Development of Ontologies for the Semantic Web

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In Proc. of the 40th Annual Conference of the Italian Computing Association (AICA 2002)

Development of Ontologies for the Semantic Web: An Overview Diego Calvanese1 , Giuseppe De Giacomo1 , Enrico Franconi2 1

Dipartimento di Informatica e Sistemistica Universit`a di Roma “La Sapienza” Via Salaria 113, 00198 Roma, Italy, {calvanese,degiacomo}@dis.uniroma1.it 2

Faculty of Computer Science Free University of Bozen-Bolzano Dominikanerplatz 3, I-39100 Bozen, Italy [email protected] http://www.inf.unibz.it/~franconi/

Abstract In this paper we overview the main research areas that, in our view, will have a profound impact on the future development of ontologies for the Semantic Web. Specifically, apart from the research on ontologies itself, we analyse the following fieldswhose technologies may contribute to ontology development: knowledge representation and reasoning techniques and systems, information integration and interoperation in databases and information systems, and quality analysis and quality based design.

1

Introduction

Currently computers are changing from single isolated devices to entry points into a worldwide network of information. As a result, support in the exchange of data, information, and knowledge is becoming the key issue in current computer technology. Ontologies play a major role in supporting information exchange across various networks. Defined as “specifications of a shared conceptualisation of a particular domain”, they provide a shared and common understanding of a domain that can be communicated across people and application systems, and thus facilitate knowledge sharing and reuse. Ontologies will play a key role in growth areas such as intelligent information integration, cooperative information systems, information retrieval, digital libraries, knowledge management and electronic commerce, and bioinformatics (especially medical terminologies). Also, Ontologies are widely regarded as one of the foundational technologies for the Semantic Web. Indeed, recently we observe the development of large numbers

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of ontologies (see, e.g., WordNet1 , CYC2 , and the DAML ontology library3 ). These ontologies have, however, usually been developed in an ad hoc manner by domain experts, often with only a limited understanding of the semantics of ontology languages. The result is that many ontologies are of low quality—they make poor use of the languages in which they are written and do not accurately capture the author’s rich knowledge of the domain. This problem becomes even more acute as ontologies are maintained and extended over time, often by multiple authors. Poor quality ontologies usually require localised “tuning” in order to achieve the desired results within applications. This leads to further degradation in their overall quality, increases the brittleness of the applications that use them, and makes interoperability and reuse difficult or impossible. In order to overcome these problems, ontologists need clear and measurable quality criteria, and tools that support them in the task of engineering ontologies that meet these criteria. Indeed, the growing importance of ontologies in various application domains calls for the design and the development of the basic infrastructure for building, merging, and maintaining ontologies. However, there is still a fundamental lack of basic technical and methodological means for the development of high quality ontologies, and for preserving the quality and reusability of ontologies over time. In this paper we overview the main research areas that, in our view, will have a profound impact on the future development of ontologies for the Semantic Web. Specifically, apart from the research on ontologies itself, the following areas can deeply contribute to ontology development: • Automated reasoning techniques and systems. Ontologies can be formalised in logic, and the use of automated reasoning tools allows one to extract and exploit knowledge that is only available implicitly. This makes research in knowledge representation and reasoning of great interest for ontologies. • Information integration and interoperation. Ontologies, especially in the Semantic Web, are developed in a distributed and autonomous way by independent parties. Integration and interoperation of such ontologies is a major challenge. Research on schema and data integration in databases and information systems is the starting point for studying integration and interoperation of ontologies. • Quality analysis and quality based design. Ontologies, as software, are human artifacts that will be widely deployed. Hence both designers and users of ontologies need objective and measurable quality criteria for evaluating several different aspects concerning them. Research on quality analysis, typical of software engineering, has to be adapted to the issues specific for ontology development. The rest of the paper is organised as follows. In Section 2 we overview recent research on ontology development. In Section 3 we discuss the work on knowledge representation and reasoning relevant for ontologies. In Section 4 we analyse the research in integration and interoperation in databases and information systems, which can be the base for 1

http://www.cogsci.princeton.edu/~wn/ http://www.cyc.com/ 3 http://www.daml.org/ontologies/ 2

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integration and interoperation of ontologies. In Section 5 we look at recent developments in quality analysis that can be relevant for ontologies. Finally, in Section 6 we discuss the limitations of the current approaches for ontology development and preliminary ideas to overcome them.

2

Ontologies

Even though ontologies have a long history in Artificial Intelligence (AI), the meaning of this concept still generates a lot of controversy in discussions, both within and outside of AI. We follow Gruber’s definition [46]: an ontology is a specification of a conceptualisation, that is, an abstract and simplified view of the world that we wish to represent, described in a language that is equipped with a formal semantics. In knowledge representation, an ontology is a description of the concepts and relationships in an application domain. Depending on the users of this ontology, such a description must be understandable by humans and/or by software agents. Researchers in AI were the first to develop ontologies with the purpose of facilitating automated knowledge sharing (see, e.g., [82, 52, 77]). Since the beginning of the 90’s, ontologies have become a popular research topic, and several AI research communities, including knowledge engineering, knowledge acquisition, natural language processing, and knowledge representation, have investigated them. More recently, the notion of an ontology is becoming widespread in fields such as intelligent information integration, cooperative information systems, information retrieval, digital libraries, e-commerce, and knowledge management. Ontologies are widely regarded as one of the foundational technologies for the Semantic Web [18, 68, 53]: when annotating web documents with machine-interpretable information concerning their content, the meaning of the terms used in such an annotation should be fixed in a (shared) ontology. Recent research has focused on the development of formalisms—with a logic-based semantics—for representing ontologies (e.g., OIL [38] and DAML+OIL [34]) and tools for building, maintaining and integrating ontologies (e.g., OilEd, OntoEdit, Chimaera, and Icom [16, 79, 69, 39]). An important area of research is concerned with philosophical issues related to the structure of knowledge and with the specification of general-purpose “upper level” ontologies that reflect this structure (see, e.g., CYC and the IEEE Standard Upper Ontology effort). Progress in these endeavours has, however, been very slow—it has proved extremely difficult to reach a consensus, and many now doubt if it is possible to define ontologies that accurately represent the structure of knowledge across widely varying domains of discourse. In contrast to such general ontologies, a variety of specialised ontologies have now been built for all kinds of applications (see, e.g., [45, 74, 71, 72, 54]). These efforts have been more successful and some of these ontologies have turned out to be very useful in applications. Various methodologies are being developed on how to build a “good” ontology. These methodologies are mainly developed in the field of knowledge acquisition (KA) (see, e.g., [49, 78]), but also by researchers following a more interdisciplinary approach [47, 15, 6]. These approaches differ in many aspects, e.g., in the underlying representation 3

formalism, and whether they are equipped with an explicit notion of quality.

3

Reasoning Techniques and Systems

Interestingly, state-of-the-art ontology languages like OIL and DAML+OIL can be viewed as dialects (see, e.g., [59]) of the most popular logic-based knowledge representation family of languages, namely description logics (DLs) [31, 32]. Hence, DLs form a good starting point for the development of a logic-based formalism for representing ontologies. This relation was already clear in early days of DL research (i.e., 80ies and early 90ies), but at that time there was a fundamental mismatch between the expressive power and the efficiency of reasoning that DL systems provided, and the expressivity and the large ontologies that ontologists needed. In the last decade, the expressive power provided by DL-based systems has increased dramatically: starting from tight worst-case complexity bounds for highly expressive DLs [22, 36, 25, 80, 28] (these DLs are mostly EXPTIME-complete), practical reasoning algorithms for these logics were developed [57, 59, 58]. These algorithms turned out to be amenable to sophisticated optimisation techniques, and they gave rise to the current state-of-the-art reasoning systems, which behave surprisingly well in practice [55, 56, 48]. In addition to the research on standard reasoning problems like subsumption, nonstandard reasoning problems for description logics have been investigated in the last few years. This was motivated by the observation that an optimal support for building and maintaining large knowledge bases also requires system services that cannot be provided by the standard reasoning techniques. These non-standard reasoning problems encompass matching and unification of concepts (useful, e.g., for browsing ontologies and detecting redundancies) [11, 9, 8], least-common-subsumer and most-specific-concept computation (useful to support the definition of new concepts) [7, 10], and approximation of concepts (useful for approximate reasoning and for a comprehensible presentation of ontologies to non-expert users). Though DLs are good candidates for a logical basis of ontology languages, this research field is not the only source of results on reasoning in logics that are relevant for ontologies. In various other areas of computational logic, results relevant for this project can be found: e.g., in temporal, modal, dynamic, and hybrid logic, in automated deduction, and in the research on decidable fragments of first-order logic [51, 43, 44, 83, 23, 24, 4, 42, 5]. These results may also concern reasoning problems that are not prominent in DLs (e.g., model checking), but which may become relevant for the quality-oriented ontology management [21, 19]. And they may concern logics that differ in expressive power from DLs (e.g., some hybrid and temporal logics [3, 37]). Fortunately, there exist a well-established correspondences between the above mentioned fields, and thus there is a good chance that results can be transferred [76, 35, 20, 62, 2].

4

Integration and Interoperation

Ontology integration is the problem of combining ontologies from different sources, and providing the user with a unified view of these ontologies, called mediated ontology. 4

Ontology interoperation is the problem of allowing various ontologies to interoperate and exchange information, without necessarily providing a mediated ontology as the principle mean of accessing information in them. Research on ontology integration and interoperation can draw from the large amount of work carried out in data integration [27, 26]. For a survey on data integration, see e.g., [65]. Many organisations face the problem of integrating conceptual schemas residing in several sources. Companies that build a Data Warehouse, a Data Mining, or an Enterprise Resource Planning system must address this problem. Also, integrating ontologies in the semantic web is the subject of several investigations and projects nowadays. Indeed, the web is constituted by a variety of information sources, each expressed over a certain ontology, and in order to extract information from such sources, their semantic integration and reconciliation in terms of a mediated ontology is required. Many research works have addressed the fundamental problem of how to specify the mapping (also called articulation) between the mediated global schema (global ontology) and the information sources (local ontologies), see, e.g., [61, 33, 67, 29, 70, 41, 64]. For capturing such a mapping in an appropriate way, the notion of query is a crucial one, since it is very likely that a concept in one source corresponds to a (possibly simple) view—that is, a query—over the other sources. As a result the problem of information integration is strongly related to the problem of view-based query processing in data integration [17, 30]. Two basic approaches have been used to specify the relation between the sources and the mediated schema [81]. The first approach, called global-as-view, requires that the mediated schema is expressed in terms of the concepts in the sources. More precisely, to every concept of the mediated schema, a view over the concepts in the sources is associated, so that its meaning is specified in terms of those concepts. The second approach, called local-as-view, requires the mediated schema to be specified independently from the sources. The relationships between the mediated schema and the sources are established by defining every source as a view over the mediated schema. Thus, in the local-as-view approach, we specify the meaning of the sources in terms of the concepts in the mediated schema. It is clear that the latter approach favours the extensibility of the integration system, and provides a more appropriate setting for its maintenance. For example, adding a new source to the system requires only to provide the definition of the source, and does not necessarily involve changes in the mediated schema. On the contrary, in the global-as-view approach, adding a new source typically requires changing the definition of the concepts in the mediated schema. Interestingly, also approaches that mix the global-as-view and local-as-view perspective have been proposed [40, 26]. This work can be seen as a first step towards ontology interoperation that does without a global reconciled view of the information.

5

Quality Analysis

Models and tools for quality oriented ontology development can build on substantial previous work in the fields of data and software quality. Quality assurance includes all the planned and systematic actions necessary to provide adequate confidence that a product will satisfy a given set of quality requirements. There is a significant amount 5

of work on quality assurance on data, from which the project can draw input [88]. Research regarding the design of data manufacturing systems that incorporate data quality aspects is classified into two approaches: (i) the development of analytical models and (ii) the design of system technologies to ensure the quality of data. In the former group, one can find models for investigating the quality in existing systems [12, 13] that produce, e.g., expressions for the magnitude of certain types of errors; it is also possible to predict the impact of quality control efforts on such rates. In the latter group, data tracking techniques have been proposed, which use a combination of statistical control and manual identification of errors and their sources [60, 73, 75]. The models in [84, 86, 87, 85] assume that the quality design of an information system can be incorporated in the overall design of the system. Further work on data quality can be found in [14, 63, 50, 66, 1]. Of particular interest for quality-based ontology development is also the work carried out in the ESPRIT-IV “Foundations of Data Warehouse Quality (DWQ)” project [65], where a global framework linking quality factors with conceptual data models equipped with formal semantics was proposed.

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Limitations of the Current Approaches

Today, the key role of ontologies in information management in general and the Semantic Web in particular, has led to the rapid development of a large number of ontologies. These ontologies have, however, usually been developed in an ad-hoc manner by domain experts, often with only a limited understanding of the semantics of ontology languages. The result is that many ontologies are of low quality—they make poor use of the languages in which they are written and do not accurately capture the author’s rich knowledge of the domain. To the best of our knowledge, an ontology building methodology which uses the support provided by state-of-the-art logic based ontology languages and also helps the user to take advantage of the full expressive power of such a language is still missing. Such a methodology is indeed needed to build high-quality ontologies since (1) domain experts are, in general, not experts in the ontology language they are using, (2) formalising one’s knowledge in any kind of formalism is, in general, a highly complicated task which requires a lot of discipline, perseverance, and/or support. These problems are often aggravated by the fact that one ontology is built by several domain experts, and that ontologies need to be maintained and extended over the time. Other limitations of the current methodologies arise from the current status of the reasoning techniques employed by the state-of-the-art ontology systems. First of all, the expressive power required for representing high-quality ontologies is still not fully provided. Secondly, the sheer size of realistic ontologies pose problems that may require new optimisation techniques for reasoning. Thirdly, the fact that ontologies need to be integrated and interoperated requires the investigation of novel paradigms (such as a mixed global-as-view and local-as-view approach) and of reasoning problems tailored to supporting these tasks. Poor quality ontologies usually require localised “tuning” in order to achieve the desired results within applications. This leads to further degradation in their overall 6

quality (e.g., their interpretability), increases the brittleness of the applications that use them, and makes interoperability and re-use difficult or impossible. These considerations will be of crucial importance to the Semantic Web, where it is expected that (very large numbers of) web pages will be marked up using multiple ontologies, without the authors of the ontologies having any knowledge or control over the use to which they are being put. The research and the technologies we have overviewed in this paper will contribute in overcoming these problems. In particular, ontologists need clear and measurable quality criteria, and tools that take them into account when building, maintaining, and interoperating ontologies. To “take into account” means both to measure the quality of an ontology w.r.t. specific quality criteria as well as to support the user when operating an ontology. The latter point requires that the user, when operating an ontology, can state her quality requirements, which are then taken into account by the system supporting this operation. Logics with suitable computational properties are natural candidates to provide formal foundations for solid and measurable quality criteria on which to base methodologies and advanced tools for ontology development.

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Conclusions

In this paper we have overviewed some of the research areas the promise to have a big impact on ontology development for the Semantic Web, namely ontologies, automated reasoning, integration and interoperation, and quality analysis. An aspect that we have not touched is the development and composition of e-services that make use of ontologies, which are of fundamental interest for the Semantic Web and currently actively studied. To tackle such an issue, the research mentioned in this paper is still of importance. However, an understanding of the basic issues regarding the semantics of e-services is also needed and still under investigation.

Acknowledgements The ideas presented here are the result of various discussions with Franz Baader, Jeen Broekstra, Carole Goble, Frank van Harmelen, Ian Horrocks, Maurizio Lenzerini, Daniele Nardi, Ulrike Sattler, and Heiner Stuckenschmidt. We would like to thank all of them.

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