[show abstract][hide abstract] ABSTRACT: This chapter reports about the software process guidance in ontology-driven software development (ODSD), one of the core ontology-enabled services of the ODSD environments. Ontology-driven software process guidance amounts to a significant step forward in software engineering in general (cf. Fig. 1.1 on p. 3). Its role is to guide developers through a complex software development process by providing information about the consistency of artefacts and about the tasks to be accomplished to reach a particular development goal.
[show abstract][hide abstract] ABSTRACT: Processes in software development generally have two facets. They can be model objects, as described in Sect. 4.2, and also workflows, as described in Sect. 4.3. In this chapter, we analyse typical problems in process modelling and develop ontology reasoning technologies to address them in the ODSD infrastructure. We show how different ontological representation of process models can be constructed for different purposes and how reasoning can be applied to guarantee the consistency of models.
[show abstract][hide abstract] ABSTRACT: Requirements Engineering (RE) is essential to a software
project. As the result of RE process, software Requirement Speci�ca-
tions (SRS) should be consistent, correct and complete. However, the
acquisition, speci�cation and evolution of requirements from dierent
stakeholders or sources usually leads to incomplete, ambiguous, and faulty
requirements. This may become an incalculable risk for the whole project
and a disaster for the �nal software product. In this paper we present a
method to improve the quality of a SRS semi-automatically. Facilitated
by ontology reasoning techniques, we describe how to detect and repair
faulty information in the SRS. Furthermore, we also provide various
metrics to measure the quality of the SRS at any time during the RE
process. Finally, we generalize our approach to be applicable for any
information captured in an ontology.
Proceedings of the 8th International Workshop on Semantic Web Enabled Software Engineering (SWESE 2012; 12/2012
[show abstract][hide abstract] ABSTRACT: A matchmaking system for finding renting houses is required as the housing problem becomes serious in China and many people resort to rent a house. A semantic approach based on abductive conjunctive query answering (CQA) in Description Logic ontologies is exploited to provide more matches for a request about renting houses. Moreover, a matchmaking system based on this approach is developed. This demo will guide users to find suitable renting houses using this matchmaking system and show the advantages of the system.
[show abstract][hide abstract] ABSTRACT: In this paper, we describe the Knowledge Organisation System Implicit Mapping (KOSIMap) framework, which differs from existing ontology mapping approaches by using description logic reasoning (i) to extract implicit information for every entity, and (ii) to remove inappropriate mappings from an alignment.
Proceedings of the 6th International Workshop on Ontology Matching, Bonn, Germany, October 24, 2011; 01/2011
[show abstract][hide abstract] ABSTRACT: So far researchers in the Description Logics / Ontology communities mainly consider ontology reasoning services for static ontologies. The rapid development of the Semantic Web and its emerging data ask for reasoning technologies for dynamic knowledge streams. Existing work on stream reasoning is focused on lightweight languages such as RDF and RDFS. In this paper, we introduce the notion of Ontology Stream Management System (OSMS) and present a stream-reasoning approach based on Truth Maintenance System (TMS). We present optimised EL++ algorithm to reduce memory consumption. Our evaluations show that the optimisation improves TMS-enabled EL++ reasoning to deal with relatively large volumes of data and update efficiently.
Proceedings of the 20th ACM Conference on Information and Knowledge Management, CIKM 2011, Glasgow, United Kingdom, October 24-28, 2011; 01/2011
[show abstract][hide abstract] ABSTRACT: Computing all diagnoses of an inconsistent ontology is important in ontology-based applications. However, the number of diagnoses can be very large. It is impractical to enumerate all diagnoses before identifying the target one to render the ontology consistent. Hence, we propose to represent all diagnoses by multiple sets of partial diagnoses, where the total number of partial diagnoses can be small and the target diagnosis can be directly retrieved from these partial diagnoses. We also propose methods for computing the new representation of all diagnoses in an OWL DL ontology. Experimental results show that computing the new representation of all diagnoses is much easier than directly computing all diagnoses.
IEEE 23rd International Conference on Tools with Artificial Intelligence, ICTAI 2011, Boca Raton, FL, USA, November 7-9, 2011; 01/2011
[show abstract][hide abstract] ABSTRACT: The goal of the Scalable OWL 2 Reasoning for Linked Data lecture is twofold: first, to introduce scalable reasoning and querying
techniques to Semantic Web researchers as powerful tools to make use of Linked Data and large-scale ontologies, and second,
to present interesting research problems for the Semantic Web that arise in dealing with TBox and ABox reasoning in OWL 2.
The lecture consists of three parts. The first part will begin with an introduction and motivation for reasoning over Linked
Data, including a survey of the use of RDFS and OWL on the Web. The second part will present a scalable, distributed reasoning
service for instance data, applying a custom subset of OWL 2 RL/RDF rules (based on a tractable fragment of OWL 2). The third
part will present recent work on faithful approximate reasoning for OWL 2 DL. The lecture will include our implementation
of the mentioned techniques as well as their evaluations. These notes provide complimentary reference material for the lecture,
and follow the three-part structure and content of the lecture.
Reasoning Web. Semantic Technologies for the Web of Data - 7th International Summer School 2011, Galway, Ireland, August 23-27, 2011, Tutorial Lectures; 01/2011
[show abstract][hide abstract] ABSTRACT: In this paper, we discuss optimisations of rule-based materialisation approaches for reasoning over large static RDF datasets.
We generalise and re-formalise what we call the “partial-indexing” approach to scalable rule-based materialisation: the approach
is based on a separation of terminological data, which has been shown in previous and related works to enable highly scalable
and distributable reasoning for specific rulesets; in so doing, we provide some completeness propositions with respect to
semi-naïve evaluation. We then show how related work on template rules – T-Box-specific dynamic rulesets created by binding
the terminological patterns in the static ruleset – can be incorporated and optimised for the partial-indexing approach. We
evaluate our methods using LUBM(10) for RDFS, pD* (OWL Horst) and OWL 2 RL, and thereafter demonstrate pragmatic distributed
reasoning over 1.12 billion Linked Data statements for a subset of OWL 2 RL/RDF rules we argue to be suitable for Web reasoning.
[show abstract][hide abstract] ABSTRACT: Uncertainty reasoning and inconsistency handling are two important problems that often occur in the applications of the Semantic
Web. Possibilistic description logics provide a flexible framework for representing and reasoning with ontologies where uncertain
and/or inconsistent information exists. Based on our previous work, we develop a possibilistic description logic reasoner.
Our demo will illustrate functionalities of our reasoner for various reasoning tasks that possibilistic description logics
The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference, ESWC 2010, Heraklion, Crete, Greece, May 30 - June 3, 2010, Proceedings, Part II; 01/2010
[show abstract][hide abstract] ABSTRACT: To support the reuse and combination of ontologies in Semantic Web applications, it is often necessary to obtain smaller ontologies from existing larger ontologies. In particular, applications may require the omission of certain terms, e. g., concept names and role names, from an ontology. However, the task of omitting terms from an ontology is challenging because the omission of some terms may affect the relationships between the remaining terms in complex ways. We present the first solution to the problem of omitting concepts and roles from knowledge bases of description logics (DLs) by adapting the technique of forgetting, previously used in other domains. Specifically, we first introduce a model-theoretic definition of forgetting for knowledge bases (both TBoxes and ABoxes) in DL-Lite
, which is a non-trivial adaption of the standard definition for classical logic, and show that our model-based forgetting satisfies all major criteria of a rational forgetting operator, which in turn verifies the suitability of our model-based forgetting. We then introduce algorithms that implement forgetting operations in DL-Lite knowledge bases. We prove that the algorithms are correct with respect to the semantic definition of forgetting. We establish a general framework for defining and comparing different definitions of forgetting by introducing a parameterized family of forgetting operators called query-based forgetting operators. In this framework we identify three specific query-based forgetting operators and show that they form a hierarchy. In particular, we show that the model-based forgetting coincides with one of these query-based forgetting operators.
Annals of Mathematics and Artificial Intelligence 01/2010; 58:117-151. · 0.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: This paper describes the problem of doing description logic (DL) reasoning with partially closed world. The issue was addressed by extending the syntax of DL SROIQ with an NBox, which specifies the predicates to close, extending the semantics with the idea of negation as failure, reducing the closed world reasoning to incremental reasoning on classical DL ontologies, and applying the syntactic approximation technology to improve the reasoning performance. Compared with the existing DBox approach, which corresponds to the relation database, the NBox approach supports deduction on closed concepts and roles. Also, the approximate reasoning can reduce reasoning complexity from N2EXPTIME-complete to PTIME-complete while preserving the correctness of reasoning for ontologies with certain properties.
[show abstract][hide abstract] ABSTRACT: Large scale semantic web applications require e cient and robust description logic (DL) reasoning services. In this pa- per, we present a soundness preserving tractable approxima- tive reasoning approach for TBox reasoning inR, a fragment of OWL2-DL supportingALC GCIs and role chains with 2Ex- pTime-hard complexity. We first rewrite the ontologies into EL+ with an additional complement table maintaining the complementary relations between named concepts, and then classify the approximation. Preliminary evaluation shows that our approach can classify existing benchmarks in large scale e ciently with a high recall.
Proceedings of the 23rd International Workshop on Description Logics (DL 2010), Waterloo, Ontario, Canada, May 4-7, 2010; 01/2010
[show abstract][hide abstract] ABSTRACT: Ontology diagnosis, a well-known approach for handling inconsistencies in a description logic (DL) based ontology, computes a diagnosis of the ontology, i.e., a minimal subset of axioms in the ontology whose removal restores consistency. However, ontology diagnosis is computationally hard, especially computing a minimum cost diagnosis (MCD) which is a diagnosis such that the sum of the removal costs attached to its axioms is minimized. This paper addresses this problem by finding data tractable DLs for computing an MCD which allow computing an MCD in time polynomial in the size of the ABox of a given ontology. ABox decomposition is used to find a sufficient and necessary condition to identify data tractable DLs for computing an MCD under the unique name assumption (UNA) among all fragments ofSHIN that are at least as expressive as DL-Litecore without inverse roles. The most expressive, data tractable DL identified isSHIN without inverse roles or qualified existential restrictions.