316 research outputs found
Reasoning with Very Expressive Fuzzy Description Logics
It is widely recognized today that the management of imprecision and
vagueness will yield more intelligent and realistic knowledge-based
applications. Description Logics (DLs) are a family of knowledge representation
languages that have gained considerable attention the last decade, mainly due
to their decidability and the existence of empirically high performance of
reasoning algorithms. In this paper, we extend the well known fuzzy ALC DL to
the fuzzy SHIN DL, which extends the fuzzy ALC DL with transitive role axioms
(S), inverse roles (I), role hierarchies (H) and number restrictions (N). We
illustrate why transitive role axioms are difficult to handle in the presence
of fuzzy interpretations and how to handle them properly. Then we extend these
results by adding role hierarchies and finally number restrictions. The main
contributions of the paper are the decidability proof of the fuzzy DL languages
fuzzy-SI and fuzzy-SHIN, as well as decision procedures for the knowledge base
satisfiability problem of the fuzzy-SI and fuzzy-SHIN
Tractable approximate deduction for OWL
Acknowledgements This work has been partially supported by the European project Marrying Ontologies and Software Technologies (EU ICT2008-216691), the European project Knowledge Driven Data Exploitation (EU FP7/IAPP2011-286348), the UK EPSRC project WhatIf (EP/J014354/1). The authors thank Prof. Ian Horrocks and Dr. Giorgos Stoilos for their helpful discussion on role subsumptions. The authors thank Rafael S. Gonçalves et al. for providing their hotspots ontologies. The authors also thank BoC-group for providing their ADOxx Metamodelling ontologies.Peer reviewedPostprin
Optimizing Description Logic Reasoning for the Service Matchmaking and Composition
The Semantic Web is a recent initiative to expose semantically rich information associated with Web resources to build more intelligent Web-based systems. Recently, several projects have embraced this vision and there are several successful applications that combine the strengths of the Web and of semantic technologies. However, Semantic Web still lacks a technology, which would provide the needed scalability and integration with existing infrastructure. In this paper we present our ongoing work on a Semantic Web repository, which is capable of addressing complex schemas and answer queries over ontologies with large number of instances. We present the details of our approach and describe the underlying architecture of the system. We conclude with a performance evaluation, which compares the current state-of-the-art reasoners with our system
On the Computation of Common Subsumers in Description Logics
Description logics (DL) knowledge bases are often build by users with expertise in the application domain, but little expertise in logic. To support this kind of users when building their knowledge bases a number of extension methods have been proposed to provide the user with concept descriptions as a starting point for new concept definitions. The inference service central to several of these approaches is the computation of (least) common subsumers of concept descriptions. In case disjunction of concepts can be expressed in the DL under consideration, the least common subsumer (lcs) is just the disjunction of the input concepts. Such a trivial lcs is of little use as a starting point for a new concept definition to be edited by the user. To address this problem we propose two approaches to obtain "meaningful" common subsumers in the presence of disjunction tailored to two different methods to extend DL knowledge bases. More precisely, we devise computation methods for the approximation-based approach and the customization of DL knowledge bases, extend these methods to DLs with number restrictions and discuss their efficient implementation
Ecient Inference-aware RDB2RDF Query Rewriting
RDB2RDF systems generate RDF from relational databases,
operating in two dierent manners: materializing the database content into RDF or acting as virtual RDF datastores that transform SPARQL queries into SQL. In the former, inferences on the RDF data (taking into account the ontologies that they are related to) are normally done by the RDF triple store where the RDF data is materialised and hence the
results of the query answering process depend on the store. In the latter, existing RDB2RDF systems do not normally perform such inferences at query time. This paper shows how the algorithm used in the REQUIEM system, focused on handling run-time inferences for query answering, can
be adapted to handle such inferences for query answering in combination with RDB2RDF systems
Corporate Smart Content Evaluation
Nowadays, a wide range of information sources are available due to the
evolution of web and collection of data. Plenty of these information are
consumable and usable by humans but not understandable and processable by
machines. Some data may be directly accessible in web pages or via data feeds,
but most of the meaningful existing data is hidden within deep web databases
and enterprise information systems. Besides the inability to access a wide
range of data, manual processing by humans is effortful, error-prone and not
contemporary any more. Semantic web technologies deliver capabilities for
machine-readable, exchangeable content and metadata for automatic processing
of content. The enrichment of heterogeneous data with background knowledge
described in ontologies induces re-usability and supports automatic processing
of data. The establishment of “Corporate Smart Content” (CSC) - semantically
enriched data with high information content with sufficient benefits in
economic areas - is the main focus of this study. We describe three actual
research areas in the field of CSC concerning scenarios and datasets
applicable for corporate applications, algorithms and research. Aspect-
oriented Ontology Development advances modular ontology development and
partial reuse of existing ontological knowledge. Complex Entity Recognition
enhances traditional entity recognition techniques to recognize clusters of
related textual information about entities. Semantic Pattern Mining combines
semantic web technologies with pattern learning to mine for complex models by
attaching background knowledge. This study introduces the afore-mentioned
topics by analyzing applicable scenarios with economic and industrial focus,
as well as research emphasis. Furthermore, a collection of existing datasets
for the given areas of interest is presented and evaluated. The target
audience includes researchers and developers of CSC technologies - people
interested in semantic web features, ontology development, automation,
extracting and mining valuable information in corporate environments. The aim
of this study is to provide a comprehensive and broad overview over the three
topics, give assistance for decision making in interesting scenarios and
choosing practical datasets for evaluating custom problem statements. Detailed
descriptions about attributes and metadata of the datasets should serve as
starting point for individual ideas and approaches
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