1. Introduction

1.1 Motivation and subject domain

The management of large amounts of information and knowledge is of ever increasing importance in today’s large organizations. With the ongoing ease of supplying information online, especially in corporate intranets and knowledge bases, finding the right information becomes an increasingly difficult task. Today’s search tools perform rather poorly in the sense that information access is mostly based on keyword searching or even mere browsing of topic areas. This unfocused approach often leads to undesired results. The following example illustrates the problem more clearly:

One might, for example, want to find out which organization established the Agreement of Agriculture. A simple search for “establish Agreement of Agriculture” might result in a huge list of documents containing these words, but actually none of them containing the desired result: WTO or World Trade Organization. The problem becomes even worse, if the result searched for only appears in a foreign language document. Figure 1 shows an extract of an ontology, which could solve this problem. The grey ellipses represent generic concepts, whereas the white ones represent specific instances of these concepts. The two concepts shown here are interlinked by a relationship. The ontology enabled search application would first identify “Agreement of Agriculture” as a “standard” and would then detect the relationship “establish” to “international organization” and its instances, and hence solve the problem by extending the search query. Furthermore, it could provide added value by detecting other relationships that provide the user with more possibilities, for example standards of other organizations could be presented.

This example shows how ontologies can help to improve the management of information. Semantically annotated documents, i.e. documents which are indexed with ontological terms and concepts instead of simple keywords, provide several advantages. First, the ontological abstraction provides robustness against changes in the document. In the above example, the document content might change using the term ‘Agricultural Agreement’ instead of ‘Agreement of Agriculture’. However, since the document has been annotated with the ontological semantics, this will not affect the search results. Second, since the ontology used for annotating the document is domain specific, the semantic meanings and interpretations of keywords are bound to that domain and therefore the retrieval is likely to be more efficient. A term can have several meanings in different domains. By first mapping the keyword to its semantic representation in a specific ontology and using the ontology’s linked knowledge structure, a much more focused search approach can be taken. Third, document specific representations no longer affect the search. This is extremely important in the case of multilingual representations. Keywords of several languages are mapped to the same concept in an ontology and are therefore given the same meaning. Multilingual search portals can be established to produce the same results, no matter which language is used for retrieval.

Figure 1: Ontology example, excerpt

Another important issue of knowledge management, especially with regards to document metadata and indexing, is the classification of documents. Presently, this is carried out by subject specialists in a time consuming process. With today’s vast amount of available information on the WWW, automatic support is needed to efficiently manage this task. Ontologies play a critical role in supporting the machine readable semantics needed to facilitate automation.

Before such powerful Semantic Web^[1] applications can be built and used within certain domains of knowledge, the basic requirement, a machine readable vocabulary represented by a domain ontology has to be established. The creation of ontologies is a time consuming task and often carried out in an ad-hoc manner. Only few methodologies exist, and even less automated tool support is available. Constituting the knowledge base for future Semantic Web applications, domain ontologies have to be created continuously in all possible areas and communities. The need for a reusable methodology is evident. This paper outlines a comprehensive, reusable framework for semi-automatically-aided building of domain ontologies. A prototype project is used for the application of this computer-aided framework, which provides the reader with a practical, methodological ontology engineering approach.

The domain that serves for creating the prototype ontology is the Biosecurity Portal on food safety, animal and plant health. The portal is an access point for official national and international information relating to biosecurity, the risks associated with agriculture (including fisheries and forestry), and food production. Many countries are still struggling with rapid advances in technology and often lack access to basic information on food safety, animal health and plant health. However, access to this information is of paramount importance for countries to protect health, agriculture and the environment.

One of the goals of the portal is to serve as an electronic information exchange mechanism for the addressed community and therefore to ensure efficient and effective information retrieval. The extension of its knowledge base to information available on various other sources in the WWW can highly support the purpose of the portal. Serving an international community, the information must be retrievable in various languages. The domain is multidisciplinary across three different, but related subject areas. The motivation to create a commonly agreed on, formally specified vocabulary in form of domain ontologies becomes evident

1.2 Overview of the approach

The presented project introduces a comprehensive framework for building a domain-specific ontology. The approach combines classical methodologies for human-based ontology engineering with semiautomatic support of a heuristic toolset. Actually, two methods for ontology acquisition are applied in order to create the domain ontology. The first is to create a small domain-specific core ontology from scratch and then apply a focused web crawler to this ontology in order to retrieve domain related web pages and interesting domain terms for extending this base ontology. The second acquisition approach takes a well-established thesaurus as a basic vocabulary reference set and converts it to an ontology representation. Then a domain specific and a general corpus of texts are used in order to remove concepts that are not descriptive for the domain. The heuristic used here is, that domain specific concepts are more frequent in the domain-specific text corpus. A side product of this removal step is again a list of frequent terms, which can eventually enhance the ontology (see Volz 2000 for more details on this approach). The results of these steps are assessed to assemble the final domain specific ontology, which is now accessible through a multilingual web portal.

1.3 Outline

The next section provides a brief introduction to the larger framework the prototype project is embedded in. In section 3 a proposed layered multilingual ontology model is introduced. It sets the basis for the methodological framework, which is discussed in detail in Section 4. All steps of the prototype project are then presented in Section 5 and currently available results are shown. Finally, section 6 gives an outlook on further work and opportunities that this project enables.