Gergely Lukácsy

Efficient description logic reasoning in prolog: The dlog system

Traditional algorithms for description logic (DL) instance retrieval are inefficient for large amounts of underlying data. As DL is becoming more and more popular in areas such as the Semantic Web and information integration, it is very important to have systems which can reason efficiently over large data sets. In this paper we present an approach to transform DL axioms, formalised in the

Prolog Based Description Logic Reasoning

\mathcal{SHIQ}

Translating description logic queries to prolog

DL language, into a Prolog program under the unique name assumption. This transformation is performed with no knowledge about particular individuals: they are accessed dynamically during the normal Prolog execution of the generated program. This technique, together with the top-down Prolog execution, implies that only those pieces of data are accessed that are indeed important for answering the query. This makes it possible to store the individuals in a database instead of memory, which results in better scalability and helps in using DL ontologies directly on top of existing information sources. The transformation process consists of two steps: (1) the DL axioms are converted to first-order clauses of a restricted form, and (2) a Prolog program is generated from these clauses. Step (2), which is the focus of the present paper, actually works on more general clauses than those obtainable by applying step (1) to a

Towards automatic semantic integration

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Plagiarism Detection in Source Programs Using Structural Similarities

knowledge base. We first present a base transformation, the output of which can be either executed using a simple interpreter or further extended to executable Prolog code. We then discuss several optimisation techniques, applicable to the output of the base transformation. Some of these techniques are specific to our approach, while others are general enough to be interesting for DL reasoner implementors not using Prolog. We give an overview of DLog, a DL reasoner in Prolog, which is an implementation of the techniques outlined above. We evaluate the performance of DLog and compare it to some widely used DL reasoners, such as RacerPro, Pellet and KAON2.

Web services as XML data sources in enterprise information integration

In this paper we present the recent developments of the DLog system, an ABox reasoning engine for the the

Description logic reasoning in prolog

\mathcal{SHIQ}

Open world reasoning in datalog

description logic language. DLog differs from traditional description logic reasoners in that it transforms description logic axioms into a Prolog program. The transformation is done independently from the ABox, i.e. the set of assertions about the individuals. This makes it possible to store the ABox assertions in a database, rather than in memory. This approach results in better scalability and helps using description logic ontologies directly on top of existing information sources. The transformation involves several optimisation steps, which aim at producing more efficient Prolog programs. In this paper we focus on the partial evaluation technique we apply to obtain programs that do not use logic variables. This simplifies the implementation, improves performance and opens up the possibility of compiling into Mercury code. In the paper we also present the recent architectural changes in the DLog system, summarise the most important features of the implementation and evaluate the performance of DLog by comparing it to the best available description logic reasoners.

GALEN based formal representation of ICD10

In this paper we present a novel approach for determining the instances of description logic concepts when huge amounts of underlying data are expected. In such cases, traditional description logic theorem proving techniques cannot be used due to performance problems. Our idea is to transform a concept description into a Prolog program which represents a query-plan. This transformation is done without any knowledge of the particular data. Data are accessed dynamically during the normal Prolog execution of the generated program. With this technique only those pieces of data are accessed which are indeed important for answering the query, i.e. we solve the original problem in a database friendly way. We evaluate the performance of our approach and compare it to several description logic reasoners.

Information Integration through Reasoning on Meta-data

We present an Enterprise Information Integration (EII) system called SINTAGMA supporting the semantic integration of heterogeneous information sources. This paper focuses on the features of SINTAGMA allowing the partially automated creation of complex processes.