Abstract. This is a system description of the Description Logic reasoner FaCT++. The reasoner implements a tableaux decision procedure for the well known SHOIQ description logic, with additional support for datatypes, including strings and integers. The system employs a wide range of performance enhancing optimisations, including both standard techniques (such as absorption and model merging) and newly developed ones (such as ordering heuristics and taxonomic classification). FaCT++ can, via the standard DIG interface, be used to provide reasoning services for ontology engineering tools supporting the OWL DL ontology language. 1

We show how a tableaux algorithm for that include range and domain axioms, prove that the extended algorithm is still a decision concepts w.r.t. such a role box, and show how support for range and domian axioms can be exploited in order to add a new form of absorption optimisation called role absorption. We illustrate the effectiveness of the optimised algorithm by analysing the perfomance of our FaCT++ implementation when classifying terminologies derived from realistic ontologies. 1

OWL-DL is a World Wide Web Consortium standard for representing ontologies on the Semantic Web. It can be seen as a syntactic variant of the Description Logic SHOIN (D), with an OWL-DL ontology corresponding to a SHOIN (D) knowledge base. The very recent accomplishment of a decision procedure for SHOIN (D) poses the challenge of turning the decision procedure into a practical implementation. In particular, we emphasize the need of new optimization techniques for nominals, especially in the presence of large number of individuals in the KB. In this paper, we present new techniques for optimizing DL reasoning in the presence of nominals in the TBox and individuals in a large ABox. We have integrated our optimizations in the open-source Pellet reasoner, which is sound and complete for SHOIN (D), and found that they suffice for efficiently classifying the famous Wine Ontology. We also show that these optimization techniques produce significant performance improvements in other widely used ontologies containing nominals, such as the OWL-S and AKT ontologies.

For many years, the Modal Logic community has pursued various techniques for robustly combining logics. These methodologies reflect a new direction in Logic applied to Knowledge Representation, namely the direction toward constructing and investigating complex combined logical formalisms out of simpler ones. The E-Connections framework is a novel technique for combining Abstract Description Systems (ADSs), a generalization of several families of decidable logics, including Description Logics, Modal Logics, as well as many logics of time and space. In this paper, we investigate different E-Connection languages involving Description Logics. Recently, E-Connections of Description Logics have been proposed as a suitable formalism for various applications, such as ontology integration on the Semantic Web. We propose two novel combinations: one-way E-Connections and E-Connections allowing for transitive and symmetric link relations. Then, we investigate in depth the problem of reasoning with E-Connections and provide different tableau-based decision procedures. Finally, we show that our algorithms can be implemented as an extension of existing DL reasoners and present our prototype implementation in the Pellet system. To the best of our knowledge, the algorithms presented in this paper are the first practical decision procedures for E-Connections. 1

Abstract. The development of ontologies involves continuous but relatively small modifications. Existing ontology reasoners, however, do not take advantage of the similarities between different versions of an ontology. In this paper, we propose a technique for incremental reasoning—that is, reasoning that reuses information obtained from previous versions of an ontology—based on the notion of a module. Our technique does not depend on a particular reasoning calculus and thus can be used in combination with any reasoner. We have applied our results to incremental classification of OWL DL ontologies and found significant improvement over regular classification time on a set of real-world ontologies. 1

Abstract. We present an application – the Instance Store – aimed at solving some of the scalability problems that arise when reasoning with the large numbers of individuals envisaged in the semantic web. The approach uses well-known techniques for reducing description logic reasoning with individuals to reasoning with concepts. Crucial to the implementation is the combination of a description logic terminological reasoner with a traditional relational database. The resulting form of inference, although specialised, is sound and complete and sufficient for several interesting applications. Most importantly, the application scales to sizes (over 100,000s individuals) where all other existing applications fail. This claim is substantiated by a detailed empirical evaluation of the Instance Store in contrast with existing alternative approaches.

In this paper, we investigate incrementally updating classifications of ontologies encoded in the expressive description logic SHOIN (D), which also corresponds to the W3C standard Web Ontology Language, OWL-DL. In particular we present various optimizations for incremental classification under both axiom addition and removal. Several of the optimizations are independent of the logic and proof theory as long as classification can be reduced to satisfiability testing. Additionally, we provide an empirical analysis of the optimizations through an experimental implementation in the Pellet OWL-DL reasoner. Keywords: Description Logics, Information Change, Ontology, Classification 1.

Abstract. Various data sources on the Web tend to be highly dynamic; this is evident in prominent Web services frameworks in which devices register or deregister their descriptions quite rapidly and in Semantic Web portals which allow content authors to modify or extend the ontologies and submit content. However, current Description Logic (DL) reasoners have only been considered for relatively static knowledge bases. This work aims to provide more efficient DL reasoning techniques for frequently changing instance bases (ABoxes). More specifically, we investigate the process of incrementally updating the tableau completion graphs used for reasoning in the expressive DLs SHOQ and SHIQ, which correspond to a large subset of the W3C standard Web Ontology Language, OWL-DL. We present an algorithm for updating completion graphs under the syntactic addition and removal of ABox assertions. We also provide an empirical analysis of the approach through an implementation in the Pellet OWL-DL reasoner. 1

Abstract. Tableau algorithms are currently the most widely-used and empirically the fastest algorithms for reasoning in expressive Description Logics, including the important Description Logics SHIQ and SHOIQ. Achieving a high level of performance on terminological reasoning in expressive Description Logics when using tableau-based algorithms requires the incorporation of a wide variety of optimisations. The Description Logic system FaCT++ implements a wide variety of such optimisations, some present in other reasoners and some novel or refined in FaCT++.

Syndication on the Web has attracted a great amount of attention in recent years. However, today’s state-of-the-art syndication approaches still provide relatively weak ex-pressive power from a modeling perspective and provide very little automated reasoning support. If a more expressive approach with a formal semantics can be provided, many benefits can be achieved, including a rich semantics-based mechanism for expressing sub-scriptions and published content and automated reasoning for discovering subscription matches not found using traditional syntactic syndication approaches. In this dissertation, I develop a syndication framework based on the Web Ontology Language (OWL), which is the standardized language for representing the semantics of information on the Web. One of the main advantages of the framework is its support for formal reasoning, as the semantics of subsets of OWL are founded in description logic (a decidable fragment of first-order logic). Therefore, the previously mentioned benefits can be achieved using description logic (DL) reasoning. However, the main limitation in using OWL as the underlying representation model is related to the overhead of DL reasoning under changing data, which makes the approach