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<Paper uid="W06-1708">
  <Title>The problem of ontology alignment on the web: a first report</Title>
  <Section position="3" start_page="0" end_page="51" type="intro">
    <SectionTitle>
1 Introduction
</SectionTitle>
    <Paragraph position="0"> Many fundamental issues about the viability and exploitation of the web as a linguistic corpus have not been tackled yet. The web is a massive repositoryoftextandmultimediadata. However, thereis not a systematic way of classifying and retrieving these documents. Computational Linguists are of course not the only ones looking at these issues; research on the Semantic Web focuses on providing a semantic description of all the resources on the web, resulting into a mesh of information linked up in such a way as to be easily processable by machines, on a global scale. You can think of it as being an efficient way of representing data on the World Wide Web, or as a globally linked database.1 The way the vision of the Semantic Web will be achieved, is by describing each document using languages such as RDF Schema and OWL, which are capable of explicitly expressing the meaning of terms in vocabularies and the relationships between those terms.</Paragraph>
    <Paragraph position="1">  The issue we are focusing on in this paper is that these languages are used to define ontologies as well. If ultimately a single ontology were used to describe all the documents on the web, systems would be able to exchange information in a transparent way for the end user. The availability of such a standard ontology would be extremely helpful to NLP as well, e.g., it would make it far easier to retrieve all documents on a certain topic. However, until this vision becomes a reality, a plurality of ontologies are being used to describe documents and their content. The task of automatic ontologyalignment ormatching(HughesandAshpole, 2005) then needs to be addressed. The task of ontology matching has been typically carried out manually or semi-automatically, for example through the use of graphical user interfaces (Noy and Musen, 2000). Previous work hasbeendonetoprovideautomatedsupporttothis time consuming task (Rahm and Bernstein, 2001; Cruz and Rajendran, 2003; Doan et al., 2003; Cruz et al., 2004; Subba and Masud, 2004). The various methods can be classified into two main categories: schema based and instance based.</Paragraph>
    <Paragraph position="2"> Schema based approaches try to infer the semantic mappings by exploiting information related to the structure of the ontologies to be matched, like their topological properties, the labels or description of their nodes, and structural constraints defined on the schemas of the ontologies. These methods do not take into account the actual data classified by the ontologies. On the other hand, instance based approaches look at the information contained in the instances of each element of the schema. These methods try to infer the relationships between the nodes of the ontologies from the analysis of their instances. Finally, hybrid approaches combine schema and instance based  methods into integrated systems.</Paragraph>
    <Paragraph position="3"> Neither instance level information, nor NLP techniques have been extensively explored in previous work on ontology matching. For example, (Agirre et al., 2000) exploits documents (instances) on the WWW to enrich WordNet (Miller et al., 1990), i.e., to compute &amp;quot;concept signatures,&amp;quot; collection of words that significantly distinguish one sense from another, however, not directly for ontology matching. (Liu et al., 2005) uses documents retrieved via queries augmented with, for example, synonyms that WordNet provides to improve the accuracy of the queries themselves, but not for ontology matching. NLP techniques such as POS tagging, or parsing, have been used for ontology matching, but on the names and definitions in the ontology itself, for example, in (Hovy, 2002), hence with a schema based methodology.</Paragraph>
    <Paragraph position="4"> In this paper, we describe the results we obtained when using some simple but effective NLP methodstoalignwebontologies, usinganinstance based approach. As we will see, our results show that more sophisticated methods do not necessarily lead to better results.</Paragraph>
  </Section>
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