRAGON may be located anywhere in the book reviews 42A and 42B. Of particular interest in the UI 30 is the display of a taxonomy 34 that identifies different classifications and sub-classifications for the content in answers 38. These classifications and sub-classifications are alternatively referred to as facets. In this example, the taxonomy 34 includes a first price taxonomy 34A that includes a first set of facets 35A that are associated with book prices, a second year taxonomy 34B that includes a set of facets 35B associated with book publication dates, and a third author taxonomy 34C that includes a set of facets 35C associated with book authors. Different taxonomies 34 may be preconfigured in the navigation system 20. The taxonomies 34 associated with question 36, or with a particular enterprise, are selected by the navigation system, or the user, and displayed in UT 30. In some applications, some or all of the taxonomy is dynamically generated by the navigation system. This will be described in more detail below. An associated number 46 may be displayed that indicates the number of content items 40 and 42 that correspond with a particular facet 35. For example, the search results (answers 38) for DRAGON may have produced a total of 74 structured content items 40 and/or unstructured content items 42 related to DRAGON books in a price range between $0-$5. Similarly, the navigation system 20 may have identified a combination of twenty-two structured content items 40 and/or unstructured content items 42 related to books published between 1980 and 1989. A process referred to as classification automatically classifies the content used to produce the answers 38 with different facets 35 in taxonomy 34. Classification is also described in more detail below. When a user selects one of the facets 35 in taxonomy 34, the navigation system 20 automatically displays the structured and unstructured content 40 and 42, respectively, classified for the selected facet 35. For example, selecting the $5-$10 facet 35A causes the navigation system to display twenty-six answers 38 that are all associated somehow with dragon books within a price range of $5-$10. Taxonomies Taxonomy: Genre Fiction
Non Fiction
As mentioned above, a facet 35 is another name for an item in a taxonomy (a �taxonomy item�). The facets 35 can be referred to by a string constructed from the names of the facets along the path from the root of taxonomy to the facet. For example, each name may be separated by a period, �.�, and all other periods may be preceded by a backslash. For example, the 1980-89 facet in Seamless Association of Structured and Unstructured Content In Briefly, the IML 50 allows content searching for documents that contain the same �concept� that is described in the question 36, for example. The navigation system 20 then provides navigation of the content provided by the IML 50 or can be used with any other data retrieval system that does not use IML 50. As described above in As previously mentioned in The navigation system 30 uses classifiers 54 and 56 to seamlessly associate both the structured content 40 and the unstructured content 42 with the same associate facets 35. The taxonomy 34 by itself is just an association of classification items and has no direct connection to the content 52. The connection between content 52 and specific facets 35 is provided by the classifiers 54 and 56. These classifiers 54 and 56 include procedures that identify the pieces of content 52 that are associated with particular facets 35. For example, the structured content classifier 54 may be associated with price facet 35A that identifies a book price range category of $0-$5. Accordingly, the structured classifier 54 may include procedures that access the price field 44D in structured content 40A and identifies any books with a price between $0-$5. Of particular interest is the association of the structured classifier 54 and the unstructured classifier 56 with the same facet 35A. Unstructured classifier 56 includes procedures that parse through the unstructured content 42 for any documents that may indicate a book price in a range between $0-$5. For example, the unstructured content classifier 56 may parse through web pages to identify metadata that refers to a book having a price between $0-$5. In one example, each piece of content 52 is assigned an associated Identifier (ID) 58 during the data retrieval process by data retrieval system 18. Upon identifying content 52 corresponding with the associated facet 35, the classifiers 54 and 56 then load the document ID 58 for the identified content into an associated facet list 59A. There are ID lists 59A-59C associated with each facet. In this example, the unstructured document 42A is identified by unstructured classifier 56 as associated with facet 35A and the associated ID 58A entered into list 59A. Similarly, the structured document 40B is identified by structured classifier 54 as associated with facet 35A and the associated ID 58B entered into facet list 59A. When the taxonomy 34 is displayed on UI 30 in It should be understood that any combination of different classifiers 54 and 56 can be associated with any combination of facets 35. In the example above, both a structured classifier 54 and a unstructured classifier 56 are each associated with a same facet 35A. However, there could be additional structured or unstructured classifiers associated with the same facet 35A. For example, facet 35A may use another, different structured classifier to classify other structured content 40B. Further, other types of unstructured classifiers may be associated with facet 35A to parse through different types of unstructured content 42. There also may be one classifier associated with multiple different facets 35. For example, a classifier may be abstracted to identify any content 52 that indicates a book price. A taxonomy builder used by the classifier may then further refine classification of the identified content down to specific price ranges associated with the different price facets 35A, 35B, and 35C. This is described in more detail below. In another embodiment, a same classifier may include procedures for identifying facet related content in both the structured and unstructured content 40 and 42, respectively. However there may be advantages to using separate structured and unstructured classifiers such as reduced software modification and increased software efficiency for different structured and unstructured data formats. Some of the classifiers, such as unstructured classifier 56 may also use the IML 50 to more effectively identify content 52 associated with a particular facet 35. For example, the intelligent association of different concepts with a particular facet 35 allows the classifier 56 to identify content 42 that may not necessarily use the exact same words used in the facet. For instance, the IML 50 in unstructured classifier 56 may be used to identify unstructured content 42 that contains a book price but does not necessarily use the symbol �$�, or use the word �dollar�. For example, the IML 50 may associate the symbol �$� and the word �dollar� with the words �Euro�, �bucks�, �cost�, �Yen�, etc. The facet 35 may then simply apply a concept such as <dollar> or <price>. The IML 50 in classifier 56 which then automatically identifies any other documents that contain words associated with the <price> concept. The classifiers in this example are associated with book information. But, of course, as previously mentioned above, the taxonomy, facets and associated classifiers are adaptable to any subject matter. Taxonomy Builder For this reason, some classifiers may not just identify the content associated with a particular facet, but may also further subdivide the identified content 52 into subparts. This is performed by a taxonomy builder. Alternatively, a custom taxonomy builder can be built and used. The taxonomy builder takes the data from a classifier and uses it to construct a new taxonomy that is attached underneath an existing facet. Each time the content is classified, this taxonomy is reconstructed based on any new information. For example, The procedures in the taxonomy builder 62 then automatically classify any identified content into different sub-categories such as $0-$5, $5-$10, $10-$15, etc. The taxonomy builder 62 then generates a taxonomy 66 under price facet 61A that includes new facets 68A-68D associated with different price ranges. The taxonomy builder 62 can then attach the newly created taxonomy 66 under the general price facet 61A previously created in taxonomy 60. The classifier 61 then classifies the content 52 for each newly built facet 68. In this example, the same or a similar ontology 70 is also used for classifying the content 52 that was identified by the data retrieval system 18. The ontology 70 in this example is associated with the concept �vehicle model�. The vehicle model concept may have multiple different categories, such as sedan, minivan, truck, etc. Under the sedan concept there may be multiple different sub-concepts that include Accord, Civic, and Taurus, etc. Any concept or sub-concept can be referred to in IML 50. For example, the sub-concept Accord may be referred to as <Accord> using the IML 50. As described above, the IML 50 is used in the classifiers to automatically identify other words and phrases associated with the concept <Accord>. The taxonomy builder 74 uses all, or a portion of the ontology 70, to automatically build a taxonomy 76. In this example, the taxonomy builder 74 extracts a section 72 of ontology 70 and inserts it into an associated sedan section 77 of vehicle model taxonomy 76. In this example, each sub-category in section 72 of ontology 70 is converted into an associated facet 78 in taxonomy 76. The taxonomy builder 74 then automatically builds classifiers 79 for each of the automatically created facets 78A-78C in taxonomy 76. For example, an Accord unstructured content classifier 79A may be built by taxonomy builder 74 to classify unstructured data in content 52 associated with the concept <Accord> specified by facet 78A. Classifier 79A may be built by taxonomy builder 74 to use IML 50 to search for the concept <Accord> in the unstructured data in content 52. The taxonomy builder 74 may also automatically create an Accord structured content classifier 79B to classify structured data in content 52 associated with the Accord facet 78A. Other classifiers may similarly be generated for the other automatically created facets 78B and 78C. Intra-Document Classification As described above, the navigation system 20 is not restricted to documents as the only level of search granularity. Content can also be classified in sentences, sections, or any other arbitrary document portion. This is especially useful for taxonomies or facets that are heavily represented in the content. For instance, an insurance company might have many documents that each discuss different types of insurance. With document level classification, the user can restrict the query to, say, auto insurance but still get many other documents discussing other different types of unrelated insurance. For example, auto insurance may still appear inconsequentially somewhere in many documents. With intra-document classification, a sentence or section specification increases the likelihood that all returned results are associated with auto insurance. To explain further, The minivan document facet 91A is used by classifier 92 to identify any document in content 96 that contains the concept <minivan> in a manner similar as discussed above. However, the section facet 91B causes the classifier 92 to only identify document sections that contain the concept <minivan>. A document section could be defined for example as a paragraph, a grouping of text separated from other text by a line space, or any other delineation between groups of text. The sentence facet 91C only identifies sentences in content 96 containing the concept <minivan>. In the cases of sections and sentences, only answers that overlap these sections and sentences are shown to the user if that facet is selected. To explain further, a user enters the text question �4-wheel drive�. The data retrieval system 18 retrieves content 96 that includes document A and document B. One or more intra-document classifiers 92 are associated with the minivan facet 91 and produce different results 98 depending on the intra-document classification used in facet 91. For example, the minivan document classification facet 91A may be analyzed as the IML statement DOC(<minivan>) by classifier 92. Accordingly, classifier 92 identifies all documents in content 96 that include the concept <minivan>. In this example, both document A and document B would be identified in results 98A, since the concept minivan is contained in both 4-wheel drive documents. The minivan sentence classification facet 91C is analyzed as the IML statement SENTENCE(<mimivan>) by classifier 92. Classifier 92 identifies all sentences in content 96 that include the concept <minivan>. In this example, only document B includes both the concept <minivan> and the concept <4-wheel drive> in the same sentence and thus that answer, and not document A, is shown. Similar classification is used for the minivan section classification facet 91B where an IML statement SECTION(<minivan>) is used by classifier 92 to identify any sections within documents that contain the concept <minivan>. Facet Expansion In many query systems, it is difficult to back out of selections. The navigation system 20 ( In addition to the answers 106, the navigation system also displays a taxonomy 102 that is related to the question 104. In this example, the question is related to a particular vehicle model. Accordingly, the navigation system may present a model taxonomy 102 that includes the facets sedan and Outback, since Outback was requested by the question and sedan is its parent. The navigation system provides the substantial advantage of allowing the user to then automatically expand the previous search by selecting one of the displayed facets. For example, the user may then select the facet 108 related to the broader category of sedans. Referring to Parasitic Classification The navigation system can take advantage of classifications in one data source to help classify content in other data sources. For example, an enterprise may have overlapping structured and unstructured content. For instance, a book database may contain a structured price list and may also include reviews for some of the books identified in the price list. The navigation system 20 can not only classify both the structured and unstructured content from different databases but can use the structured book list database to further classify the unstructured content. The navigation system may use a first structured classifier to identify books in the structured database within specific price ranges. The structured classifier does this by identifying book prices in a preconfigured price field. The classifier can then use other classifications in the structured database, such as the book title field, to identify books referred to in the non-structured content that are within the same price range. For example, the classifier can identify the titles in the structured database for all books within a particular price range. The identified book titles are then used by an unstructured classifier to identify similar book titles in the unstructured content. These book titles are then classified as being within the same price range as the books identified in the structured content, even though the unstructured content may not identify or include a book price. To explain further, Also shown in A structured classifier 126 generates a taxonomy 128 corresponding with the categories defined in structured content 124. For example, the taxonomy 128 may contain facets 129 that correspond to the different product release categories contained in structured content 124. The structured classifier 126 may then identify the data items in structured content 124 that are associated with each release facet 129A-129D. For example, the classifier 126 may identify a document 125 contained in category �release 2.0� of content 124. The document 125 may describe a product referring to �turbo java�. Accordingly, the structured classifier 126 classifies the document 125 under the release 2.0 facet 129B. Other classifiers, such as unstructured classifier 130, may then be associated with the facets 129 generated by structured classifier 126. The unstructured classifier 130 is created by the structured classifier 126 to search the unstructured content 120 for any information associated with the facets 129 generated from the structured content 124. For example, classifier 130 may search unstructured content 120 for any data items associated with the release 2.0 facet 129B. The unstructured classifier 130 may also use the IML 50 to identify any unstructured content 120 associated with concepts identified by the facets 129. The classifier 130 can also be used to classify other content related to the facets 129. For example, a document in unstructured content 120 referring to �turbo java� may be classified under release 2.0 facet 129B due to the previous classification of the turbo java document 125 under the release 2.0 facet 129B by structured classifier 126. It should also be understood that the taxonomy classification could also be generated from unstructured content and then used to classify content in other unstructured or structured content. Configurable Classifiers Different custom classifier interfaces 144 and 146 convert the formats used in different content 148 and 150, respectively, into a common format that can then be used by the same classifier 142. The classifier 142 can then classify both the content 148 and content 150 with associated facets in taxonomy 140. This prevents having to write different classifiers 142 for each different content format. Configurable classifiers are not restricted to structured data and may also be applied to unstructured data of any form. Ontology/Result Based Classification Referring to A user may enter the question CHECKING ACCOUNTS via the UI 30 ( Simply using a taxonomy generator to identify common terms in the data items 163 and then creating an associated taxonomy may not yield the most effective content classification. For example, the taxonomy generator could create two separate facets, one for opening checking accounts and another for creating new checking accounts. This could be confusing when viewed by the user. Alternatively, the classifier 164 may not create a facet associated with new checking accounts because many of the data items use slightly different terminology when referring to the same general subject matter. Accordingly, the classifier 164 would not associate the content in any of the data items together and determine that there are not enough data items associated with any one of these terms to create a facet category. To resolve this issue, the taxonomy builder 164 also refers to the ontology 160 when creating taxonomy 166. The taxonomy builder 164 analyzes the text of the results 162 as well as the concepts and other linguistic aspects in ontology 160. The concepts in ontology 160 may have different levels. For example, there may be customer concepts, industry concepts and language concepts. Certain concepts, such as customer concepts may be more important than industry concepts or language concepts. The taxonomy builder 164 compares the ontology 160 with the results 162 and builds a taxonomy 166 according to the comparison. For example, the taxonomy builder 164 may use known clustering algorithms that assign scores to the different concepts and words in ontology 160 corresponding with results 162. The taxonomy 166 is then generated based on this scoring. It should be understood that taxonomy 166 is dynamically generated at run time based on the search results from a query. Other static taxonomies may also be used in the navigation system and created offline prior to operation. To explain further, both the opening of a checking account referred to in data item 163A and creating a new checking account referred to in data item 163F may each be associated with a CHECKING ACCOUNT category 161A in ontology 160. If enough similar data items 163 are associated with one of the same ontology category 161A, the classifier 164 may create a corresponding new account facet 168A in taxonomy 166. Similarly, the taxonomy builder 164 may also generate a closing account facet 168B when enough data items 163 are associated with closing or shutting down a checking account and generate an interest rate facet 168C when enough data items 163 are associated with checking account interest rates. Note that content 162 has little or no data items associated with branch location category 161B in ontology 160. Accordingly, the taxonomy builder 164 does not create a corresponding facet in taxonomy 166. Thus, the ontology 160 and results 162 actually play off each other to create a better taxonomy classification. Rules Based Facet Restriction Rules can determine which facets are selected for classifying content. Users are not required to select facets in a taxonomy to allow particular facets to take effect. Rules allow the taxonomy to operate simply by the user asking a question. Rules can also be used to automatically select facets in the taxonomy according to different questions, even if the question does not exactly match an existing facet. The rules include conditions that can refer to particular concepts in a question or particular types of users asking the question. Actions contained in the rules can then initiate a search for a particular concept or show a particular answer. The actions contained in the rules can also be used to manipulate facet restrictions. A rule 176 includes a condition 176A and an associated action 176B. In this example, the question 180 triggers the condition 176A in rule 176. For example, the condition 176A is premised on the concept <Outback> existing in the question 180. Note that a concept does not require the exact word �Outback� be used in the question 180 and only requires a word or phrase that is associated with the concept <Outback> as identified by an IML 50. If the concept <Outback> exists in the question 180, the action 176B is triggered. In this example, the action 176B automatically restricts the search of content 182 to data items classified in a model.sedan.outback facet 171. Accordingly, only the answers 172 associated with the facet 171 are displayed. The navigation system will then find the price facets 190 that cover the price range identified in question 186. For example, price facets 190A and 190B cover the entire specified price range of $12K-$20K. Accordingly, the price facets 190A and 190B are automatically selected in response to question 186 and the answers 188 classified from content 187 will only include vehicles having a price range of between $10K-$20K. Accordingly, a user may enter a question 210 and associated answers are displayed on the user interface 209. The user may then make a selection 222 for facet 220. The navigation system displays content 224B classified with Accord facet 220 and also displays the content in action 212B. In this case, the content in action 212B is an advertisement associated with facet 220. Alternate Display Formats For example, the DRAGON question 252 is processed by the data retrieval system 18 described above. Some of the content from the data search is displayed as answers 254A and 254B. The answer 254A includes some of the same content that may be contained in the answers described above. However, the answer now also includes facets 256 and that have been classified with the content. For example, a classifier may have classified the content 256A and 256B with a $0-$5 price range facet 256C. Accordingly, the facet 256C is displayed along with content 256A and 256B. Similarly, the same, or another, classifier may have also classified the content 256A and 256B with a 1980-1989 publication date facet 256D. Accordingly, the facet 256D is displayed along with the content 256A and 256B and facet 256C. Selecting the facet 256C or 256D in answer 254A operates in the same manner as the facets described above. Thus, selecting the year 1980-89 facet 256D would display answers for books published between the years 1980-89. Other answers, such as answer 254B, may display other facets that are associated with the corresponding content. One of the advantages of this format is that the user is immediately notified that the book identified in answer 254A is within a price range of $0-$5 dollars and published between the years of 1980-1989. This is displayed in answer 254A without ever clicking on a price range facet or clicking on a particular publication year facet. In another variation, different taxonomy categories 260 may be displayed on the user interface 250. The user can then select which taxonomies and search content is displayed as answers 254. An analog facet display 262 can also be used to select different sets of facets that correspond with the selected position on display 262. Detailed Rule Description The rules above are described below in more detail. Many of the descriptions below refers to the following taxonomies. Taxonomy: Genre
A facet can be automatically selected in a rule using an �Add Facet Restriction� item in an �Advanced� menu. This will create an action tab with two mechanisms for selecting facets. First, there is a text field in which the full name of the facet can be typed in quotes (e.g., �Genre.Fiction�). The appropriate facet within the tree shown below the text field can also be selected. Note that this tree may not be complete until the data is classified. Multiple facets can be selected using rules. As with selecting one facet, multiple facets can be selected within a �Add Facet Restriction� action in either the text field or the tree. Within the text box, an IML expression can be written where the primary difference is that quotes text is interpreted as facet references rather than tokens. Two facets, a and b, can be selected with the expression: �a� OR �b�. If only results that were classified by both facets is desired, the following expression can be used: �a� OVERLAP �b�. Note that �a� IS �b� will also work but only for exact matches. For example, all documents will be correctly returned that were classified as a and b. However, no match will occur for a document classified as a with a section classified as b. The OVERLAP operator will cause the section to be matched. Selecting facets in the tree will produce an expression which is the intersection of the taxonomies and a union of the facets within the taxonomies. For instance, selecting Mystery, Fantasy, and $20-$29.99 in the genre taxonomy, will produce the expression: (�Genre.Fiction.Mystery� OR �Genre.Fiction.Speculative.Fantasy�) OVERLAP �Price.$20-$29.99�. Facets may be selected in different rules. The navigation system 20 attempts to combine them in a reasonable way in the same order that the rules were analyzed. As long as the facet expressions are in the form of an intersection of unions and the unions are isolated to one taxonomy each, then facet expressions are combined to maintain that form. If a different type of expression is found, from that point on, expressions are combined using OVERLAP. For example, consider the case where three rules fire with the following expression. �a.b� OVERLAP (�x.y� OR �x.z�) �a.c� OVERLAP �1.2� �a.b� OR �x.z� The first two expressions conform to the standard expression style, so they combine into: (�a.b� OR �a.c�) OVERLAP (�x.y� OR �x.z�) OVERLAP �1.2�. However, the third expression does not conform (it contains a union from two different taxonomies) and thus is merely appended with the OVERLAP operator: (�a.b� OR �a.c�) OVERLAP (�x.y� OR �x.z�) OVERLAP �1.2� OVERLAP (�a.b� OR �x.z�). Note that facets selected in a search rule only affect that search. Facets selected in other rules are combined and affect all subsequent searches. A facet can be restricted for a particular search. Adding such a restriction within a search rule may apply just to that search and may, by default, be combined with the previous restrictions. An option can also be selected to not combine with previous restrictions. This is particularly useful for doing collection based facets which are not to be applied to structured searches. A previous facet restriction can be over-ridden. A search rule may include the Facet Restriction action and also a checkbox which, when checked, causes the current restriction to replace previous restrictions. Facet references can contain configurable wildcards if the full path of the facet is unknown. For example, it may not be exactly known where Fantasy is located in the genre taxonomy. Fantasy can be referred to using Genre.*.Fantasy. This returns the first facet in the genre taxonomy that has the name Fantasy using a standard breadth first search. In other words, the first, shallowest possible match, is identified. If there is no match, the facet will be removed from the expression. The id for a facet may not be known. This is sometimes the case with numeric or range based facets where the facet reference cannot exactly be expressed. Consider the taxonomy of prices. A rule can match prices and create a facet restriction such as Price.*.{price}. Unfortunately, individual prices may not exist in the taxonomy preventing a match. To find the most specific facet whose range contains the price, the following restriction can be used: Price.< >*.{price}. Note that this extension requires min/max values and a comparator to be specified for the facet. Some classifiers may do this automatically. An approximate match may be generated for a facet. Sometimes a user may ask something like �Show me books around $15�. If the facet reference Price.< >*. {price} is used, the facet $15-$19.99 may be returned back which is probably not the desired result. A similar case may exist when a user asks �What books were published in 1998?�. The preferred response may not be 1998, but 1990-1999. This can be accomplished with the �* wildcard. For these two examples, that would be Price.�*.$15 and Year.�*.1998. To be precise, this expression looks for a containing facet first. If the requested value is completely contained in the facet, then that facet is matched (e.g., both Price.< >*.$12 and Price.�*.$12 will match $10-$14.99). Otherwise, the parent is matched. So, Price.�*.$15 will match $10-$19.99. Note that this extension has min/max values and a comparator to be specified for the facet. Some classifiers may do this automatically. A range of facets can be selected. For example, a user may ask �What books cost between $0 and $22�. This range can be requested in a facet restriction expression by using an * IML operator which is overloaded for this purpose. The following expression would be used: �Price.< >*.$0�*�Price.< >*.$22�. This will cause the facets $0-$9.99, $10-$19.99, and $20-$14.99 to be selected. Note that this extension has min/max values and a comparator to be specified for the facet. Some classifiers may also do this automatically. The user may also request a value that doesn't exist. In many cases, the best thing to do is nothing, and the facet restriction will be ignored. But sometimes, especially when the user has asked for a range, some action is required. For example, the user may have asked for books starting at −10 dollars. Alternatively, the user may be asking for books published starting at 1945 when there is only data starting at 1960. The following wildcards can be used. The �<*� wildcard is equivalent to < >* but if no answer is returned, finds the closest match that is bigger than the requested value. For example, Price.<*.{price} matches $0-$4.99 if the {price} evaluates to $−10, $−3.19, $0, $2.37, etc. The �>*� operator is equivalent to < >* but if no answer is returned, finds the closest match that is smaller than the requested value. The user can be restricted from seeing particular facets. A facet restriction can be set using ISNT. For example, the user should not see any non-fiction books, the following expression can be used: �Genre� ISNT �Genre.Non Fiction� A user can be restricted to see only particular facets. For example, to restrict a user to only fiction books, rather than using �Genre.Fiction�, the following operator can be used �RESTRICTED(Genre.Fiction)�. A facet may be selected in the rules and the user may then navigate to a more general facet, the navigation system may still search for the original facet. To solve this problem, the navigation system can �fix� the question by replacing the old facet referring text with the new. The user still sees the old question, but as far as the system is concerned, the question has been changed. A variable may be set on the facet restriction that corresponds to the part of the question that needs to be replaced. For example, there may be a rule with the following language condition (�science fiction�)=scifi. The facet restriction would then be �Genre.*.Science Fiction�=scifi. Now the navigation system can tell what part of the question was about science fiction and replace it with the appropriate information. The system described above can use dedicated processor systems, micro controllers, programmable logic devices, or microprocessors that perform some or all of the operations. Some of the operations described above may be implemented in software and other operations may be implemented in hardware. For the sake of convenience, the operations are described as various interconnected functional blocks or distinct software modules. This is not necessary, however, and there may be cases where these functional blocks or modules are equivalently aggregated into a single logic device, program or operation with unclear boundaries. In any event, the functional blocks and software modules or features of the flexible interface can be implemented by themselves, or in combination with other operations in either hardware or software. Having described and illustrated the principles of the invention in a preferred embodiment thereof, it should be apparent that the invention may be modified in arrangement and detail without departing from such principles. I claim all modifications and variation coming within the spirit and scope of the following claims. Patent Citations
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Crooked Alley KidSpace
Crooked Alley KidSpace
630 Wirt Road, Groveport 43125
(614) 836-3333
Crooked Alley KidSpace, the City of Groveport's place just for kids, offers a kid-friendly site for daily arts and education programming, as well as a safe site for your child's next birthday party, Scout meeting or social gathering. KidSpace is not a staffed facilty; all inquiries may be made through Groveport Town Hall, 648 Main Street (614-836-3333)
Children's Arts & Education Classes
Classes are reasonably priced for the family budget with many programs offered free of charge. Most classes are designed for children ages 3 - 12, with an enclosed playground offering a kid-sized treehouse, slide and climbing equipment. Consider learning how to play the guitar, take a junior theater class or create a work of art in a kids' art class. All classes are through registration by calling Groveport Town Hall at 836-3333. Advance payment is required. Children age 9 and under must be accompanied by a parent.
Classes at KidSpace and Town Hall
For the current calendar of KidSpace classes, as well as the current calendar of Town Hall programs, please click here.
Private Reservations
KidSpace is the ideal site for your child's next birthday party, Scout meeting or gathering. Under special arrangements, you can even arrange for a slumber party on a Saturday night for a small group of children with ample adult supervision.
You'll have the use of the entire facility for your event. Explore the Library with shelves of books for every age. FunSpace, our large multipurpose space, is ideal for birthday parties or special events, offering space for 84 people with tables and chairs, or 180 with theatre seating. We'll even provide the tables and chairs. The Community Affairs staff will help you to create the perfect event.
Begining January 1, 2015 KidSpace will no longer be free to use by Groveport residents and businesses. A $25.00 non-refundable fee will be charged for Groveport residents and businesses. Rates for non-residents is $50 per hour remain the same.
Crooked Alley Kidspace Reservation Contract

subset: commoncrawl


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AM 5 Zyprexa, 200 Lamictal, 60 Cymbalta, .5 Ativan
afternoon 5 Zyprexa
PM 5 Zyprexa, 200 Lamictal
It's Election Day! Fancy that.
A dear friend recently referred to Obama as, "the bee's knees," and I have to agree.
One of the reasons I agree is because he did something incredibly wonderful for those of us with mental illnesses when he passed the Mental Health Parity Law. I talk about it here. Thanks to Obama, we finally have the mental health treatment rights we have always deserved.
I'm certainly not going to tell you who to vote for.
But whoever you support, get out there and practice your right to vote!
Until next time...


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The installation ceremony for Sterling College's 12th president, Scott A. Rich, will be held in Culbertson Auditorium in Spencer Hall on Friday, Oct. 18 at 9:30 a.m. Rich will deliver his inaugural speech, "A Call to Extraordinary Service," along with music sung by the Highland Singers and a charge led by Chair of the Board of Trustees Heather McCreery '90.
The Sterling College Board of Trustees announces that Mr. Scott Rich has accepted the presidency of Sterling College. Rich will become Sterling's twelfth president effective May 1, 2013.
"The Board and I are very excited that Scott has agreed to serve the college in this capacity. His strength of character, vast understanding of finance and operations, and a strong desire for excellence make him the ideal president for Sterling College, "Chairman of the Board Heather McCreery said.


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Are you planning a vintage style wedding? This Sunday 30th January is the Vintage Chic Wedding Fair in Birmingham.
This is the second Vintage Chic Wedding Fair organised by Simply Chic and it promises to be even more spectacular than the first. There will be an abundance of suppliers who all specialise in vintage weddings; from wedding cakes to stationery, photographers to entertainment, you will be sure to find fabulous ideas for your wedding here.
The fair is open between 11.30am – 4.30pm and is taking place at The Old Library in the Custard Factory, Birmingham, B9 4AA
Entry is £2.00 or free to pre-registered brides. To register your attendance please email email@example.com
Image courtesy of Theresa Furey Photography.


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silentbob wrote:Cheers. If all else fails I s'pose I could always get a pair of cheap replacement glasses just for OR. On a related note I'm seriously considering passing up on the next generation of consoles and putting the money towards OR and upgrading my PC instead. Just seems like OR has the potential to deliver a completely new experience, whereas Durango and PS4 will just be... shinier. |
#9343418, By phAge The Oculus Rift; VR Finally comes of age?
phAge 24,501 posts
Seen 29 minutes ago
Registered 12 years ago


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Interview Steven van Broeckhoven
Simone Pierini from Iris Press sent us this interview with the current PWA Freestyle World Champion Steven van Broeckhoven (B-72), called SIMPLY A CHAMPION: "RIDE WITH A SMILE". In June and July Steven spent time in Italy, where Simone did this interview you can read below. At the moment Steven is already on Fuerteventura, to preapare for the upcoming PWA freestyle event.
Interview by Simone Pierini
"Ride with a smile". This is Steven Van Broeckhoven's message. The 2011 PWA Freestyle World Champion, born in Belgium and is one of few Europeans, who was able to win a PWA title in his category. French Antoine Albeau won the freestyle title in 2001. A champion who is polite, smart, and almost quiet.
Passion for windsurfing, mental balance, clear ideas, a great attention to the study and development of materials, a mad attraction towards every technical aspect of freestyle moves: all this represents the strength of Steven. Like everybody else, as he likes to describe himself, he experiences his sport with happiness and respect. He received a gift winning the World title. He has not thrown it to the wind, nor did he let it blow away as so many have done. He has developed it, looking for every detail that could make him a better rider. With humility and patience.
At the age of 27 he is in his full physical, technical and especially mental maturity. The interview he gave us only confirms the sensations we experienced at first glance. What emerges is his openness to the world of windsurfing. In this case the Italian one that first welcomed him in Sardinia, Sa Barra (Sant'Antioco) the Olympus of freestyle discovered by his friend Luigi Madeddu, and then in Tuscany, Talamone (Grosseto), where he visited as special guest the XRay Talent Factory, the stage of Raimondo Gasperini. All this before moving on to the Lake Garda for the Young Gun Camp promoted by his new sponsors NeilPryde/JP Australia.
Simone Pierini/Iris Press: Here in Italy you are seen as a "Martian". Who is Steven Van Broeckhoven instead?
Steven: A guy as many. I like windsurfing because it's freedom to me. It gives you the opportunity to meet a lot of people, to travel and discover beautiful places around the world. I don't like to standardize myself, I am curious and rather simple.
Simone Pierini/Iris Press: How does it change the life of an athlete crowned world champion?
Steven: Nothing changes in my head or in my way of thinking. But now windsurfing became a job, certainly a nice job but with less fun than before. I have to train more and improve myself constantly, and I need to have a detailed physical preparation. That's why I see and I experience windsurfing in two different situations: those with friends, in which there is complete mental freedom, and that of training, where concentration is at its maximum.
Simone Pierini/Iris Press: As a child did you imagine to be able to reach these levels?
Steven: As a child, like everyone else, I read magazines and I followed the world of competitions. I was good and almost immediately I realized that I had a great ease in learning, I used to learn everything very quickly. Anyway, I never thought of becoming world champion.
Simone Pierini/Iris Press: Where and how did you start windsurfing?
Steven: I started when I was 9 years old in a small lake near to where I was born, Lommel, two hours from the Belgian coast. There was always a little wind there, so when I was 12 my friends took advantage of the weekend to take me to Holland on Lake Grevelingen where I started to plan and to gradually improve.
Simone Pierini/Iris Press: Who is your idol?
Steven: I do not have an idol, someone who I could take as an example or who inspired me. I always watched and admired all those champions who have shaped the history of windsurfing, but I chose to follow my path, my own style. However, if I should name someone that would be Tonky Frans, as he represents what I see in windsurfing, that is: happiness, joy, and fun.
Simone Pierini/Iris Press: How do you imagine your future?
Steven: Windsurfing. That´s what I love and what I live for. Even though i prefer not to look to far in time. I certainly have an idea, but this is and will remain my secret.
Simone Pierini/Iris Press: Who is your toughest contender?
Steven: At the moment all top-4 of the PWA. There is no one in particular, the level is so high that you can win or loose with anyone. What I can say is that I have many friends in the circuit of competitions.
Simone Pierini/Iris Press: The first event of the Season, Podersdorf, was not so lucky. With what kind of motivation will you arrive in Fuerteventura?
Steven: I came from the title won in 2011, I trained a lot for the Austrian competition. But when my heat started there was no wind. But that was it. Fuerteventura is another story, I am here to win. (Steven spent several weeks on Fuerteventura already and trained a lot in highwind conditions)
Simone Pierini/Iris Press: You chose Sardinia for your holiday, but especially for your training. What took you to Sa Barra (Sant'Antioco)?
Steven: I was in Corsica, I knew I had some time before getting to Lake Garda for the Young Gun Camp & Bump and Jump 2012. At that point I decided to take a trip to Sardinia. Advised by my Italian friends Francesco Tedeschi and Luigi Madeddu I decided to go to Sa Barra, and I discovered one of the best places ever for freestyle.
Simone Pierini/Iris Press: What kind of spot is Sa Barra?
Steven: A true freestyle spot, where you may find both flat water and a chop for the most extreme moves. The northwesterly wind is super constant. The water is warm. There is a great atmosphere, the locals have welcomed me very well making me feel immediately at ease. Then Gigi is a really good rider. It has been nice to surf with him. I'll go back there for sure.
Simone Pierini/Iris Press: How did you meet Gigi Madeddu who hosted you in Sa Barra?
Steven: I met Gigi in 2009 at the EFPT tourstop in Sardinia. Then I spent much time with him on holiday in Bonaire and it was nice to find him here. We are good friends now.
Simone Pierini/Iris Press: Your presence in Talamone has thrilled the boys from the XRay Talent Factory. What do you think about these events?
Steven: In 2009 I was on holiday in Sardinia and Raimondo invited me to greet the kids of the XRay Talent Factory. This year my return to Italy coincided again with his event so I am happy to be back and to have made a contribution. These events are very important, they benefit the future of our sport, the development of the young and the visibility of the sponsors, that as all sectors they are not experiencing a great economic time.
Simone Pierini/Iris Press: Do you follow any young talent in Belgium that may follow your footsteps in the future?
Steven: There are several very good young riders, definitively a growing movement. Unfortunately, I am almost never at home so I can´t follow them personally, but they will have a great future.
Simone Pierini/Iris Press: What advice would you give to young people who approach freestyle?
Steven: They must go on their own way, with fun and passion. Without any conditioning, no obligation, since windsurfing is freedom and it must remain so. With this spirit one may improve. My advice is to do windsurfing with a smile.
Simone Pierini/Iris Press: The economic crisis is palpable, companies are experiencing difficulties and the PWA races have been reduced. What´s your opinion about it?
Steven: I think it is a moment. Last year we competed at seven PWA events, while this year the organizers are having difficulties in finding sponsors and we are likely to compete three or four tourstops at most. I am sure that from next year on there will be a big step forward. New realities and new locations are being proposed. One of these is Brouwersdam, in Holland, which offers perfect conditions for freestyle.
Simone Pierini/Iris Press: Much has been said about your transition from Gaastra/F2 to NeilPryde/Jp, how did it develop?
Steven: With Gaastra/F2 I reached the top, I won the Pwa and concluded a great adventure. Frankly, it was hard to do better. NeilPryde/Jp have created a new project, they focused on me to try to bring back the freestyle title after the last of the three won by Ricardo Campello in 2005. Certainly, this has changed my life too, there are more promotion, more videos, more photos: it became a 360° job which give me great incentives. A fantastic work on new materials began with Robet Stroj designer for the sails and Werner Gringler as board shaper. They pay much attention to details of the technical aspect, the love to listen to their athletes in order to create a top-product. I hope I can bring them and the whole team great satisfaction.
Simone Pierini/Iris Press: To conclude, the simplest question. Is your life beautiful?
Steven: For sure! (he answers without hesitation). No doubt I am realizing the dream of many riders, going around the world and finding always a warm welcome, children asking for autographs and photos. They look at you as you are different from them. But actually for me life is beautiful in its simplicity. Going around the world is beautiful, but if I have to say what is happiness for me I answer that is windsurfing with my friends at home, joking with them. Windsurfing in Maui or in any other spot on the planet is not comparable to the joy of having at your side who loves you".
© Interview by: Simone Pierini/Iris Press Photos: Daniele Stanisci/Iris Press, Francesca Salaris/Windsurfing Club Sa Barra, Michele Tagliafico/Windsurfing Club Sa Barra.


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Random Thought Part Deux
Saturday, January 19, 2008
8 comments
nope, i`ll take the pizza
9:42 AMI'll take both.
I had pizza the other night at our fav local joint. Marco, the pizza guy, was back from Italy. Thank you, God. Love myself some Marco.
Had mater soup and grilled cheesey (that's what Buddha calls 'em) on Thursday during my glorious snow day. VERY GOOD TIMES!
i love grilled cheese sandwiches...my daughter mojo makes them better than anyone ever...you'll have to ask babs about how good the pizza's are in west, texas...it sure smelled good..
7:34 PMi like to have tomato soup, take slices of white bread and tear it into peices and then squish them into little balls and then drop them in the soup...I could eat a 1/2 loaf of bread with a can of soup...well, till i became diabetic..that killed that good time..ha
7:26 PMVery profound, Ms. Shark. If you put some pepperoni on that grilled cheese...well, there you have it.
I like 2 slices of American on a good hearty bakery wheat bread. The kind with all the grainy goodness right in there for you to see. And then the tomato soup.
Somehow this classic never appeared in my house. My mother never served grilled cheese with any soup. I am not sure why. The first time I saw this meal in action was college. My roommate. She was the grilled-cheese-and-tomato-soup queen.
My mother also never made casseroles of any kind. She hated them. I am making up for lost tasty comfort food by trying out as many casseroles as possible!


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Tags house, electro house, dance, club, electronic
Genres house, electronic, dance, progressive house, trance (beta) (flag as incorrect)
Artists TV Rock (beta)
Published May 27, 2011
Eleven tracks including music by Avicii, Steve Angello, and Axwell.
◊◊◊ fuck diamond ◊◊◊ by sydneyjs
what i always go back to.
shoegaze, electronic, chill, smoking, hookah
¡n†r∆n§¡† by sydneyjs
if you're chillin with the best these'll put you at rest.
chillwave, study, smoking, relax, electronic
study drift by sydneyjs
study, relax, smoke, enjoy.
chill, electronic, ambient, chillwave
concentrate by sydneyjs
chill-out/study mix .
chillout, electro, study, bonobo


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3 bed, 1.5 full bath. Original Craftsman Bungalow character retained with this down-to-the-studs remodel! Amazing updates More info |
5 bed, 2.5 full bath. Large home in super location! Close to Micron, the river, quick easy access to downtown and Luck Pe More info |
3 bed, 2 full bath. 3 BD 2 BA single level with separate office near entry. Open living area flows from spacious living More info |
4 bed, 3.5 full bath. PC9423 Open living room flows into dining room w/ great kitchen access. Living & dining also have g More info |
4 bed, 2.5 full bath. Clearwater by Eaglewood Homes. As you walk into this exquisite home you will see the workmanship of More info |
3 bed, 2 full bath. Beautiful and new! This home is 1356 square feet of remodeled excellence! Offering tons of space fo More info |
4 bed, 3 full bath. Probably the best lot in Wilderness Ranch, less that a half mile off HWY 21. Huge remodeled log hom More info |
3 bed, 2.5 full bath. Impeccably maintained residence nestled adjacent to open common area in the Hidden Springs Communit More info |
3 bed, 2.5 full bath. Excellent opportunity to own three blocks from BSU. Well designed 3BD/2. 5BA. Vinyl siding and full More info |
2 bed, 1 full bath. Great renovated Boise Bench home. New furnace, new air conditioner, new paint, new trifold doors in More info |


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ResearchWe improve the care, quality of life, and survival rate of children with malignant diseases. |
Dr. Mitchell Cairo
The Pediatric Cancer Research Foundation Laboratory at Maria Fareri Children's Hospital at New York Medical College is focused on six overarching areas of research. These state of the art research initiatives include:
- Cancer genetics and treatment of childhood and adolescent hematological malignancies;
- Genetically re-engineering immune cells to enhance targeted adoptive cellular cancer immunotherapy;
- Tumor immunology and identifying targets for humoral targeted immunotherapy;
- Stem cell biology, cancer stem cells, and regenerative therapy;
- Developing targeted therapy for childhood, adolescent andyoung adult solid tumors and brain tumors.
- Translational therapeutic consortiums for childhood and adolescent cancer therapy.
Our translational consortium research programs will be testing targeted cellular therapy against Hodgkin Lymphoma in patients who have failed first or second line therapy.
In addition, we have developed several national translational research consortiums concentrating on Stem Cell Transplantation for high risk patients with Acute Leukemia, Lymphoma cell therapy, Pediatric Sarcoma cell therapy, Familial Stem Cell Transplantation for high risk Sickle Cell Disease, and non-Hodgkin Lymphoma translational research and treatment.
We are indebted to PCRF's Board of Directors, Advisory Board Members, officers and volunteers for their generous support of the aforementioned pediatric cancer research programs. The year 2014 marks the 32nd year of sustained and continuous support in our research mission on children and adolescents with cancer. We can't thank PCRF enough for the golden opportunity to participate in the enhancement of the pediatric cancer survival rate from 20% to 90% in the last 30 years.
Grantees |
Michael Arnold, M.D. |
Mitchell Cairo, M.D. |
Laurence Cooper, M.D. |
Brian Crompton, M.D. |
Margarita Gutova, M.D. |
Alex Huang, M.D., Ph.D |
Theodore Moore, M.D. |
Kathleen Sakamoto, M.D., Ph.D |
Robert Seeger, M.D. |
Jason Yustein, M.D., Ph.D. |
International Symposium |
all members of Castle Connolly
Medical, LTD. America's trusted
source for identifying top doctors.


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This site uses cookies and similar technologies to customize and enhance your online experience. By using this site, you accept our use of these technologies. To learn more, please read our cookie policy.
Luc Mbah a Moute Read to Achieve
The Bucks joined forces with The UPS Stores, the Marine's Toys for Tots program, Scholastic and Sam's Hope to provided books for kids at Longfellow Elementary School in Milwaukee


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All Linda Penwarden jewellery is shipped in a gift box. Careful storage between wears will ensure protection from damage and environment.
Like their natural hosts, pearls are prone to damage from pollution and rough treatment. Store pearls separately from other jewellery as these soft stones can be easily scratched by harder gemstones or metals. Excessive sun and heat can also damage pearls, dehydrating them. Perfumes, oils, sunscreen etc should be avoided. Wipe pearls gently with a soft cloth after wear to remove any trace residue. Pearls are normally strung on silk and moisture, oils and frequent touching can degrade the integrity of this silk. As a result, regular re-stringing is recommended. Never expose pearls to chlorinated water.
Silver will naturally tarnish when exposed to the air, so store it in an airtight container or tarnish-protecting cloth to help to minimize the amount of care necessary. Keep your sterling silver jewellery in a cool, dry place. Polishing cloths are an excellent choice for cleaning your sterling silver jewellery.
Gold will develop a film build up of oils from lotions, powders, soaps and natural skin oils which takes away from it's natural patina. Chemicals in the air can also oxidize the gold depending on the karat grade used. Always remove gold jewellery before entering swimming pools or using household cleaners, as chlorinated water and harsh chemicals can erode gold jewellery, create abrasions or reduce lustre. Keep your precious pieces in a fabric-lined jewel case, or a box with compartments or dividers.
The choice of stone setting is a security as well as an aesthetic choice. The more a stone is exposed, the more vulnerable it is to damage and loosening. At home ultrasonic cleaners can also loosen gem settings. Repeated rough treatment and wear can loosen all stone settings and you could lose your precious gems. Fine jewellery should be checked at least once a year for wear and the security of settings.
© 2015 Penwarden Fine Jewellery
631 Mount Pleasant Rd, Toronto, Ontario M4S 2M9


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Wicked Halloween Snack: Witch's Brew
This is a Halloween snack only a kid could love: salty pretzels, candy corn, a PB&J sandwich, fruit leather and peas….that's right, peas. Maybe we'd all be wise to follow the lead of my 5-year-old and not get so caught up with "correct" food combos. Getting your kids to eat their veggies couldn't be more fun!
What you'll need:
- 1 PB&J sandwich
- 1 fruit leather
- 8 pretzel sticks
- 10 pieces of candy corn
- 1/4 cup sweet peas
To make Witch's Brew:
Using a circle cookie cutter (or an empty soup can), cut a circle out of the PB&J sandwich. Wrap the fruit leather around the sandwich, leaving a lip at the top so the peas won't roll off (use peanut butter as "glue" if needed). Break 1 pretzel stick in half and press it into the top of the sandwich for the stirring spoon. Stack 7 pretzel sticks onto the plate as shown for the logs. Place the wrapped sandwich on top of the sticks. Arrange the candy corn around the logs for the fire. Gently put peas into place on top of sandwich. Easy-peasy!
You can also try these other fun Halloween snacks:
Halloween | Tags:
Creepy Spooky Scary Halloween Fun!, Halloween, Halloween Snacks, Peas, Quick and Easy Snacks
1 Comment | Post Your Comment
One Response to "Wicked Halloween Snack: Witch's Brew"
I'm the Editorial Assistant for Fun Family Crafts and I wanted to let you know that we have featured this awesome Halloween snack! You can see it here:
Thanks for a wonderful project idea!


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The Right Path
Here's a little bit of inspiration to make your day fabulous, fun and full of joy.
"Do not follow where the path may lead. Go instead where there is no path and leave a trail." —Ralph Waldo Emerson
Maybe you find yourself following in the same career path as your mother or living in the same city as your friends from college. If you're happy, great! If not, maybe it's time to discover your own path in life. —Crystal Tate
What trail do you want to leave behind? Tell us in the comments.


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Kidding Around
It's time to answer some more questions in regards to the Mavs. This week, we'll be covering different angles of the offseason for the Mavs, via the questions and answers format. Again, questions can be sent via the comments section and on Twitter.
We start this batch of questions with a specific theme and that will be Jason Kidd. This week, the point guard of the championship roster of 2011 decided to call it a career after 19 seasons of NBA duty.
Dirk Nowitzki took to Twitter @swish41 to react to the news of Kidd's retirement.
Sad day. My boy jkidd is retiring. One of the best point guards ever and one of the fiercest competitors I have ever played with.
— Dirk Nowitzki (@swish41) June 3, 2013
Amazing career. He always put the team and winning first. All the best to him in retirement. #hof
— Dirk Nowitzki (@swish41) June 3, 2013
Kidd certainly means a lot of Nowitzki as the acquisition of the veteran point guard back in 2008 was instrumental in building the championship squad that won the title in improbable fashion over the Miami Heat.
With his career now over and the clock starting with his announcement as a Hall of Famer likely to be announced in five years, let's look at five questions and answers in regards to J-Kidd.
1. Was it time for Kidd to hang them up and retire?
The way the postseason played out, it certainly appears that's the case. With two more years left on his deal, Kidd might have felt like it was the necessary move to call it career. It's a game, but there's a lot of mental and physical preparation that are needed in order to survive an 82-game season. He's smart enough to realize that it's time to let it go. He's still one of the smartest players in the game, but if his body can't respond like it used to, the brainpower isn't enough.
2. Should you hold any ill will against Kidd for the way he departed Dallas to go to New York?
I can understand if you do, especially with how it played out, but I certainly don't hold any negative feelings for him. Like I mentioned, Kidd is one of the smartest players in the game. He saw what was going down in Dallas leading up to this past season. It certainly didn't appear that Dallas was primed to be in the position that New York was going to be in.
Could the situation have been played out in a better fashion? Absolutely, but it's hard to imagine many things that are executed in the perfect manner. It's not like people are going to remember Kidd's time in Dallas as his agreeing to stay with the Mavs only to join the Knicks as the defining moment of his career. People are going to remember him being one of the catalysts for the team's championship in 2011.
3. Does his playoff run this year tarnish his legacy as a Hall of Famer?
This goes along the line with the previous question. I have a hard time believing people will be prisoners of the moment and suggest this run in New York killed Kidd's career. That's just half a year of bad basketball that comes nowhere near tarnishing a 19-year career.
The 10-time All-Star led the NBA in assists five times and finished second all-time in assists and steals behind John Stockton. He was an absolute genius when it came to controlling on offense with his passing. During his absolute peak, Kidd was an absolute beast with his ability to rebound the ball and push the ball in transition. His court vision and ability to work angles made him a triple-double beast. The 107 triple-doubles he had ranks more than anyone in the league not named Oscar Robertson and Magic Johnson. He'll go down as one of the best point guards to ever play the game.
Keep in mind, he came into the league as "Ason Kidd" as people didn't believe he could develop a jumper. He's going out ranking third in the history of the NBA with 1,988 3-pointers made. Think about that.
4. What is the next step for him?
I think it would take some time and practice, but I think Kidd could actually do well working in TV. It will likely be a natural transition to move into coaching in some form or fashion. That position and the role of an adviser in the front office make a lot of sense for him. The adviser role makes even more sense for Kidd. He can use his basketball brain when he's needed, but he can spend most of his time on a golf course. That's not a bad gig if you can find it.
Will he make an immediate move or enjoy the non-NBA life for a year? I doubt he actually takes a break. He'll need something to do. It'll just be interesting to see what he actually decides to do.
5. Should his number be in the rafters at the American Airlines Center?
That's going to be one of the more intriguing things to keep track of, especially considering Mark Cuban's comments about him last season. On KESN-FM in August, Cuban said this after being asked if he thought Kidd would have his jersey retired. "J. Kidd is a big boy; he can do whatever he wants," Cuban said. "But you don't change your mind like that. That was … yeah. I'm sure I'll get over it at some point, but as of now, I wouldn't put J. Kidd's number in the rafters."
Cuban was clearly bitter about the departure. Kidd tried to call Cuban to let him know his decision, but Cuban didn't take the call because he was in Washington, D.C. museum with his family.
You would have to think that Cuban eventually cools off and lets Kidd's jersey hang in the rafters. Cuban also has some residual work to do by taking care of Derek Harper and his No. 12. Cuban did comment recently that he's thinking about doing something along the lines of the Cowboys' Ring of Honor. That way, he can do something special and unique for those who won't necessarily have their jersey hanging in the rafters. That said, both Harper and Kidd need their jerseys hanging in the American Airlines Center.
Bryan Gutierrez writes about sportsmen. He also attended Ball So Hard University, studying ideologies of Clark Kent. You can follow him on Twitter @BallinWithBryan.


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This week has been crazy stressful. Life is happening so fast, and in less than two weeks I'll be out of college and into the real world. Crazy, right?!
Throughout all of the stresses that invade my everyday life, I've learned that there is so much more than the paper or the test that seem to bog me down and make life seem so hectic. So, if you're having a stressful pull-out-your-hair kind of day, just remember: it's the little things!
I've made a list of little distractions that make me smile, and hopefully they will do the same for you!
- Cute animals. If you say that a picture of a baby giraffe or a monkey hugging a kitten doesn't make you smile, than you just need to check out these pictures and see if you change your mind!
- Dance it out! Seriously. Dancing is a great way to release endorphins and to forget about everything that's weighing you down, even if it is just for a couple of minutes. I personally love jamming out to this song by Icona Pop or this one by Macklemore featuring Ryan Lewis
- Bake something. After all, a little chocolate goes a long way! Here is one of my favorite truffle recipes by Chocolate Covered Katie (bonus: it only takes five minutes!)
- Watch a funny movie. My suggestions? Me, Myself & Irene, There's Something About Mary, or Finding Nemo. Dory knows how to perk my day right up!
- Exercise. If working out isn't your thing, go for a walk or just do something active. Working out releases endorphins, making you happier!
Well, there you have it. Just a small list of a few things that perk me up and can help you get through a tough week!
Oh, one thing I didn't put on my list……..
……..can you guess what it is?
Shopping! Retail therapy always does a body good. But remember to spend within your means, because I know there has been a time or two that I've spent more than I should, and the clothes will only make you happy for so long!
Have a great Thursday!


text: Just got a tune after the install of my 3600 stall and i am not very impressed. When the car is in drive and you get on it, the rpms go up and then they feel like they level off before it shifts and kinda does the same thing in second but not as bad. It only does this when the car is in drive and not when manually shifting. Could this be the tune or is my tranny on its way out?


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Program Leadership
Program Administration
Bill Reckmeyer | Director & Co-Founder
(Professor of Leadership & Systems – Anthropology, College of Social Sciences; and IES Senior Fellow, International and Extended Studies)
Director & Co-Founder, SJSU Salzburg Program (2006-2011). Salzburg Global Fellow (1995-2004) and Faculty Chair, International Study Program in Global Citizenship (2004-Present). Fulbright Host/Scholar (2007); Chief Systems Scientist, Systems of Systems Center of Excellence (2003-2006); Kellogg National Leadership Fellow (1988-1992); President, American Society for Cybernetics (1982-1985). Visiting Professor at leading universities in US, Europe, and Australia, including Sydney (2010); Stanford (2009-2010; 2003-2006); Maryland (2000-2005); Harvard (1993-1999); St. Gallen (1995, 1987); Swedish Royal Institute of Technology (1985-1993); and USC (1985-1986). SJSU's Outstanding Professor (2010), Austen D. Warburton Award (2009), and Distinguished Faculty Mentor (2011).
Steering Group
Mark Novak | Co-Founder
(Professor - Sociology, College of Social Sciences)
Dr. Novak has supervised university international and professional development programs for the past 25 years in Canada and the United States, including SJSU's Study Abroad programs and International House. An active scholar in the study of adult development, adult education, and aging, Dr. Novak has written numerous articles for professional journals in the United States and abroad. He has also written several books, including a general interest study on Successful Aging, and other texts in the field of gerontology. He has served on the board of the San Jose Jazz Society, currently serves on the board of the Silicon Valley Workforce Investment Network, and is an active member of Rotary International.
Cynthia Rostankowski | Faculty Advisor - Salzburg Scholars
(Coordinator | Humanities Honors Program)
SJSU Salzburg Fellow (2010), SJSU Salzburg Faculty Advisor (2013-2014), and SJSU Salzburg Steering Group (2012-Present). A former Fulbright-Hays Scholar at Georg-August University in Göttingen, Germany, Cynthia teaches team-taught, interdisciplinary, and multicultural courses in the humanities from antiquity to the 21st century. Areas of research and interest include topics in aesthetics and philosophy of art, medieval culture, and student thriving. Past board of trustees member of the American Society for Aesthetics. B.A. in Philosophy and German, Marquette University, Wisconsin; M.A. and Ph.D. in Philosophy, Stanford University, California.
Brad Stone | Chair - Fellows Selection Committee
(Chair & Professor - Chemistry, College of Science)
SJSU Distinguished Service Award (2013); SJSU Salzburg Fellow (2008); SJSU Salzburg Steering Group (2010-2013); and former Chair of the University Council of Chairs and Directors (2007-2010). Brad has been a long-time research collaborator in photo physics and microfluidics at Imperial College, London (1994-present); and in planetary atmospheres and laboratory astrophysics at NASA Ames Research Center (1989-present). He has also served as University Co-Director, NASA Faculty Fellowship Program (2000-2005). A jazz aficionado, Brad has 30+ years of experience as radio programmer, KSJS faculty advisor, and jazz music director. He is a 7-time recipient of the Jazz Programmer of the Year Award (Jazz-Week and Gavin); the Duke DuBois Humanitarian Award (JazzWeek 2008); and is a long-time Board Member for San José Jazz as well as the Jazz Organ Fellowship.
Andy Wood | Faculty Advisor - Salzburg Scholars Club
(Professor - Communication Studies, College of Social Sciences; and Humanities Honors Program, College of Humanities & Arts)
SJSU Salzburg Fellow (2010); SJSU Salzburg Faculty Advisor (2012); Faculty Advisor, SJSU Salzburg Scholars Club (2013), and SJSU Salzburg Steering Group (2011-2013). Dr. Wood has authored or co-authored books on internet communication, reality television, roadside Americana, and the 1939 New York World's Fair. His latest book on City Ubiquitous: Place, Communication, and the Rise of Omnitopia received the 2009 Jane Jacobs Urban Communication Award from the Urban Communication Foundation. His peer-reviewed articles have appeared in Critical Studies in Media Communication, Text and Performance Quarterly, and Communication Theory. He has also served on editorial boards for several regional and national journals. An in-demand speaker & commentator, Andy's ideas have appeared in USA Today, National Public Radio, and other media.
Carmel Weiler | Program Assistant - Salzburg Orientation
(Graduate Student – MA in Philosophy, College of Humanities & Arts)
SJSU Salzburg Scholar (2012). BA in Humanities, minor in German from SJSU (2011); Dean's Scholar (2011); and member of the Provost's Honors Seminar (2012). Carmel is a member of Phi Kappa Phi (2012) and of Toastmasters International (2001-Present), where she has served as Club President (2002-2003), Area Governor (2003-2004), Division Governor (2004-2005), and Distinguished Toastmaster (2004). She is fluent in German, having spent her senior year of high school as an exchange student in Germany with Youth For Understanding, and has volunteered with YFU as a team teacher specializing in the Pre-Departure Orientation program. Carmel is President and Technology Consultant for ComTego, Inc. since founding it in 2001. Prior to that, she was an IT Manager for several Silicon Valley startups.


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What a great year it was for Samsung, and that success was just recognized. GSMA, organizers of the Mobile World Congress event, have just announced that Samsung was named "Manufacturer of the year" and that the Samsung Galaxy S II was named "Smartphone of the year" at the organization's annual GSMA Awards. According to sources close to the scene, the Samsung Reps were ecstatic when the news came in. Congratulations Samsung on a great handset, and a phenomenal year! Source: GSMA image Via: Mobile Burn
Just disappointed about their two tabs being dual core, but the Note 10.1 seems nice Haha lol global mobile. Motorola Droid 2: CyanogenMod 9