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Papers by Hassan Aït-Kaci

Deliverable D3.3 Complexity and optimization of combinations of rules and ontologies. Will includ... more Deliverable D3.3 Complexity and optimization of combinations of rules and ontologies. Will include the selection of promising combinations, and the complexity analysis of these selected combinations, as well as theoretical optimizations for processing.

Workshop on Persistent Object Systems, 1985

Journal of Intelligent Information Systems, 2015

ABSTRACT This document addresses the question of how efficiently the most well-known Semantic Web... more ABSTRACT This document addresses the question of how efficiently the most well-known Semantic Web (SW) reasoners perform in processing (classifying and querying) taxonomies of enormous size. Using techniques that were proposed 25 years ago for implementing efficient lattice operations, we have implemented a simple taxonomic concept classification and Boolean query-answering system. We compared its performance with those of the best existing SW reasoning systems over several very large taxonomies under the exact same conditions for so-called TBox reasoning.

Lattice operations such as greatest lower bound (GLB), least upper bound (LUB), and relative comp... more Lattice operations such as greatest lower bound (GLB), least upper bound (LUB), and relative complementation (BUTNOT) are becoming more and more important in programming languages supporting object inheritance. We present a general technique for the efficient implementation of such operations based on an encoding method. The effect of the encoding is to plunge the given ordering into a boolean lattice of binary words, leading to an almost constant time complexity of the lattice operations. A first method is described based on a transitive closure approach. Then, a more space-efficient method minimizing code-word length is described. Finally, a powerful grouping technique called modulation is presented which drastically reduces code space while keeping all three lattice operations highly efficient. This technique takes into account idiosyncrasies of the topology of the poset being encoded which are quite likely to occur in practice. All methods are formally justified. We see this wor...

This article illustrates how constraint logic programming can be used to express data models in r... more This article illustrates how constraint logic programming can be used to express data models in rule-based languages, including those based on graph pattern-matching or unification to drive rule application. This is motivated by the interest in using constraint-based technology in conjunction with rule-based technology to provide a formally correct and effective—indeed, efficient!—operational base for the semantic web.

DESCRIPTION This is a brief recapitulation of the CEDAR ANR CHEX-2012 Project grant No ANR-12-CHE... more DESCRIPTION This is a brief recapitulation of the CEDAR ANR CHEX-2012 Project grant No ANR-12-CHEX-0003-01 (Jan. 2013 – Jan 2015) ◮ summarizing the CEDAR project ◮ what was the CEDAR project’s objective in the beginning? ◮ tallying the productions of the CEDAR project ◮ dabbling in some details just to give a taste ◮ sharing some lessons learned during the CEDAR project ◮ inviting you all to look deeper under the hood of CEDAR

Taxonomy classi�cation and query answering are the core reasoning services provided by most of th... more Taxonomy classi�cation and query answering are the core reasoning services provided by most of the Semantic Web (SW) reasoners. However, the algorithms used by those reasoners are based on Tableau method or Rules. These well-known methods in the literature have already shown their limitations for large-scale reasoning. In this demonstration, we shall present the CEDAR system for classifying and reasoning on very large taxonomies using a technique based on lattice operations. This technique makes the CEDAR reasoner perform on par with the best systems for concept classi�cation and several orders-of- magnitude more e�ciently in terms of response time for query-answering. The experiments were carried out using very large taxonomies (Wikipedia: 111599 sorts, MESH: 286381 sorts, NCBI: 903617 sorts and Biomodels: 182651 sorts). The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners, namely FaCT++, Pellet, HermiT, TrOWL, SnoRocket and RacerPro

An RDF (Resource Description Framework) instance generator produces RDF triples by complying with... more An RDF (Resource Description Framework) instance generator produces RDF triples by complying with an ontology that defines classes, subclasses, relations, and constraints. There aremany instance generators which rely onWeb Ontology Language (OWL) meaning that these generators can read only the ontologies which are written in OWL. However, the existing generators are locked-in to a specific ontology, which means the generators can read only a specific ontology. For instance, the LUBM generator can read only the LUBM ontology, which is clearly a limitation as it is not able to read any other ontology such as biomodel ontology. This promotes the need for a generic RDF instance generator that is able to read and parse any ontology written in OWL. In this technical report, we describe a generic RDF instance generator. We develop this generator to enable users to use their own ontology to generate RDF triples which can be used to meet their specific needs.

We present an extended version of the CEDAR taxonomic reasoner for large-scale ontologies. This n... more We present an extended version of the CEDAR taxonomic reasoner for large-scale ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The CEDAR system is based upon the OSF formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional map-ping between OSF graph structures and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances from an ABox consisting of an RDF triplestore. We carried out comparative performance evaluation experiments using CEDAR as well as well-known Semantic Web reasoners (such as FaCT++, Pellet, HermiT, TrO WL, and RacerPro) on very large public ontologies. For the same queries on the same ontologies, the results achieved by CEDAR were compared to those obtained by all the other reasoners. The results of experiments show that CEDAR consistently performs on a par ...

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), 2014

Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '88, 1988

ABSTRACT This report discusses the implementation of a knowledge base for a library information s... more ABSTRACT This report discusses the implementation of a knowledge base for a library information system. It is done using a typed logic programming language—LOGIN—where type inheritance is built in. The knowledge base is structured in a hierarchical taxonomy of library object classes where each class is represented in a FRAME style knowledge structure and inherits the properties of its parents, and where infrastructural inference rules have been established through typed Horn clauses. Also in this document, some programming techniques aimed at using the power of inheritance as taxonomic inference are discussed.

2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014

ABSTRACT We present a new version of CEDAR, a taxonomic reasoner for large-scale ontologies. This... more ABSTRACT We present a new version of CEDAR, a taxonomic reasoner for large-scale ontologies. This extended version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. CEDAR is built on top of the OSF formalism and based on an entirely new architecture which includes several optimization techniques. Using OSF graph structures, we define a bidirectional mapping between OSF structure and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances. Experiments were carried out using very large ontologies. The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners such as FaCT++, Pellet, HermiT, TrOWL, and RacerPro. CEDAR performs on a par with the best systems for concept classification and several orders of magnitude more efficiently in terms of response time for Boolean query-answering.

Deliverable D3.3 Complexity and optimization of combinations of rules and ontologies. Will includ... more Deliverable D3.3 Complexity and optimization of combinations of rules and ontologies. Will include the selection of promising combinations, and the complexity analysis of these selected combinations, as well as theoretical optimizations for processing.

Workshop on Persistent Object Systems, 1985

Journal of Intelligent Information Systems, 2015

ABSTRACT This document addresses the question of how efficiently the most well-known Semantic Web... more ABSTRACT This document addresses the question of how efficiently the most well-known Semantic Web (SW) reasoners perform in processing (classifying and querying) taxonomies of enormous size. Using techniques that were proposed 25 years ago for implementing efficient lattice operations, we have implemented a simple taxonomic concept classification and Boolean query-answering system. We compared its performance with those of the best existing SW reasoning systems over several very large taxonomies under the exact same conditions for so-called TBox reasoning.

Lattice operations such as greatest lower bound (GLB), least upper bound (LUB), and relative comp... more Lattice operations such as greatest lower bound (GLB), least upper bound (LUB), and relative complementation (BUTNOT) are becoming more and more important in programming languages supporting object inheritance. We present a general technique for the efficient implementation of such operations based on an encoding method. The effect of the encoding is to plunge the given ordering into a boolean lattice of binary words, leading to an almost constant time complexity of the lattice operations. A first method is described based on a transitive closure approach. Then, a more space-efficient method minimizing code-word length is described. Finally, a powerful grouping technique called modulation is presented which drastically reduces code space while keeping all three lattice operations highly efficient. This technique takes into account idiosyncrasies of the topology of the poset being encoded which are quite likely to occur in practice. All methods are formally justified. We see this wor...

This article illustrates how constraint logic programming can be used to express data models in r... more This article illustrates how constraint logic programming can be used to express data models in rule-based languages, including those based on graph pattern-matching or unification to drive rule application. This is motivated by the interest in using constraint-based technology in conjunction with rule-based technology to provide a formally correct and effective—indeed, efficient!—operational base for the semantic web.

DESCRIPTION This is a brief recapitulation of the CEDAR ANR CHEX-2012 Project grant No ANR-12-CHE... more DESCRIPTION This is a brief recapitulation of the CEDAR ANR CHEX-2012 Project grant No ANR-12-CHEX-0003-01 (Jan. 2013 – Jan 2015) ◮ summarizing the CEDAR project ◮ what was the CEDAR project’s objective in the beginning? ◮ tallying the productions of the CEDAR project ◮ dabbling in some details just to give a taste ◮ sharing some lessons learned during the CEDAR project ◮ inviting you all to look deeper under the hood of CEDAR

Taxonomy classi�cation and query answering are the core reasoning services provided by most of th... more Taxonomy classi�cation and query answering are the core reasoning services provided by most of the Semantic Web (SW) reasoners. However, the algorithms used by those reasoners are based on Tableau method or Rules. These well-known methods in the literature have already shown their limitations for large-scale reasoning. In this demonstration, we shall present the CEDAR system for classifying and reasoning on very large taxonomies using a technique based on lattice operations. This technique makes the CEDAR reasoner perform on par with the best systems for concept classi�cation and several orders-of- magnitude more e�ciently in terms of response time for query-answering. The experiments were carried out using very large taxonomies (Wikipedia: 111599 sorts, MESH: 286381 sorts, NCBI: 903617 sorts and Biomodels: 182651 sorts). The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners, namely FaCT++, Pellet, HermiT, TrOWL, SnoRocket and RacerPro

An RDF (Resource Description Framework) instance generator produces RDF triples by complying with... more An RDF (Resource Description Framework) instance generator produces RDF triples by complying with an ontology that defines classes, subclasses, relations, and constraints. There aremany instance generators which rely onWeb Ontology Language (OWL) meaning that these generators can read only the ontologies which are written in OWL. However, the existing generators are locked-in to a specific ontology, which means the generators can read only a specific ontology. For instance, the LUBM generator can read only the LUBM ontology, which is clearly a limitation as it is not able to read any other ontology such as biomodel ontology. This promotes the need for a generic RDF instance generator that is able to read and parse any ontology written in OWL. In this technical report, we describe a generic RDF instance generator. We develop this generator to enable users to use their own ontology to generate RDF triples which can be used to meet their specific needs.

We present an extended version of the CEDAR taxonomic reasoner for large-scale ontologies. This n... more We present an extended version of the CEDAR taxonomic reasoner for large-scale ontologies. This new version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. The CEDAR system is based upon the OSF formalism. It is implemented on an entirely new architecture which includes several optimization techniques. We define a bidirectional map-ping between OSF graph structures and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances from an ABox consisting of an RDF triplestore. We carried out comparative performance evaluation experiments using CEDAR as well as well-known Semantic Web reasoners (such as FaCT++, Pellet, HermiT, TrO WL, and RacerPro) on very large public ontologies. For the same queries on the same ontologies, the results achieved by CEDAR were compared to those obtained by all the other reasoners. The results of experiments show that CEDAR consistently performs on a par ...

2014 IEEE/ACS 11th International Conference on Computer Systems and Applications (AICCSA), 2014

Proceedings of the 11th annual international ACM SIGIR conference on Research and development in information retrieval - SIGIR '88, 1988

ABSTRACT This report discusses the implementation of a knowledge base for a library information s... more ABSTRACT This report discusses the implementation of a knowledge base for a library information system. It is done using a typed logic programming language—LOGIN—where type inheritance is built in. The knowledge base is structured in a hierarchical taxonomy of library object classes where each class is represented in a FRAME style knowledge structure and inherits the properties of its parents, and where infrastructural inference rules have been established through typed Horn clauses. Also in this document, some programming techniques aimed at using the power of inheritance as taxonomic inference are discussed.

2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014

ABSTRACT We present a new version of CEDAR, a taxonomic reasoner for large-scale ontologies. This... more ABSTRACT We present a new version of CEDAR, a taxonomic reasoner for large-scale ontologies. This extended version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. CEDAR is built on top of the OSF formalism and based on an entirely new architecture which includes several optimization techniques. Using OSF graph structures, we define a bidirectional mapping between OSF structure and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances. Experiments were carried out using very large ontologies. The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners such as FaCT++, Pellet, HermiT, TrOWL, and RacerPro. CEDAR performs on a par with the best systems for concept classification and several orders of magnitude more efficiently in terms of response time for Boolean query-answering.