Jeff Z. Pan

Resource Description Framework

This chapter introduces Resource Description Framework (RDF), the W3C recommendation for semantic annotations in the Semantic Web. It will cover the syntax and semantics of RDF, as well as its relation with the W3C OWL Web Ontology Language. To address the mismatch between RDF and OWL-DL, the most expressive decidable fragment of the OWL standard, we introduce a novel variant of RDF(S), called RDFS-FA, which provides a solid semantic foundation for many of the latest Description Logic-based SW ontology languages, such as OWL-DL and OWL2-DL.

An Argument-Based Approach to Using Multiple Ontologies

Logic-based argumentation offers an approach to querying and revising multiple ontologies that are inconsistent or incoherent. A common assumption for logic-based argumentation is that an argument is a pair ****** ,*** *** where *** is a minimal subset of the knowledgebase such that *** is consistent and *** entails the claim *** . Using dialogue games, agents (each with its own ontology) can exchange arguments and counterarguments concerning formulae of interest. In this paper, we present a novel framework for logic-based argumentation with ontological knowledge. As far as we know, this is the first proposal for argumentation with multiple ontologies via dialogues. It allows two agents to discuss the answer to queries concerning their knowledge (even if it is inconsistent) without one agent having to copy all of their ontology to the other, and without the other agent having to expend time and effort merging that ontology with theirs. Furthermore, it offers the potential for the agents to incrementally improve their knowledge based on the dialogue by checking how it differs from the other agents.

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.

Verifying feature models using OWL

Feature models are widely used in domain engineering to capture common and variant features among systems in a particular domain. However, the lack of a formal semantics and reasoning support of feature models has hindered the development of this area. Industrial experiences also show that methods and tools that can support feature model analysis are badly appreciated. Such reasoning tool should be fully automated and efficient. At the same time, the reasoning tool should scale up well since it may need to handle hundreds or even thousands of features a that modern software systems may have. This paper presents an approach to modeling and verifying feature diagrams using Semantic Web OWL ontologies. We use OWL DL ontologies to precisely capture the inter-relationships among the features in a feature diagram. OWL reasoning engines such as FaCT++ are deployed to check for the inconsistencies of feature configurations fully automatically. Furthermore, a general OWL debugger has been developed to tackle the disadvantage of lacking debugging aids for the current OWL reasoner and to complement our verification approach. We also developed a CASE tool to facilitate visual development, interchange and reasoning of feature diagrams in the Semantic Web environment.

TrOWL: tractable OWL 2 reasoning infrastructure

The Semantic Web movement has led to the publication of thousands of ontologies online. These ontologies present and mediate information and knowledge on the Semantic Web. Tools exist to reason over these ontologies and to answer queries over them, but there are no large scale infrastructures for storing, reasoning, and querying ontologies on a scale that would be useful for a large enterprise or research institution. We present the TrOWL infrastructure for transforming, reasoning, and querying OWL2 ontologies which uses novel techniques such as Quality Guaranteed Approximations and Forgetting to achieve this goal.

Fuzzy extensions of OWL: Logical properties and reduction to fuzzy description logics

The Semantic Web is an extension of the current web, where information would have precisely defined meaning, based on knowledge representation languages. The current W3C standard for representing knowledge is the Web Ontology Language (OWL). OWL is based on Description Logics which is a popular knowledge representation formalism. Although, DLs are quire expressive they feature limitations with respect to what can be said about vague knowledge, which appears in several applications. Consequently, fuzzy extensions to OWL and DLs have gained considerable attention. In the current paper we study fuzzy extensions of the Semantic Web language OWL. First, we present the (abstract) syntax and semantics of a rather elementary fuzzy extension of OWL creating fuzzy OWL (f-OWL). More importantly we use this extension to provide an investigation on the semantics of several f-OWL axioms and more precisely for those which, in classical DLs, can be expressed in different but equivalent ways. Moreover, we present a translation method which reduces inference problems of f-OWL into inference problems of expressive fuzzy Description Logics, in order to provide reasoning support through fuzzy DLs. Finally, we present two further fuzzy extensions of OWL based on fuzzy subsumption and fuzzy nominals.

Extending description logics with uncertainty reasoning in possibilistic logic

Possibilistic logic provides a convenient tool for dealing with uncertainty and handling inconsistency. In this paper, we propose possibilistic description logics as an extension of description logics, which are a family of well‐known ontology languages. We first give the syntax and semantics of possibilistic description logics and define several inference services in possibilistic description logics. We show that these inference serviced can be reduced to the task of computing the inconsistency degree of a knowledge base in possibilistic description logics. Since possibilistic inference services suffer from the drowning problem, that is, axioms whose confidence degrees are less than or equal to the inconsistency are not used, we consider a drowning‐free variant of possibilistic inference, called linear order inference. We propose an algorithm for computing the inconsistency degree of a possibilistic description logic knowledge base and an algorithm for the linear order inference. We consider the impact of our possibilistic description logics on ontology learning and ontology merging. Finally, we implement these algorithms and provide some interesting evaluation results.

Scalable querying services over fuzzy ontologies

Fuzzy ontologies are envisioned to be useful in the Semantic Web. Existing fuzzy ontology reasoners are not scalable enough to handle the scale of data that the Web provides. In this paper, we propose a framework of fuzzy query languages for fuzzy ontologies, and present query answering algorithms for these query languages over fuzzy DL-Lite ontologies. Moreover, this paper reports on implementation of our approach in the fuzzy DL-Lite query engine in the ONTOSEARCH2 system and preliminary, but encouraging, benchmarking results. To the best of our knowledge, this is the first ever scalable query engine for fuzzy ontologies.

A Flexible Ontology Reasoning Architecture for the Semantic Web

Knowledge-based systems in the semantic Web era can make use of the power of the semantic Web languages and technologies, in particular those related to ontologies. Recent research has shown that user-defined data types are very useful for semantic Web and ontology applications. The W3C semantic Web best practices and development working group has set up a task force to address this issue. Very recently, OWL-Eu and OWL-E, two decidable extensions of the W3C standard ontology language OWL DL, have been proposed to support customized data types and customized data type predicates, respectively. In this paper, we propose a flexible reasoning architecture for these two expressive semantic Web ontology languages and describe our prototype implementation of the reasoning architecture, based on the well-known FaCT DL reasoner, which witnesses the two key flexibility features of our proposed architecture: 1) It allows users to define their own data types and data type predicates based on built-in ones and 2) new data type reasoners can be added into the architecture without having to change the concept reasoner

OWL-Eu: Adding customised datatypes into OWL

Although OWL is rather expressive, it has a very serious limitation on datatypes; i.e., it does not support customised datatypes. It has been pointed out that many potential users will not adopt OWL unless this limitation is overcome, and the W3C Semantic Web Best Practices and Development Working Group has set up a task force to address this issue. This paper makes the following two contributions: (i) it provides a brief summary of OWL-related datatype formalisms, and (ii) it provides a decidable extension of OWL DL, called OWL-Eu, that supports customised datatypes. A detailed proof of the decidability of OWL-Eu is presented.