Kiril Ivanov Simov

On-To-Knowledge: Semantic Web-Enabled Knowledge Management

On-To-Knowledge builds an ontology-based tool environment to improve knowxadledge management, dealing with large numbers of heterogeneous, distributed, and semistructured documents typically found in large company intranets and the World-Wide Web. The project’s target results are: (i) a toolset for semantic information processing and user access; (ii) OIL, an ontology-based inference layer for the World-Wide Web; and (iii) an associated methodology and validation by industrial case studies. This chapter offers an overview of the On-To-Knowledge approach to knowledge management.

MULTISENSOR: Development of multimedia content integration technologies for journalism, media monitoring and international exporting decision support.

This paper presents an overview and the first results of the FP7 MULTISENSOR project, which deals with multidimensional content integration of multimedia content for intelligent sentiment enriched and context oriented interpretation. MULTISENSOR aims at providing unified access to multilingual, multimedia and multicultural economic, news story material across borders in order to support journalism and media monitoring tasks and provide decision support for internationalisation of companies.

Mapping data driven and upper level ontology

The Linking Open data (LOD) [1] initiative aims to facilitate the emergence of a web of linked data by publishing and interlinking open data on the web in RDF. The access to these data becomes increasingly a challenge. This paper presents an innovative method for retrieving facts from vast amounts of data by using an upper level ontology (PROTON) [2] as an access interface. FactForge, the largest and most heterogeneous body of general factual knowledge that was ever used for logical inference served to develop and test the method of mapping ontologies.

FactForge: data service or the diversity of inferred knowledge over LOD

Linked Open Data movement is maturing. LOD cloud increases by billions of triples yearly. Technologies and guidelines about how to produce LOD fast, how to assure their quality, and how to provide vertical oriented data services are being developed (LOD2, LATC, baseKB). Little is said however about how to include reasoning in the LOD framework, and about how to cope with its diversity. This paper deals with this topic. It presents a data service --- FactForge --- the biggest body of general knowledge from LOD on which inference is performed. It has close to 16B triples available for querying, derived from about 2B explicit triples, after inference and some OWLIM repository specific optimization. We discuss the impacts of the reference layer of FactForge and inference on the diversity of the web of data, and argue for a new paradigm of data services based on linked data verticals, and on inferred knowledge.