Provenance for SPARQL queries

Determining trust of data available in the Semantic Web is fundamental for applications and users, in particular for linked open data obtained from SPARQL endpoints. There exist several proposals in the literature to annotate SPARQL query results with values from abstract models, adapting the seminal works on provenance for annotated relational databases. We provide an approach capable of providing provenance information for a large and significant fragment of SPARQL 1.1, including for the first time the major non-monotonic constructs under multiset semantics. The approach is based on the translation of SPARQL into relational queries over annotated relations with values of the most general m-semiring, and in this way also refuting a claim in the literature that the OPTIONAL construct of SPARQL cannot be captured appropriately with the known abstract models.Comment: 22 pages, extended version of the ISWC 2012 paper including proof

Blog ontology (BloOn) and Blog visualization system (BloViS)

Blogs have emerged as a powerful way to convey and spread any sort of ideas. Thousands of people write daily in these on-line diaries and hold a captive audience. Furthermore information spread in blogs provide an online laboratory to analyze how brands, trends, ideas and information spread through social communities in the Internet. To support this analysis, in this paper describes an ontology for Blogs (BloOn) which describes the blog data domain and enable services for analyzing and reasoning over blog data across applications; and proposes a blog ontology visualization system (BloViS) based on 3D visualization techniques, metaphors and the incorporation of virtual world features for the users to investigate the nature of meme dissemination. We expect that this proposed Information Visualization platform may advance the state of the art of Internet memetics phenomena, blog ontology and 3D visualization of digital memes

Introducing Contextual Reasoning to the Semantic Web with OWL^c

Representing the context of triples and reasoning on contextualized triples is an open problem in the semantic web. In this paper, we present OWLc: a contextual two-dimensional web ontology language. Using the first dimension, we can define contexts-dependent classes, properties, and axioms and using the second dimension, we can express knowledge about contexts which we consider formal objects, as proposed by McCarthy [17]. Moreover, we describe a contextual extension of the OWL entailment rules, and we present a new set of rules for reasoning on contexts. We demonstrate the modeling strength and reasoning capabilities of OWLc with a practical scenario from the digital humanity domain. We chose the FDS project in virtue of its inherent contextual nature, as well as its notable complexity which allow us to highlight many issues connected with contextual knowledge representation and reasoning

Publishing Uncertainty on the Semantic Web: Blurring the LOD Bubbles

International audience.The open nature of the Web exposes it to the many imper-fections of our world. As a result, before we can use knowledge obtainedfrom the Web, we need to represent that fuzzy, vague, ambiguous and un-certain information. Current standards of the Semantic Web and LinkedData do not support such a representation in a formal way and indepen-dently of any theory. We present a new vocabulary and a framework tocapture and handle uncertainty in the Semantic Web. First, we definea vocabulary for uncertainty and explain how it allows the publishingof uncertainty information relying on different theories. In addition, weintroduce an extension to represent and exchange calculations involvedin the evaluation of uncertainty. Then we show how this model and itsoperational definitions support querying a data source containing differ-ent levels of uncertainty metadata. Finally, we discuss the perspectiveswith a view on supporting reasoning over uncertain linke

Focusing on Precision- and Trust-Propagation in Knowledge Processing Systems

Kuuluu myös sarjaan: Information Systems and Applications, incl. Internet/Web, and HCIIn knowledge processing systems, when gathered data and knowledge from several (external sources) is used, the trustworthiness and quality of the information and data has to be evaluated before continuing processing with these values. We try to address the problem of the evaluation and calculation of possible trusting values by considering established methods from known literature and recent research. After the calculation, the obtained values have to be processed, depending on the complexity of the system, where the values are used and needed. Here the way of trust propagation, precision propagation and their aggregation or fusion is crucial, when multiple input values come together in one processing step. We discuss elaborated trust definitions already available and according options for trust and precision aggregation and propagation in units of knowledge processing.201