Alexandros Chortaras

Optimized query rewriting for OWL 2 QL

The OWL 2 QL profile has been designed to facilitate query answering via query rewriting. This paper presents an optimized query rewriting algorithm which takes advantage of the special characteristics of the query rewriting problem via first-order resolution in OWL 2 QL and computes efficiently the rewriting set of a user query, by avoiding blind and unnecessary inferences, as well as by reducing the need for extended subsumption checks. The evaluation shows that in several cases the algorithm achieves a significant improvement and better practical scalability if compared to other similar approaches.

Optimising resolution-based rewriting algorithms for OWL ontologies

An important approach to query answering over OWL ontologies is via rewriting the input ontology (and query) into a new set of axioms that are expressed in logics for which scalable query answering algorithms exist. This approach has been studied for many important fragments of OWL like SHIQ SHIQ , Horn- SHIQ SHIQ , OWL 2 QL, and OWL 2 EL. An important family of rewriting algorithms is the family of resolution-based algorithms, mostly because of their ability to adapt to any ontology language (such algorithms have been proposed for all aforementioned logics) and the long years of research in resolution theorem-proving. However, this generality comes with performance prices and many approaches that implement algorithms that are tailor-made to a specific language are more efficient than the (usually) general-purposed resolution-based ones. In the current paper we revisit and refine the resolution approaches in order to design efficient rewriting algorithms for many important fragments of OWL. First, we present an algorithm for the language DL-Lite R,⊓ R,⊓ which is strongly related to OWL 2 QL. Our calculus is optimised in such a way that it avoids performing many unnecessary inferences, one of the main problems of typical resolution algorithms. Subsequently, we extend the algorithm to the language ELHI ELHI which is strongly related to OWL 2 EL. This is a difficult task as ELHI ELHI is a relatively expressive language, however, we show that the calculus for DL-Lite R,⊓ R,⊓ requires small extensions. Finally, we have implemented all algorithms and have conducted an extensive experimental evaluation using many well-known large and complex OWL ontologies. On the one hand, this is the first evaluation of rewriting algorithms of this magnitude, while, on the other hand, our results show that our system is in many cases several orders of magnitude faster than the existing systems even though it uses an additional backwards subsumption checking step.

Learning ontology alignments using recursive neural networks

The Semantic Web is based on technologies that make the content of the Web machine-understandable. In that framework, ontological knowledge representation has become an important tool for the analysis and understanding of multimedia information. Because of the distributed nature of the Semantic Web however, ontologies describing similar fields of knowledge are being developed and the data coming from similar but non-identical ontologies can be combined only if a semantic mapping between them is first established. This has lead to the development of several ontology alignment tools. We propose an automatic ontology alignment method based on the recursive neural network model that uses ontology instances to learn similarities between ontology concepts. Recursive neural networks are an extension of common neural networks, designed to process efficiently structured data. Since ontologies are a structured data representation, the model is inherently suitable for use with ontologies.

Adaptation of weighted fuzzy programs

Fuzzy logic programs are a useful framework for handling uncertainty in logic programming; nevertheless, there is the need for modelling adaptation of fuzzy logic programs. In this paper, we first overview weighted fuzzy programs, which bring fuzzy logic programs and connectionist models closer together by associating significance weights with the atoms of a logic rule: by exploiting the existence of weights, it is possible to construct a neural network model that reflects the structure of a weighted fuzzy program. Based on this model, we then introduce the weighted fuzzy program adaptation problem and propose an algorithm for adapting the weights of the rules of the program to fit a given dataset.

Integrated Query Answering with Weighted Fuzzy Rules

Weighted fuzzy logic programs increase the expressivity of fuzzy logic programs by allowing the association of a significance weight with each atom in the body of a fuzzy rule. In this paper, we propose a prototype system for the practical integration of weighted fuzzy logic programs with relational database systems in order to provide efficient query answering services. In the system, a dynamic weighted fuzzy logic program is a set of rules together with a set of database queries, fuzzification transformations and fact derivation rules, which allow the provided set of rules to be augmented with a set of fuzzy facts retrieved from the underlying databases. The weights of the rules may be estimated by a neural network-based machine learning process using some specially designated for this purpose training database data.

A Connectionist Model for Weighted Fuzzy Programs

The usefulness of the results of logic programming in real-life applications is sometimes limited due to the inability of this theory to model the uncertain and dynamic character of real environments. Fuzzy logic programming has been lately considered as an important framework for handling uncertainty in logic programming systems. Still, there is a need for modelling adaptation of logic programs and the progress in this area is rather slow. In the present paper, we first extend fuzzy logic programs in a direction that brings them closer to the connectionist approach: we introduce weighted fuzzy programs, which allow the association of significance weights with the atoms that make up the body of a logic rule. The weights add expressiveness to the programs and allow the determination of the degree with which an antecedent affects the value of the rule consequent. Then, we propose a neural network implementation of weighted fuzzy programs that is capable of computing the minimal Herbrand model of a weighted fuzzy program.

Top-down computation of the semantics of weighted fuzzy logic programs

We describe a procedural, query answering-oriented semantics for weighted fuzzy logic programs. The computation of the semantics combines resolution with tabling methodologies and is done by constructing and evaluating an appropriate resolution graph.

Enriching and publishing cultural heritage as linked open data

In the last decade, a lot of effort has been put by the cultural community around the world into digitization and aggregation activities. The main outcome of these was the development of portals like Europeana, DPLA, DigitalNZ, and National Library of Australia, which are collecting and providing access to the public digitized cultural assets from Europe, America, New Zealand, and Australia, respectively. Their main objective, however, is not only to bring the public closer to culture but also to efficiently represent information about cultural objects that will make them useful to various target groups like teachers, students, and developers by also permitting their creative reuse. The best practice for fulfilling this requirement is the publication of such information according to the Linked Open Data (LOD) principles. In this chapter, we present the tools developed and the methodology adopted through the participation of our group in aggregation activities for enriching and publishing cultural heritage as Linked Open Data.

Web Reasoning for Cultural Heritage

Cultural Heritage is the focus of a great and continually increasing number of R&D initiatives, aiming at efficiently managing and disseminating cultural resources on the Web. As more institutions make their collections available online and proceed to aggregate them in domain repositories, knowledge-based management and retrieval becomes a necessary evolution from simple syntactic data exchange. In the process of aggregating heterogeneous resources and publishing them for retrieval and creative reuse, networks such as Europeana and DPLA invest in technologies that achieve semantic data integration. The resulting repositories join the Linked Open Data cloud, allowing to link cultural heritage domain knowledge to existing datasets. Integration of diverse information is achieved through the use of formal ontologies, enabling reasoning services to offer powerful semantic search and navigation mechanisms.

DEFINITION AND ADAPTATION OF WEIGHTED FUZZY LOGIC PROGRAMS

Fuzzy logic programming has been lately used as a general framework for representing and handling imprecise knowledge. In this paper, we define the syntax and the semantics of definite weighted fuzzy logic programs, which extend definite fuzzy logic programs by allowing the inclusion of different significance weights in the individual atoms that make up the antecedent of a fuzzy logic rule. The weights add expressiveness to a fuzzy logic program and allow the determination of the level up to which an atom in the antecedent of a rule may affect the truth value of its consequent. In describing the semantics of definite weighted fuzzy logic programs we introduce the notion of the generalized weighted fuzzy conjunction operator, which can be regarded as a weighted t-norm based aggregation. We determine the properties of generalized weighted fuzzy conjunction operators and provide several examples. A methodology for constructing generalized weighted fuzzy conjunction operators using generator functions of existing t-norms is also introduced. Finally, a method for setting up a parametric weighted fuzzy logic program and automatically adapting the weights of its rules using a numerical dataset is developed.