The advances of the Linked Open Data LOD initiative are giving rise to a more structured Web of data. Indeed, a few datasets act as hubs e.g., DBpedia connecting many other datasets. They also made possible new Web services for entity detection inside plain text e.g.,i¾?DBpedia Spotlight, thus allowing for new applications that can benefit from a combination of the Web of documents and the Web of data. To ease the emergence of these new applications, we propose a query-biased algorithm LDRANK for the ranking of web of data resources with associated textual data. Our algorithm combines link analysis with dimensionality reduction. We use crowdsourcing for building a publicly available and reusable dataset for the evaluation of query-biased ranking of Web of data resources detected in Web pages. We show that, on this dataset, LDRANK outperforms the state of the art. Finally, we use this algorithm for the construction of semantic snippets of which we evaluate the usefulness with a crowdsourcing-based approach.
@cite_17: Recently, there has been interest in ranking the resources and generating corresponding expressive descriptions from the Semantic Web. This paper proposes an approach for automatically generating snippets from RDF documents and assisting users in better understanding the content of RDF documents returned by Semantic Web search engines. A heuristic method for discovering topics, based on the occurrences of RDF nodes and the URIs of original RDF documents, is presented and experimented in this paper. In order to make the snippets more understandable, two strategies are proposed and used for ranking the topic-related statements and the query-related statements respectively. Finally, the conclusion is drawn based on the discussion about the performances of our topic discovery and the whole snippet generation approaches on a test dataset provided by Sindice.
@cite_20: With the development of the Semantic Web, more and more ontologies are available for exploitation by semantic search engines. However, while semantic search engines support the retrieval of candidate ontologies, the final selection of the most appropriate ontology is still difficult for the end users. In this paper, we extend existing work on ontology summarization to support the presentation of ontology snippets. The proposed solution leverages a new semantic similarity measure to generate snippets that are based on the given query. Experimental results have shown the potential of our solution in this problem domain that is largely unexplored so far.
@cite_8: A query-relevant snippet for ontology search is useful for deciding if an ontology fits users' needs. In this paper, we illustrate a good snippet in a keyword-based ontology search engine should be with term-association view and compact, and propose an approach to generate it. To obtain term-association view snippets, a model of term association graph for ontology is proposed, and a concept of maximal r-radius subgraph is introduced to decompose the term association graph into connected subgraphs, which preserve close relations between terms. To achieve compactness, in a query-relevant maximal r-radius subgraph, a connected subgraph thereof with a small graph weight is extracted as a sub-snippet. Finally, a greedy method is used to select sub-snippets to form a snippet in consideration of query relevance and compactness without violating the length constraint. An empirical study on our implementation shows that our approach is feasible. An evaluation on effectiveness shows that the term-association view snippet is favored by users, and the compactness helps reading and judgment.
To sum up, we agree with Ge @cite_8 that the main benefit of possessing highly structured data from an RDF graph is the possibility to find non-trivial relationships among the query terms themselves, and also between the query terms and the main concepts of the document. Moreover, we agree with Penin @cite_20 and Bai @cite_17 about the necessity to design a ranking algorithm for RDF statements that considers both the structure of the RDF graph and lexical properties of the textual data. However, we find ourselves in an inverted situation with genuine text extracted from classical Web pages, and RDF graphs automatically generated from these Web pages.