Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

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

Faceted Search over Ontology-Enhanced RDF Data

An increasing number of applications rely on RDF, OWL~2, and SPARQL for storing and querying data. SPARQL, however, is not targeted towards end-users, and suitable query interfaces are needed. Faceted search is a prominent approach for end-user data access, and several RDF-based faceted search systems have been developed. There is, however, a lack of rigorous theoretical underpinning for faceted search in the context of RDF and OWL~2. In this paper, we provide such solid foundations. We formalise faceted interfaces for this context, identify a fragment of first-order logic capturing the underlying queries, and study the complexity of answering such queries for RDF and OWL~2 profiles. We then study interface generation and update, and devise efficiently implementable algorithms. Finally, we have implemented and tested our faceted search algorithms for scalability, with encouraging results.© ACM, 2014. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in CIKM '14 Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, {ISBN#978-1-4503-2598-1, (2014)} http://dx.doi.org/10.1145/2661829.266202

SSwWS: structural model of information architecture

The Web Technologies allow a representation of a domain of knowledge. This facilitates the conversion of an explicit and tacit knowledge to the possibility of adding knowledge to the Web for automatic processing by the computer. For this reason, it has been designed to be an architecture known as SSwWS (Search Semantic with Web Services) or Search Semantic Web Services, to show how to extend the functionality of the Web search and semantic raised by Berners-Lee, on the meta-references, defined in a Web ontology, so that a user on the Internet can find the answers to their questions through Web services in a simple and fast

Ontology-based end-user visual query formulation: Why, what, who, how, and which?

Value creation in an organisation is a time-sensitive and data-intensive process, yet it is often delayed and bounded by the reliance on IT experts extracting data for domain experts. Hence, there is a need for providing people who are not professional developers with the flexibility to pose relatively complex and ad hoc queries in an easy and intuitive way. In this respect, visual methods for query formulation undertake the challenge of making querying independent of users’ technical skills and the knowledge of the underlying textual query language and the structure of data. An ontology is more promising than the logical schema of the underlying data for guiding users in formulating queries, since it provides a richer vocabulary closer to the users’ understanding. However, on the one hand, today the most of world’s enterprise data reside in relational databases rather than triple stores, and on the other, visual query formulation has become more compelling due to ever-increasing data size and complexity—known as Big Data. This article presents and argues for ontology-based visual query formulation for end-users; discusses its feasibility in terms of ontology-based data access, which virtualises legacy relational databases as RDF, and the dimensions of Big Data; presents key conceptual aspects and dimensions, challenges, and requirements; and reviews, categorises, and discusses notable approaches and systems

On prototypes for Winslett’s semantics of DL-Lite ABox evolution

Abstract. Evolution of Knowledge Bases expressed in Description Logics (DLs) proved its importance. Most studies on evolution in DLs have focused on modelbased approaches to evolution semantics and in particular on Winslett’s semantics (WS). It was understood that evolution under WS even in tractable DLs, such as DL-Lite, suffers from inexpressibility, i.e., the result of evolution cannot be expressed in the same logics. In this work we show which combination of DL-Lite logical constructs is responsible for the inexpressibility and explain reasons for such a behaviour. We present novel techniques, based on what we called prototypes, to capture Winslett’s evolution in FO[2] for DL-LiteR. We also discuss which fragments of DL-LiteR are closed under evolution.

Understanding Inexpressibility of Model−Based ABox Evolution in DL−Lite

International audienceEvolution of Knowledge Bases (KBs) expressed in Description Logics (DLs) proved its importance. Recent studies of evolution in DLs mostly focussed on model-based approaches. They showed that evolution of KBs in tractable DLs, such as DL-Lite, suffers from inexpressibility, i.e., the result of evolution cannot be captured in DL-Lite. What is missing in these studies is understanding: in which DL-Lite fragments evolution can be captured, what causes the inexpressibility, which logics is sufficient to express evolution, and whether one can approximate it in DL-Lite. This paper provides some understanding of these issues for both update and revision. We found what DL-Lite formulas make evolution inexpressible and how to capture evolution in their absence. We introduce the notion of prototypes that gives an understanding of how to capture evolution for a rich DL-Lite fragment in FO[2]. Decidability of FO[2] gives possibility for approximations

Capturing model-based ontology evolution at the instance

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