Abstract. Modularity in ontologies is key both for large scale ontology development and for distributed ontology reuse on the Web. In this paper, we address the problem of determining and retrieving the subset of an ontology that captures the essential meaning of a given entity in the ontology. However, even defining what makes a certain set of axioms a relevant subset of an ontology for a certain task is a controversial issue. In this paper, we provide such a definition by introducing the notion of semantic encapsulation of an entity within an ontology. Such a notion will motivate a formal definition of module. We then provide an algorithm for finding and retrieving the module that encapsulates the meaning of each entity in a given ontology, an optimized implementation and some promising empirical results. 1

The Semantic Web lacks support for explaining answers from web applications. When applications return answers, many users do not know what information sources were used, when they were updated, how reliable the source was, or what information was looked up versus derived. Many users also do not know how implicit answers were derived. The Inference Web (IW) aims to take opaque query answers and make the answers more transparent by providing infrastructure for presenting and managing explanations. The explanations include information concerning where answers came from (knowledge provenance) and how they were derived (or retrieved). In this article we describe an infrastructure for IW explanations. The infrastructure includes: IWBase – an extensible web-based registry containing details about information sources, reasoners, languages, and rewrite rules; PML – the Proof Markup Language specification and API used for encoding portable proofs; IW browser – a tool supporting navigation and presentations of proofs and their explanations; and a new explanation dialogue component. Source information in the IWBase is used to convey knowledge provenance. Representation and reasoning language axioms and rewrite rules in the IWBase are used to support proofs, proof combination, and Semantic Web agent interoperability. The Inference Web is in use by four Semantic Web agents, three of them using embedded reasoning engines fully registered in the IW. Inference Web also provides explanation infrastructure for a number of DARPA and ARDA projects.

With the advent of Semantic Web languages such as OWL (Web Ontology Language), the expressive Description Logic SHOIN is exposed to a wider audience of ontology users and developers. As an increasingly large number of OWL ontologies become available on the Semantic Web and the descriptions in the ontologies become more complicated, finding the cause of errors becomes an extremely hard task even for experts. The problem is worse for newcomers to OWL who have little or no experience with DL-based knowledge representation. Existing ontology development environments, in conjunction with a reasoner, provide some limited debugging support, however this is restricted to merely reporting errors in the ontology, whereas bug diagnosis and resolution is usually left to the user. In this thesis, I present a complete end-to-end framework for explaining, pinpointing and repairing semantic defects in OWL-DL ontologies (or in other words, a SHOIN knowledge base). Semantic defects are logical contradictions that manifest as either inconsistent ontologies or unsatisfiable concepts. Where possible, I show extensions to handle related defects such as unsatisfiable roles, unintended entailments and nonentailments,

Abstract. OntoTrack is a new browsing and editing “in-one-view” ontology authoring tool that combines a hierarchical graphical layout and instant reasoning feedback for (the most rational fraction of) OWL Lite. OntoTrack provides an animated and zoomable view with context sensitive features like click-able miniature branches or selective detail views together with drag-and-drop editing. Each editing step is instantly synchronized with an external reasoner in order to provide appropriate graphical feedback about relevant modeling consequences. The most recent feature of OntoTrack is an on demand textual explanation for subsumption and equivalence between or unsatisfiability of classes. This paper describes the key features of the current implementation and discusses future work as well as some development issues. 1

Abstract. Description Logics (DLs) are a family of class based knowledge representation formalisms characterised by the use of various constructors to build complex classes from simpler ones, and by an emphasis on the provision of sound, complete and (empirically) tractable reasoning services. They have a range of applications, but are mostly widely known as the basis for ontology languages such as OWL. The increasing use of DL based ontologies in areas such as e-Science and the Semantic Web is, however, already stretching the capabilities of existing DL systems, and brings with it a range of challenges for future research. 1

Managing semantic heterogeneity is a complex task. One solution involves matching like terms to each other. We view Match as an operator that takes two graph-like structures (e.g., concept hierarchies or ontologies) and returns a mapping between the nodes of the graphs that correspond semantically to each other. State of the art matching systems (e.g., COMA, Cupid) perform well for many real world applications. However, matching systems may produce mappings that may not be intuitively obvious to human users. Moreover, there are cases where matching systems do not produce a useful mapping. In order for users to trust the mappings (and thus use them), they need to understand them. Also, if a system does not provide a mapping or provides a partial mapping, users need to understand answers so that they can understand either why a mapping was not produced or why only a partial answer was produced. In this paper we describe how matching systems can explain their answers using the Inference Web (IW) infrastructure. There, S-Match, a semantic matching system, produces proofs for mappings it has discovered.

Social network analysis (SNA) has become a mature scientific field over the last 50 years and is now an area with massive commercial appeal and renewed research interest. In this paper, we argue that new methods for collecting social nework strucuture, and the shift in scale of these networks, introduces a greater degree of imprecision that requires rethinking on how SNA techniques can be applied. We discuss a new area in data management, probabilistic databases, whose main research goal is to provide tools to manage and manipulate imprecise or uncertain data. We outline the application building blocks necessary to build a large scale social networking application and the extent to which current research in probabilisitc databases addresses these challenges.

OntoTrack is an ontology authoring tool that combines a graph-based hierarchical layout and instant reasoning feedback within one single view. Currently OntoTrack can handle ontologies with an expressivity almost comparable to OWL Lite. The graphical representation provides an animated and zoomable subsumption graph with context sensitive features such as click-able miniature branches or selective detail views, together with drag-and-drop editing. Each editing step is instantly synchronised with an external reasoner in order to provide appropriate graphical feedback about relevant modelling consequences. A recent extention of OntoTrack provides an on-demand textual explanation for subsumption relationships between classes. This paper describes the key features of the current implementation and discusses future work, as well as some development issues. OntoTrack can be downloaded at

In order for agents and humans to leverage the growing wealth of heterogeneous information and services on the web, increasingly, they need to understand the information that is delivered to them. In the simplest case, an agent or human is retrieving "look-up" information and would benefit from having access to provenance information concerning recency, source authoritariveness, etc. In more complicated situations where information is manipulated before it is returned as an answer, agents and humans would benefit from understanding the derivations and assumptions used. When services are involved, users and agents also would benefit from understanding what actions could be or were executed on the user's behalf. In this paper, we introduce a strategy for registering information sources and question answering systems providing support for implementing distributed and cooperative web services. In this paper, we describe the inference web infrastructure that supports explanations in distributed environments such as the web and describe the elements of its registry.

The Semantic Web lacks support for explaining knowledge provenance.