Building and maintaining ontologies: a set of algorithms

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Abstract

“Is_A” links are the core component of all ontologies and are organized into “hierarchies of concepts”. In this paper we will first address the problem of an automatic help to build sound hierarchies. Dependencies called “existence constraints” are the foundation for the definition of a “normalized” hierarchy of concepts. In the first part of the paper algorithms are provided to obtain a normalized hierarchy starting either from concepts or from instances using Boolean functions. The second part of the paper is devoted to the hierarchy maintenance: automatically inserting, merging or removing pieces of knowledge. We also provide a way to give synthetic views of the hierarchy.

Introduction

Information sharing from multiple heterogeneous sources is a challenging issue. There is now evidence that the adequate tool for dealing with semantically heterogeneous data is ontology. Numerous definitions may be found for “ontology” depending on their fields (databases, mathematics, linguistics, philosophy) [27]. However, one of these definitions is becoming predominant in the field of information systems: “an ontology is a formal conceptualization of a real world, sharing a common understanding of this real world”. This definition fits so well to the semantic heterogeneity problem that one of the main application field for ontology is heterogeneous data sharing. Wache et al. present a survey of existing approaches to intelligent information integration after analyzing 25 multi-sources information systems based on ontologies [56].

An ontology provides various semantic links between concepts such as synonyms, antonyms, hyponyms/hypernyms (Is_A) and meronyms/holonyms (part-of). An extensive discussion of relation types is presented in [49]. Other links are supported by “canonical graphs” which specify relationships expected among the concepts involved in a given action. According to the domain portrayed by the ontology, particular kind of links may be supplied. For example, in the medical domain, links are required such as “located in”, “has an effect on”. Tools for information retrieval on the Internet need links relating ideas such as a link between “boat” and “fish”.

Although the use of ontology is dramatically increasing for these last years, only a few research has been undertaken concerning tools for helping in ontology maintenance. Among them, most works concern the integration of several ontologies. A first family of research has been based on Galois lattices, as were some on hierarchy integration [32]. Some practical tools have been proposed, among them we notice ONIONS [24] developed in the context of the GALEN project. The last tendency in research on ontology maintenance is based on description logic that allows the representation of many kind of links (hierarchy of subsumptions of concepts, canonical graphs of the verbs) into a powerful logic reasoning mechanism [7], [21].

Is_A links are the core of all ontologies and are organized into “hierarchies of concepts”. In this paper we will first address the problem of an automatic help to build sound hierarchies of concepts. A type of dependencies called “existence constraints” are the foundation for the definition of a “normalized” hierarchy. Algorithms are provided in this paper to obtain a normalized hierarchy starting either from concepts or from instances. The second part of the paper is devoted to the maintenance of a hierarchy: inserting, merging or removing pieces of knowledge. Section 5 provides a mean to give synthetic views of a hierarchy.

Section snippets

Related works

Since the early nineties we can observe a growing interest in the use of ontologies for sharing information, and consequently tools and techniques have been proposed to ease their management.

In spite of the fact that ontologies and data bases conceptual models are very different in their essence––the first one is expected to formalize a domain while the second one is oriented toward a particular application––many techniques are common to both. So we have classified related works into two

Functionalities for ontology building

In this section we will show the elaboration of the Is_A hierarchy of an ontology starting either from the keywords describing concepts and their existence constraints or from a set of instances. Reverse mechanisms will be also presented with the purpose of building new hierarchies from both new concepts and legacy hierarchies.

Functionalities for ontology maintenance

We will present in this section a set of functionalities aiming to automatically update the hierarchy. They allow us to add or remove concepts, hierarchies and instances. Most of these functionalities are built over the three proposed techniques (extraction, normalization, translation).

Creating synthetic views of ontology

The Is_A hierarchy of an ontology can contain a huge number of concepts and become hard to understand.

This functionality is used to give to provide a synthetic view of the Is_A hierarchy. For example, if the user of the hierarchy of Fig. 3 decides to regroup the concepts “Boat”, “Fishing boat” and “Sailing boat” in one concept then this functionality will supply it with the hierarchy of Fig. 14.

This functionality can merge some concepts, belonging to the same hierarchy, according to a chosen

Conclusion

In this paper we have shown that based on the notion of “existence constraints” we can build sound hierarchies of concepts. After stating their definition, we provided an algorithm for building normalized Is_A hierarchies starting from a set of concepts described by keywords. This algorithm uses relationships between keywords expressed through existence constraints. If these constraints are lacking, a method for extracting Is_A hierarchies from instances, by the mean of Boolean functions, is

Acknowledgements

The authors wish to thank Jacky Akoka and Isabelle Comyn-Wattiau for their kind and efficient help.

Nadira Lammari received her Ph.D. degree in Computer Science from the CNAM University, Paris, France, in 1996. She is an Associate Professor at this same university since 1998 and a researcher in the CEDRIC Laboratory of the CNAM since 1992. Up to 1992, she was teaching at the USTHB University, Algiers, Algeria. Her current research interests include information systems engineering, the reverse engineering of databases, the reverse engineering of web sites and the semantic web. She has been

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    Nadira Lammari received her Ph.D. degree in Computer Science from the CNAM University, Paris, France, in 1996. She is an Associate Professor at this same university since 1998 and a researcher in the CEDRIC Laboratory of the CNAM since 1992. Up to 1992, she was teaching at the USTHB University, Algiers, Algeria. Her current research interests include information systems engineering, the reverse engineering of databases, the reverse engineering of web sites and the semantic web. She has been involved in the organization of ER’99 and is currently an organizer of the DSE’03 (Decision System Engineering) workshop which will be held in conjunction with the CAISE’03 conference.

    Elisabeth Métais (1959) is a Full Professor at the CNAM University (Paris, France) and a researcher in the CEDRIC Laboratory since September 2000. Up to 2000 she was an Associate Professor at the University of Versailles (France) working in the PRiSM Laboratory and she previously was a researcher for the University of Paris VI (France) where she holds her Ph.D. in Computer Science (1987). Her main axe of research has been Database Design. She participated in the definition of SECSI, the first expert system in database design, and has been interested since the early nineties in applying natural language techniques to Database Design. She is currently working on Data Warehousing, focusing on semantic heterogeneity problems in data cleaning and data integration. She directed the EVOLUTION French working group on Data Warehouse Design and is managing the REANIMATIC project aiming to build a warehouse from data collected in Intensive Care Units. She has been acting as a member of about 50 program committees and has been involved in the organisation of EDBT’96, ER’99 and DSE’03. She organised the 1st International Workshop on Application of Natural Language to Data Bases (NLDB’95) and the 5th International Conference on Applications of Natural Language to Information Systems (NLDB’2000) in Versailles (France).

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