This paper presents an information extraction method, suitable for data-rich documents, based on the knowledge represented in a domain ontology. The extractor combines a fuzzy string matcher and a word sense disambiguation (WSD) algorithm. The fuzzy string matcher finds mentions of terms combining character-level and token-level similarity measures dealing with non-standardized acronyms and inc...

Knowledge management (KM) facilitates the capture, storage, and dissemination of knowledge using information technology. In this paper, we propose a FKM (Fuzzy Knowledge Management) approach to managing fuzzy knowledge through knowledge engineering and fuzzy logic. First, fuzziness is introduced into CGs (Conceptual Graphs) for constructing fuzzy knowledge models. Fuzzy knowledge models are use...

Existing standards for crisp description logics facilitate information exchange between systems that reason with crisp ontologies. Applications with probabilistic or possibilistic extensions of ontologies and reasoners promise to capture more information, because they can deal with more uncertainties or vagueness of information. However, since there are no standards for either extension, inform...

With the rapid development of Internet of Things (IoT), enormous events are produced by various kinds of devices at high speed. Complex Event Processing (CEP) is the key part of IoT middleware which can help the user to get semantic meanings of primitive events. Context-awareness is an important feature of CEP engine. In this paper a high performance distributed context-aware CEP architecture a...

Fuzzy Description Logics (Fuzzy DLs) are logics that allow to deal with structured knowledge affected by fuzziness. Fuzzy DLs are at the heart of Fuzzy OWL 2, a fuzzy version of the standard ontology language OWL 2. Although a relatively important amount of work has been carried out in the last years, fuzzy DLs are open to be extended with several features worked out in other fields. In particu...

The integration of fuzzy sets in ontologies for the Semantic Web can be achieved in different ways. In most cases, fuzzy sets are defined by hand or with some heuristic procedure that does not take into account the distribution of available data. In this paper, we describe a method for introducing a granular view of data within an OWL ontology.

This position paper proposes a collaboration method between Semantic Web and Fuzzy Logic aiming to handle uncertainty in the information retrieval process in order to cover more relevant items in result of search process. The collaboration method employs OWL ontology in query enhancement, RDF in annotation process and fuzzy rules in ranking enhancement.

1 M.S.Raghuram, 2 M.Thenmozhi 1 Research Scholar( M.Tech), 2 Assistant Professor, 1 Information Technology, Dept of Information Technology, SRM University, Chennai, India, Dept of Information Technology, SRM University, Chennai, India [email protected], [email protected] ________________________________________________________________________________________________________ Abstr...

A growing concern for organisations is how they should deal with increasing amounts of collected data. With fierce competition and smaller margins, organisations that are able to fully realize the potential in the data they collect can gain an advantage over the competitors. It is almost impossible to avoid imprecision when processing large amounts of data. Still, many of the available informat...

An important problem for the success of ontology-based applications is how to provide persistent storage and querying. For that purpose, many RDF tools capable of storing and querying over a knowledge base, have been proposed. Recently, fuzzy extensions to ontology languages have gained considerable attention especially due to their ability to handle vague information. In this paper we investig...