Showing papers by "Ian Horrocks published in 2009"

Hypertableau reasoning for description logics

Abstract: We present a novel reasoning calculus for the description logic SHOIQ+--a knowledge representation formalism with applications in areas such as the SemanticWeb. Unnecessary nondeterminism and the construction of large models are two primary sources of inefficiency in the tableau-based reasoning calculi used in state-of-the-art reasoners. In order to reduce nondeterminism, we base our calculus on hypertableau and hyperresolution calculi, which we extend with a blocking condition to ensure termination. In order to reduce the size of the constructed models, we introduce anywhere pairwise blocking. We also present an improved nominal introduction rule that ensures termination in the presence of nominals, inverse roles, and number restrictions--a combination of DL constructs that has proven notoriously difficult to handle. Our implementation shows significant performance improvements over state-of-the-art reasoners on several well-known ontologies.

Ontology Integration Using Mappings: Towards Getting the Right Logical Consequences

Abstract: We propose a general method and novel algorithmic techniques to facilitate the integration of independently developed ontologies using mappings. Our method and techniques aim at helping users understand and evaluate the semantic consequences of the integration, as well as to detect and fix potential errors. We also present ContentMap, a system that implements our approach, and a preliminary evaluation which suggests that our approach is both useful and feasible in practice.

Efficient Query Answering for OWL 2

Abstract: The QL profile of OWL 2 has been designed so that it is possible to use database technology for query answering via query rewriting. We present a comparison of our resolution based rewriting algorithm with the standard algorithm proposed by Calvanese et al., implementing both and conducting an empirical evaluation using ontologies and queries derived from realistic applications. The results indicate that our algorithm produces significantly smaller rewritings in most cases, which could be important for practicality in realistic applications.

OWL 2 Web Ontology Language Mapping to RDF Graphs

Abstract: The OWL 2 Web Ontology Language, informally OWL 2, is an ontology language for the Semantic Web with formally defined meaning. OWL 2 ontologies provide classes, properties, individuals, and data values and are stored as Semantic Web documents. OWL 2 ontologies can be used along with information written in RDF, and OWL 2 ontologies themselves are primarily exchanged as RDF documents. The OWL 2 Document Overview describes the overall state of OWL 2, and should be read before other OWL 2 documents. This document defines the mapping of OWL 2 ontologies into RDF graphs, and vice versa.

Extracting Modules from Ontologies: A Logic-Based Approach

Abstract: The ability to extract meaningful fragments from an ontology is essential for ontology reuse. We propose a definition of a module that guarantees to completely capture the meaning of a given set of terms, i.e., to include all axioms relevant to the meaning of these terms. We show that the problem of determining whether a subset of an ontology is a module for a given vocabulary is undecidable even for OWL DL. Given these negative results, we propose sufficient conditions for a for a fragment of an ontology to be a module. We propose an algorithm for computing modules based on those conditions and present our experimental results on a set of real-world ontologies of varying size and complexity.

Exploiting Partial Information in Taxonomy Construction

Abstract: One of the core services provided by OWL reasoners is classification : the discovery of all subclass relationships between class names occurring in an ontology. Discovering these relations can be computationally expensive, particularly if individual subsumption tests are costly or if the number of class names is large. We present a classification algorithm which exploits partial information about subclass relationships to reduce both the number of individual tests and the cost of working with large ontologies. We also describe techniques for extracting such partial information from existing reasoners. Empirical results from a prototypical implementation demonstrate substantial performance improvements compared to existing algorithms and implementations.

Building Ontologies Collaboratively Using ContentCVS

Abstract: OWL Ontologies are already being used in many application domains. In particular, OWL is extensively used in the clinical sciences; prominent examples of OWL ontologies are the National Cancer Institute (NCI) Thesaurus, SNOMED CT, the Gene Ontology (GO), the Foundational Model of Anatomy (FMA), and GALEN. These ontologies are large and complex; for example, SNOMED currently describes more than 350.000 concepts whereas NCI and GALEN describe around 50.000 concepts. Furthermore, these ontologies are in continuous evolution; for example the developers of NCI and GO perform approximately 350 additions of new entities and 25 deletions of obsolete entities each month [1]. Most realistic ontologies, including the ones just mentioned, are being developed collaboratively. The developers of an ontology can be geographically distributed and may contribute in different ways and to different extents. Maintaining such large ontologies in a collaborative way is a highly complex process, which involves tracking and managing the frequent changes to the ontology, reconciling conflicting views of the domain from different developers, minimising the introduction of errors (e.g., ensuring that the ontology does not have unintended logical consequences), and so on. In this setting, developers need to regularly merge and reconcile their modifications to ensure that the ontology captures a consistent unified view of the domain. Changes performed by different users may, however, conflict in complex ways and lead to errors. These errors may manifest themselves both as structural (i.e., syntactic) mismatches between developers’ ontological descriptions, and as unintended logical consequences. Tools supporting collaboration should therefore provide means for: (i) keeping track of ontology versions and changes and reverting, if necessary, to a previously agreed upon version, (ii) comparing potentially conflicting versions and identifying conflicting parts, (iii) identifying errors in the reconciled ontology constructed from conflicting versions, and (iv) suggesting possible ways to repair the identified errors with a minimal impact on the ontology. In order to address (i), we propose to adapt the Concurrent Versioning— a successful paradigm in collaborative software development— to allow several developers to make changes concurrently to the same ontology (see Section 2). To address (ii) we propose a notion of conflict between ontology versions and provide means for identifying conflicting parts based on it (see Section 3). To address (iii) we propose in Section

Practical aspects of query rewriting for OWL 2

Abstract: Query answering for the QL profile of OWL 2 and a substantial fragment of the EL profile can be implemented via query rewriting. In our previous work, we presented RQR--a rewriting algorithm for OWL QL that can also deal with most of the EL profile. In order to test the likely practicality of RQR, we have implemented it in a query rewriting system that we call REQUIEM. A recent empirical evaluation of REQUIEM, in which we considered OWL 2 QL ontologies, indicates that it produces significantly smaller rewritings than existing approaches in most cases. However, our results suggest that typical queries over realistic ontologies can still lead to very large rewritings (e.g., containing many thousands of queries). In this paper, we describe query rewriting, briefly present the results of our empirical evaluation, and discuss various optimization techniques aimed at reducing the size of the rewritings. We additionally discuss the consequences of rewriting queries w.r.t. OWL 2 EL ontologies and present results from a preliminary empirical evaluation of REQUIEM in which we consider realistic OWL 2 EL ontologies.

Towards a Logic−based Assessment of the compatibility of UMLS sources

Abstract: The UMLS Metathesaurus (UMLS-Meta) is currently the most comprehensive effort for integrating independently-developed medical thesauri and ontologies. The techniques used in the construction of UMLS-Meta are mostly based on lexical matching and often disregard the semantics of the sources being integrated. In this paper we aim at developing logic-based techniques to automatically detect and fix potential errors in UMLS-Meta. Our research is currently at an early stage, so we only present here our preliminary ideas and experimental results.

Logic-based Ontology Integration using ContentMap.

Abstract: We present ContentMap, a system that uses a general method and novel algorithmic techniques to facilitate the integration of independently developed ontologies using mappings. Our method and techniques aim at helping users understand and evaluate the semantic consequences of the integration, as well as to detect and fix potential errors.

ContentCVS: A CVS-based Collaborative ONTology ENgineering Tool.

Abstract: We present ContentCVS, a system that implements a novel approach to facilitate the collaborative development of ontologies. Our approach adapts Concurrent Versioning, a successful paradigm in collaborative software development, to allow several developers to make changes concurrently to an ontology. Conflict detection and resolution are based on novel techniques that take into account the structure and semantics of the ontology versions to be reconciled by using precisely-defined notions of structural and semantic differences between ontologies and by extending existing ontology debugging and repair techniques.

Ontology-Based Information Systems

Abstract: : An ontology is model of (some aspect of) the world - it introduces vocabulary relevant to the domain and specifies the meaning (or semantics) of this vocabulary. For example, an anatomy ontology might introduce terms for kinds of object, such as Heart, Vein, Artery, and so on, as well as terms for relationships between objects, such as isPartOf, isConnectedTo, isCausedBy, and so on. The meaning of each term is specified by combining other terms using some formal language, typically a logic.