anchor.diff:  Using an implemented system we empirically show that we can improve result freshness by 93% over baseline algorithms such as Random Selection or Least Recently Updated."
   research_gavankar_5,5,"Chetana Gavankar, Yuan-Fang Li and Ganesh Ramakrishnan",gavankar,Explicit Query Interpretation and Diversification for Context-driven Concept Search across Ontologies,research,Search (I),1,Chetana Gavankar,paper_M110_.pdf,502,2016-10-20T13:30:00,2016-10-20T14:50:00,"We build on our earlier finding that more than 95% of the triples in actual RDF triple graphs have a remarkably tabular structure, whose schema does not necessarily follow from explicit metadata such as ontologies, but which an RDF store can automatically derive by looking at the data using so-called ``emergent schema'' detection techniques. In this paper we investigate how computers and in particular RDF stores can take advantage from this emergent schema to more compactly store RDF data and more efficiently optimize and execute SPARQL queries. To this end, we contribute techniques for efficient emergent schema aware RDF storage and new query operator algorithms for emergent schema aware scans and joins. In all, these techniques allow RDF schema processors fully catch up with relational database techniques in terms of rich physical database design options and efficiency, without requiring a rigid upfront schema structure definition."
   research_guclu_16,16,"Isa Guclu, Yuan-Fang Li, Jeff Z. Pan and Martin J. Kollingbaum",guclu,Predicting Energy Consumption of Ontology Reasoning over Mobile Devices,research,Smart Planet,1,Isa Guclu,paper_M304_.pdf,504+505,2016-10-19T14:00:00,2016-10-19T15:20:00,"The unprecedented growth in mobile devices, combined with advances in Semantic Web Technologies, has given birth to opportunities for more intelligent systems on-the-go. Limited resources of mobile devices, especially energy, demand approaches to make mobile reasoning more applicable. While Mobile-Cloud integration is a promising method for harnessing the power of semantic technologies in the mobile infrastructure, it is an open question on deciding when to reason with ontologies on mobile devices. In this paper, we introduce an energy consumption prediction mechanism for ontology reasoning on mobile devices, which allows analysis of feasibility of ontology reasoning on mobile devices in terms of energy consumption. The prediction models contributes to mobile-cloud integration and helps improve further development of ontology and semantic solutions in general."
  -research_hartig_31,31,Olaf Hartig and M. Tamer Özsu,hartig,Walking without a Map: Ranking-Based Traversal for Querying Linked Data,research,Search (II),4,Olaf Hartig ,paper_M164_.pdf,504+505,2016-10-20T15:30:00,2016-10-20T16:50:00,"The emergence of Linked Data on the WWW has spawned research interest in an online execution of declarative queries over this data. A particularly interesting approach is traversal-based query execution which fetches data by traversing data links and, thus, is able to make use of up-to-date data from initially unknown data sources. While the downside of this approach is the delay before the query engine completes a query execution, user perceived response time may be improved significantly by returning as many elements of the result set as soon as possible. To this end, the query engine requires a traversal strategy that enables the engine to fetch result-relevant data as early as possible. The challenge for such a strategy is that the query engine does not know a priori what data sources will be discovered during the query execution and which of them contain result-relevant data. In this paper, we investigate 14 different approaches to rank traversal steps and achieve a variety of traversal strategies. We experimentally study their impact on response times and compare them to a baseline that resembles a breadth-first traversal. While our experiments show that some of the approaches can achieve noteworthy improvements over the baseline in a significant number of cases, we also observe that for every approach, there is a non-negligible chance to achieve response times that are worse than the baseline."
  +research_hartig_31,31,Olaf Hartig and M. Tamer Özsu,hartig,Walking without a Map: Ranking-Based Traversal for Querying Linked Data,research,Search (II),3,Olaf Hartig ,paper_M164_.pdf,504+505,2016-10-20T15:30:00,2016-10-20T16:50:00,"The emergence of Linked Data on the WWW has spawned research interest in an online execution of declarative queries over this data. A particularly interesting approach is traversal-based query execution which fetches data by traversing data links and, thus, is able to make use of up-to-date data from initially unknown data sources. While the downside of this approach is the delay before the query engine completes a query execution, user perceived response time may be improved significantly by returning as many elements of the result set as soon as possible. To this end, the query engine requires a traversal strategy that enables the engine to fetch result-relevant data as early as possible. The challenge for such a strategy is that the query engine does not know a priori what data sources will be discovered during the query execution and which of them contain result-relevant data. In this paper, we investigate 14 different approaches to rank traversal steps and achieve a variety of traversal strategies. We experimentally study their impact on response times and compare them to a baseline that resembles a breadth-first traversal. While our experiments show that some of the approaches can achieve noteworthy improvements over the baseline in a significant number of cases, we also observe that for every approach, there is a non-negligible chance to achieve response times that are worse than the baseline."
   research_hoffner_18,18,"Konrad Höffner, Jens Lehmann and Ricardo Usbeck",hoffner,CubeQA—Question Answering on RDF Data Cubes,research,Search (II),1,Konrad Höffner,paper_M10_.pdf,504+505,2016-10-20T15:30:00,2016-10-20T16:50:00,"Statistical data in the form of RDF Data Cubes is becoming increasingly valuable as it influences decisions in areas such as health care, policy and finance. While a growing amount is becoming freely available through the open data movement, this data is opaque to laypersons. Semantic Question Answering (SQA) technologies provide access via free-form natural language queries but general SQA systems cannot process RDF Data Cubes. On the intersection between RDF Data Cubes and SQA, we create a new subfield of SQA, called RDCQA. We create an RDQCA benchmark as task 3 of the QALD-6 evaluation challenge, to stimulate further research and enable quantitative comparison between RDCQA systems. We design and evaluate the CubeQA algorithm, which
   is the first RDCQA system and achieves a global F 1 score of 0.43 on the QALD6T3-test dataset, showing that RDCQA is feasible."
   research_ibragimov_7,7,"Dilshod Ibragimov, Katja Hose, Torben Bach Pedersen and Esteban Zimanyi",ibragimov,Optimizing Aggregate SPARQL Queries using Materialized RDF Views,research,Querying,3,Dilshod Ibragimov,paper_M127_.pdf,502,2016-10-19T14:00:00,2016-10-19T15:20:00,"During the past couple of years, more and more data has been published as native RDF datasets. In this setup, both the size of the datasets and the need to process aggregate queries represent challenges for standard SPARQL query processing techniques. To overcome these limitations, materialized views can be created and used as a source of precomputed partial results during query processing. However, materialized view techniques, as proposed in relational databases, do not support RDF specifics, such as incompleteness and the need to support implicit (derived) information. Therefore, to overcome these challenges, this paper proposes MARVEL – the approach consisting of a view selection algorithm based on an RDF-specific cost model, a view definition syntax. and an algorithm for rewriting SPARQL queries using materialized RDF views. The experimental evaluation shows that the approach can improve query response time by more than an order of magnitude and is able to efficiently handle RDF specifics."
  
positive.code: As the PhD is still in an early phase, we hope to narrow the scope in the next months,
  based on feedback of the doctoral consortium."
  journal_saleem_6,6,"Muhammad Saleem,  Yasar Khan,  Ali Hasnain,  Ivan Ermilov and Axel-Cyrille Ngonga Ngomo",saleem,A Fine-Grained Evaluation of SPARQL Endpoint Federation Systems,journal,Querying/SPARQL (I),4,Muhammad Saleem,http://www.semantic-web-journal.net/content/fine-grained-evaluation-sparql-endpoint-federation-systems-2,501,2016-10-20T10:30:00,2016-10-20T11:50:00,"The Web of Data has grown enormously over the last years. Currently, it comprises a large compendium of interlinked and distributed datasets from multiple domains. Running complex queries on this compendium often requires accessing data from different endpoints within one query. The abundance of datasets and the need for running complex query has thus motivated a considerable body of work on SPARQL query federation systems, the dedicated means to access data distributed over the Web of Data. However, the granularity of previous evaluations of such systems has not allowed deriving of insights concerning their behavior in different steps involved during federated query processing. In this work, we perform extensive experiments to compare state-of-the-art SPARQL endpoint federation systems using the comprehensive performance evaluation framework FedBench. In addition to considering the tradition query runtime as an evaluation criterion, we extend the scope of our performance evaluation by considering criteria, which have not been paid much attention to in previous studies. In particular, we consider the number of sources selected, the total number of SPARQL ASK requests used, the completeness of answers as well as the source selection time. Yet, we show that they have a significant impact on the overall query runtime of existing systems. Moreover, we extend FedBench to mirror a highly distributed data environment and assess the behavior of existing systems by using the same performance criteria. As the result we provide a detailed analysis of the experimental outcomes that reveal novel insights for improving current and future SPARQL federation systems."
  journal_nuzzolese_1,1,"Andrea Giovanni Nuzzolese, Valentina Presutti, Aldo Gangemi, Silvio Peroni and Paolo Ciancarini",nuzzolese,Aemoo: Linked Data exploration based on Knowledge Patterns,journal,Search (II),3,Andrea Giovanni Nuzzolese,http://dx.doi.org/10.1016/j.websem.2015.12.001,504+505,2016-10-20T15:30:00,2016-10-20T16:50:00,"This paper presents a novel approach to Linked Data exploration that uses Encyclopedic Knowledge Patterns (EKPs) as relevance criteria for selecting, organising, and visualising knowledge. EKP are discovered by mining the linking structure of Wikipedia and evaluated by means of a user-based study, which shows that they are cognitively sound as models for building entity summarisations. We implemented a tool named Aemoo that supports EKP-driven knowledge exploration and integrates data coming from heterogeneous resources, namely static and dynamic knowledge as well as text and Linked Data. Aemoo is evaluated by means of controlled, task-driven user experiments in order to assess its usability, and ability to provide relevant and serendipitous information as compared to two existing tools: Google and RelFinder."
  journal_mazumdar_2,2,"Suvodeep Mazumdar, Daniela Petrelli,  Khadija Elbedweihy,  Vitaveska Lanfranchi and Fabio Ciravegna",mazumdar,Affective Graphs: The Visual Appeal of Linked Data,journal,Interaction,4,Suvodeep Mazumdar,http://www.semantic-web-journal.net/content/affective-graphs-visual-appeal-linked-data,504+505,2016-10-19T11:00:00,2016-10-19T12:20:00,"The essence and value of Linked Data lies in the ability of humans and machines to query, access and reason upon highly structured and formalised data. Ontology structures provide an unambiguous description of the structure and content of data. While a multitude of software applications and visualization systems have been developed over the past years for Linked Data, there is still a significant gap that exists between applications that consume Linked Data and interfaces that have been designed with significant focus on aesthetics. Though the importance of aesthetics in affecting the usability, effectiveness and acceptability of user interfaces have long been recognised, little or no explicit attention has been paid to the aesthetics of Linked Data applications. In this paper, we introduce a formalised approach to developing aesthetically pleasing semantic web interfaces by following aesthetic principles and guidelines identified from literature. We apply such principles to design and develop a generic approach of using visualizations to support exploration of Linked Data, in an interface that is pleasing to users. This provides users with means to browse ontology structures, enriched with statistics of the underlying data, facilitating exploratory activities and enabling visual query for highly precise information needs. We evaluated our approach in three ways: an initial objective evaluation comparing our approach with other well-known interfaces for the semantic web and two user evaluations with semantic web researchers."
  journal_rospocher_8,8,"Marco Rospocher, Marieke van Erp, Piek Vossen, Antske Fokkens, Itziar Aldabe, German Rigau, Aitor Soroa, Thomas Ploeger and Tessel Bogaard",rospocher,Building event-centric knowledge graphs from news,journal,Natural Language Processing,4,Marco Rospocher,http://dx.doi.org/10.1016/j.websem.2015.12.004,504+505,2016-10-21T10:30:00,2016-10-21T11:50:00,"Knowledge graphs have gained increasing popularity in the past couple of years, thanks to their adoption in everyday search engines. Typically, they consist of fairly static and encyclopedic facts about persons and organizations–e.g. a celebrity’s birth date, occupation and family members–obtained from large repositories such as Freebase or Wikipedia. In this paper, we present a method and tools to automatically build knowledge graphs from news articles. As news articles describe changes in the world through the events they report, we present an approach to create Event-Centric Knowledge Graphs (ECKGs) using state-of-the-art natural language processing and semantic web techniques. Such ECKGs capture long-term developments and histories on hundreds of thousands of entities and are complementary to the static encyclopedic information in traditional knowledge graphs. We describe our event-centric representation schema, the challenges in extracting event information from news, our open source pipeline, and the knowledge graphs we have extracted from four different news corpora: general news (Wikinews), the FIFA world cup, the Global Automotive Industry, and Airbus A380 airplanes. Furthermore, we present an assessment on the accuracy of the pipeline in extracting the triples of the knowledge graphs. Moreover, through an event-centered browser and visualization tool we show how approaching information from news in an event-centric manner can increase the user’s understanding of the domain, facilitates the reconstruction of news story lines, and enable to perform exploratory investigation of news hidden facts."""
  journal_gerber_11,11,"Daniel Gerber, Diego Esteves, Jens Lehmann, Lorenz Bühmann, Ricardo Usbeck, Axel-Cyrille Ngonga Ngomo and René Speck",gerber,DeFacto - Temporal and multilingual Deep Fact Validation,journal,Knowledge Graphs,3,Daniel Gerber,http://dx.doi.org/10.1016/j.websem.2015.08.001,501,2016-10-19T11:00:00,2016-10-19T12:20:00,"One of the main tasks when creating and maintaining knowledge bases is to validate facts and provide sources for them in order to ensure correctness and traceability of the provided knowledge. So far, this task is often addressed by human curators in a three-step process: issuing appropriate keyword queries for the statement to check using standard search engines, retrieving potentially relevant documents and screening those documents for relevant content. The drawbacks of this process are manifold. Most importantly, it is very time-consuming as the experts have to carry out several search processes and must often read several documents. In this article, we present DeFacto (Deep Fact Validation)—an algorithm able to validate facts by finding trustworthy sources for them on the Web. DeFacto aims to provide an effective way of validating facts by supplying the user with relevant excerpts of web pages as well as useful additional information including a score for the confidence DeFacto has in the correctness of the input fact. To achieve this goal, DeFacto collects and combines evidence from web pages written in several languages. In addition, DeFacto provides support for facts with a temporal scope, i.e., it can estimate in which time frame a fact was valid. Given that the automatic evaluation of facts has not been paid much attention to so far, generic benchmarks for evaluating these frameworks were not previously available. We thus also present a generic evaluation framework for fact checking and make it publicly available."
  journal_mccrae_7,7,"John P. McCrae, Mihael Arcan, Kartik Asooja, Jorge Gracia, Paul Buitelaar and Philipp Cimiano",mccrae,Domain Adaptation for Ontology Localization,journal,Multilinguality,4,John P. McCrae,http://dx.doi.org/10.1016/j.websem.2015.12.001,501,2016-10-19T14:00:00,2016-10-19T15:20:00,"Ontology localization is the task of adapting an ontology to a different cultural context, and has been identified as an important task in the context of the Multilingual Semantic Web vision. The key task in ontology localization is translating the lexical layer of an ontology, i.e., its labels, into some foreign language. For this task, we hypothesize that the translation quality can be improved by adapting a machine translation system to the domain of the ontology. To this end, we build on the success of existing statistical machine translation (SMT) approaches, and investigate the impact of different domain adaptation techniques on the task. In particular, we investigate three techniques: (i) enriching a phrase table by domain-specific translation candidates acquired from existing Web resources, (ii) relying on Explicit Semantic Analysis as an additional technique for scoring a certain translation of a given source phrase, as well as (iii) adaptation of the language model by means of weighting n-grams with scores obtained from topic modelling. We present in detail the impact of each of these three techniques on the task of translating ontology labels. We show that these techniques have a generally positive effect on the quality of translation of the ontology and that, in combination, they provide a significant improvement in quality. "
  journal_arenas_12,12,"Marcelo Arenas, Bernardo Cuenca Grau, Evgeny Kharlamov, Šarūnas Marciuška and Dmitriy Zheleznyakov",arenas,Faceted search over RDF-based knowledge graphs,journal,Search (II),2,Marcelo Arenas,http://dx.doi.org/10.1016/j.websem.2015.12.002,504+505,2016-10-20T15:30:00,2016-10-20T16:50:00,"Knowledge graphs such as Yago and Freebase have become a powerful asset for enhancing search, and are being intensively used in both academia and industry. Many existing knowledge graphs are either available as Linked Open Data, or they can be exported as RDF datasets enhanced with background knowledge in the form of an OWL 2 ontology. Faceted search is the de facto approach for exploratory search in many online applications, and has been recently proposed as a suitable paradigm for querying RDF repositories. In this paper, we provide rigorous theoretical underpinnings for faceted search in the context of RDF-based knowledge graphs enhanced with OWL 2 ontologies. We identify well-defined fragments of SPARQL that can be naturally captured using faceted search as a query paradigm, and establish the computational complexity of answering such queries. We also study the problem of updating faceted interfaces, which is critical for guiding users in the formulation of meaningful queries during exploratory search. We have implemented our approach in a fully-fledged faceted search system, SemFacet, which we have evaluated over the Yago knowledge graph."
  journal_vandenbussche_3,3,"Pierre-Yves Vandenbussche, Ghislain A. Atemezing, Maria Poveda and Bernard Vatant",vandenbussche,Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the Web,journal,Ontologies (I),1,Pierre-Yves Vandenbussche,http://www.semantic-web-journal.net/content/linked-open-vocabularies-lov-gateway-reusable-semantic-vocabularies-web-1,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"One of the major barriers to the deployment of Linked Data is the difficulty that data publishers have in determining which vocabularies to use to describe the semantics of data. This system report describes Linked Open Vocabularies (LOV), a high quality catalogue of reusable vocabularies for the description of data on the Web. The LOV initiative gathers and makes visible indicators that have not been previously harvested such as the interconnections between vocabularies, version history along with past and current referent (individual or organization). The report details the various components of the system along with some innovations such as the introduction of a property-level boost in the vocabulary search scoring which takes into account the property's type (e.g rdfs:label, dc:comment) associated with a matching literal value. By providing an extensive range of data access methods (full-text search, SPARQL endpoint, API, data dump or UI), the project aims at facilitating the reuse of well-documented vocabularies in the Linked Data ecosystem. The adoption of LOV by many applications and methods shows the importance of such a set of vocabularies and related features for the ontology design activity and the publication of data on the Web."
  journal_calvanese_4,4,"Diego Calvanese, Benjamin Cogrel, Sarah Komla-Ebri, Roman Kontchakov, Davide Lanti, Martin Rezk, Mariano Rodriguez-Muro and Guohui Xiao",calvanese,Ontop: Answering SPARQL queries over relational databases,journal,Querying/SPARQL (II),3,Diego Calvanese,http://www.semantic-web-journal.net/content/ontop-answering-sparql-queries-over-relational-databases-1,502,2016-10-21T13:30:00,2016-10-21T14:50:00,"We present Ontop, an open-source Ontology-Based Data Access (OBDA) system that allows for querying relational data sources through a conceptual representation of the domain of interest, provided in terms of an ontology, to which the data sources are mapped. Key features of Ontop are its solid theoretical foundations, a virtual approach to OBDA, which avoids materializing triples and is implemented through the query rewriting technique, extensive optimizations exploiting all elements of the OBDA architecture, its compliance to all relevant W3C recommendations (including SPARQL queries, R2RML mappings, and OWL 2 QL and RDFS ontologies), and its support for all major relational databases."
  journal_lephuoc_9,9,Danh Le-Phuoc,lephuoc,Operator-aware approach for boosting performance in RDF stream processing,journal,Streams,3,Danh Le-Phuoc,http://dx.doi.org/10.1016/j.websem.2016.04.001,501,2016-10-20T13:30:00,2016-10-20T14:50:00,"To enable efficiency in stream processing, the evaluation of a query is usually performed over bounded parts of (potentially) unbounded streams, i.e., processing windows “slide” over the streams. To avoid inefficient re-evaluations of already evaluated parts of a stream in respect to a query, incremental evaluation strategies are applied, i.e., the query results are obtained incrementally from the result set of the preceding processing state without having to re-evaluate all input buffers. This method is highly efficient but it comes at the cost of having to maintain processing state, which is not trivial, and may defeat performance advantages of the incremental evaluation strategy. In the context of RDF streams the problem is further aggravated by the hard-to-predict evolution of the structure of RDF graphs over time and the application of sub-optimal implementation approaches, e.g., using relational technologies for storing data and processing states which incur significant performance drawbacks for graph-based query patterns. To address these performance problems, this paper proposes a set of novel operator-aware data structures coupled with incremental evaluation algorithms which outperform the counterparts of relational stream processing systems. This claim is demonstrated through extensive experimental results on both simulated and real datasets."
  journal_zaveri_5,5,"Amrapali Zaveri, Anisa Rula, Andrea Maurino, Ricardo Pietrobon, Jens Lehmann and Sören Auer",zaveri,Quality Assessment for Linked Data: A Survey,journal,Linked Data Measurement,4,Amrapali Zaveri,http://www.semantic-web-journal.net/content/quality-assessment-linked-data-survey,504+505,2016-10-20T13:30:00,2016-10-20T14:50:00,"The development and standardization of semantic web technologies has resulted in an unprecedented volume of data being published on the Web as Linked Data (LD). However, we observe widely varying data quality ranging from extensively curated datasets to crowdsourced and extracted data of relatively low quality. In this article, we present the results of a systematic review of approaches for assessing the quality of LD. We gather existing approaches and analyze them qualitatively. In particular, we unify and formalize commonly used terminologies across papers related to data quality and provide a comprehensive list of 18 quality dimensions and 69 metrics. Additionally, we qualitatively analyze the 30 core approaches and 12 tools using a set of attributes. The aim of this article is to provide researchers and data curators a comprehensive understanding of existing work, thereby encouraging further experimentation and development of new approaches focused towards data quality, specifically for LD."
  
negative.code: application_charron_21,21,"Bruno Charron, Yu Hirate, David Purcell and Martin Rezk",charron,Extracting Semantic Information for e-Commerce,application,Enriching Data Sources,4,Bruno Charron,paper_A21_.pdf,502,2016-10-21T15:30:00,2016-10-21T16:50:00,"In model-based systems engineering a model specifying the system's design is shared across a variety of disciplines and used to ensure the consistency and quality of the overall design. Existing implementations for describing these system models exhibit a number of shortcomings regarding their approach to data management. In this emerging applications paper, we present the application of an ontology for space system design providing increased semantic soundness of the underlying standardized data specification, enabling reasoners to identify problems in the system, and allowing the application of operational knowledge collected over past projects to the system to be designed. Based on a qualitative evaluation driven by data derived from an actual satellite design project, a reflection on the applicability of ontologies in the overall model-based systems engineering approach is pursued."
  application_egami_54,54,"Shusaku Egami, Takahiro Kawamura and Akihiko Ohsuga",egami,Building Urban LOD for Solving Illegally Parked Bicycles in Tokyo,application,Smart Planet,2,Shusaku Egami,paper_A54_.pdf,504+505,2016-10-19T14:00:00,2016-10-19T15:20:00,"The illegal parking of bicycles is a social problem in Tokyo and other urban areas. The purpose of this study was to sustainably build Linked Open Data (LOD) for the illegally parked bicycles and to support the problem solving by raising social awareness, in cooperation with the Bureau of General Affairs of Tokyo. We first extracted information on the problem factors and designed LOD schema for illegally parked bicycles. Then we collected pieces of data from Social Networking Service (SNS) and websites of municipalities to build the illegally parked bicycle LOD (IPBLOD) with more than 200,000 triples. We then estimated the missing data in the LOD based on the causal relations from the problem factors. As a result, the number of illegally parked bicycles can be inferred with 70.9% accuracy. Finally, we published the complemented LOD and a Web application to visualize the distribution of illegally parked bicycles in the city. We hope this raises social attention on this issue."
  application_hennig_6,6,"Christian Hennig, Alexander Viehl, Benedikt Kämpgen and Harald Eisenmann",hennig,Ontology-Based Design of Space Systems,application,Ontologies (I),4,Christian Hennig,paper_A6_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"We present a domain-agnostic system for Question Answering over multiple semi-structured and possibly linked datasets without the need of a training corpus. The system is motivated by an industry use-case where Enterprise Data needs to be combined with a large body of Open Data to fulfill information needs not satisfied by prescribed application data models. Our proposed Question Answering pipeline combines existing components with novel methods to perform, in turn, linguistic analysis of a query, named entity extraction, entity / graph search, fusion and ranking of possible answers. We evaluate QuerioDALI with two open-domain benchmarks and a biomedical one over Linked Open Data sources, and show that our system produces comparable results to systems that require training data and are domain-dependent. In addition, we analyze the current challenges and shortcomings."
  application_kharlamov_13,13,"Evgeny Kharlamov, Bernardo Cuenca Grau, Ernesto Jiménez-Ruiz, Steffen Lamparter, Gulnar Mehdi, Martin Ringsquandl, Yavor Nenov, Stephan Grimm, Mikhail Roshchin and Ian Horrocks",kharlamov,Capturing Industrial Information Models with Ontologies and Constraints,application,Ontologies (I),1,Evgeny Kharlamov,paper_A13_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"Conserving fossil-based energy to reduce carbon emissions is key to slowing down global warming. The 2015 Paris agreement on climate change emphasised the importance of raising public awareness and participation to address this societal challenge. In this paper we introduce EnergyUse; a social and collaborative platform for raising awareness on climate change, by enabling users to view and compare the actual energy consumption of various appliances, and to share and discuss energy conservation tips in an open and social environment. The platform collects data from smart plugs, and exports appliance consumption information and community generated energy tips as linked data. In this paper we report on the system design, data modelling, platform usage and early deployment with a set of 58 initial participants. We also discuss the challenges, lessons learnt, and future platform developments."
  application_kharlamov_18,18,"Evgeny Kharlamov, Yannis Kotidis, Theofilos Mailis, Christian Neuenstadt, Charalampos Nikolaou, Özgür Lütfü Özcep, Christoforos Svingos, Dmitriy Zheleznyakov, Sebastian Brandt, Ian Horrocks, Steffen Lamparter, Yannis Ioannidis and Ralf Möller",kharlamov,Towards Analytics Aware Ontology Based Access to Static and Streaming Data,application,Ontologies (I),2,Evgeny Kharlamov,paper_A18_.pdf,501,2016-10-20T15:30:00,2016-10-20T16:50:00,"Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data."
  application_lopez_15,15,"Vanessa Lopez, Pierpaolo Tommasi, Spyros Kotoulas and Jiewen Wu",lopez,QuerioDALI: Question Answering over Dynamic and LInked knowledge graphs,application,Medical Applications,3,Vanessa Lopez,paper_A15_.pdf,501,2016-10-21T13:30:00,2016-10-21T14:50:00,"One focus of Semantic Technologies are formalisms that allow to express complex properties of and relationships between classes of data. The declarative nature of these formalisms is close to natural language and human conceptualisation and thus Semantic Technologies enjoy increasing popularity in scenarios where traditional solutions lead to very convoluted procedures which are difficult to maintain and whose correctness is difficult to judge.
  A fruitful application of Semantic Technologies in the field of health care data analysis has emerged from the collaboration between Oxford and Kaiser Permanente a US health care provider (HMO). US HMOs have to annually deliver measurement results on their quality of care to US authorities. One of these sets of measurements is defined in a specification called HEDIS which is infamous amongst data analysts for its complexity. Traditional solutions with either SAS-programs or SQL-queries lead to involved solutions whose maintenance and validation is difficult and binds considerable amount of resources.
  In this paper we present the project in which we have applied Semantic Technologies to compute the most difficult part of the HEDIS measures. We show that we arrive at a clean, structured and legible encoding of HEDIS in the rule language of the RDF-triple store RDFox. We use RDFox's reasoning capabilities and SPARQL queries to compute and extract the results. The results of a whole Kaiser Permanente regional branch could be computed in competitive time by RDFox on readily available commodity hardware. Further development and deployment of the project results are envisaged in Kaiser Permanente."
  