OntoExtractor: A Tool for Semi-Automatic Generation and Maintenance of Taxonomies from Semi-Structured Documents

OntoExtractor: A Tool for Semi-Automatic Generation and Maintenance of Taxonomies from Semi-Structured Documents

Marcello Leida (University of Milan, Italy)
Copyright: © 2009 |Pages: 23
DOI: 10.4018/978-1-60566-034-9.ch003
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This chapter introduces OntoExtractor, a tool for the semi-automatic generation of the taxonomy from a set of documents or data sources. The tool generates the taxonomy in a bottom-up fashion. Starting from structural analysis of the documents, it produces a set of clusters, which can be refined by a further grouping created by content analysis. Metadata describing the content of each cluster is automatically generated and analysed by the tool for producing the final taxonomy. A simulation of a tool, based on an implicit and explicit voting mechanism, for the maintenance of the taxonomy is also described. The author depicts a system that can be used to generate the taxonomy from a heterogeneous source of information, using wrappers for converting the original format of the document to a structured one. This way, OntoExtractor can virtually generate the taxonomy from any source of information just adding the proper wrapper. Moreover, the trust mechanism allows a reliable method for maintaining the taxonomy and for overcoming the unavoidable generation of wrong classes in the taxonomy.
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Nowadays, people can easily access virtually infinite sources of information. This unlimited supply of knowledge is potentially useful in a number of application scenarios; on the other hand, information needs to be organized and structured to become a robust and trusted source of knowledge. CoP (Communities of Practise) (Lave et al., 1991) are forming spontaneously, for example, among people sharing same interests, or according to an organization model, for instance among people working on the same project. Members of those communities interact and share information. The need for an organized and common structure that describes shared information is then evident. In the last few years, business ontology has been recognized as the most promising way to describe shared knowledge in a business environment. Shared information often can be redundant, incomplete, or subject to different interpretations. Therefore, we must be able to deal with different levels of uncertainty. Recent studies (Fagin, 1998; Fagin, 1999, & Fagin, 2002) propose using Soft Computer Techniques as a promising approach to handle uncertainty. The Knowledge Management Group of the University of Milan, during the collaboration with BT Intelligent Systems Research Centre, has developed and, in part, implemented a fuzzy-based approach to extract metadata and description knowledge from heterogeneous information sources. Moreover, a fuzzy-based trust system for the maintenance of the generated hierarchy has been proposed. The following sections briefly present the theories behind this approach and the demo software developed on the basis of those theories. Section 2 is a general overview of the construction of the hierarchy; the original approach is intended to build classes of ontology from the knowledge base in a bottom-up fashion. The section is focused on presenting the techniques implemented in the OntoExtractor software--a tool that clusters different types of documents with respect to their structure and their content. Section 3 describes a technique for automatic validation of the generated assertions, by explicit vote, or by considering implicit information that a user produces browsing the hierarchy; the community is described as a Fuzzy Set that depicts the distribution of the expertise in relation to the weight of their behaviours. This section is followed by a brief description of the simulator software used to test the algorithms illustrated. Last section regards the future directions of this approach and some important results and conclusions are presented.

Figure 1.

Overall structure of the process


Ontoextractor: Construction Of The Hierarchy

OntoExtractor (Cui et al., 2005) is a tool, developed by the Knowledge Management Group of the University of Milan, which extracts metadata from heterogeneous information, producing a “quick-and-dirty” hierarchy of knowledge. This tool implements most of the techniques described in this section. The construction of the hierarchy occurs in a bottom-up fashion. Starting from heterogeneous documents, we use the clustering process to group the documents in meaningful clusters. These clusters identify classes in the ontology or, like in the current version of the OntoExtractor tool, extensions of iPhi categories (Martin et al., 2003).The construction of the hierarchy is a three-step process:

  • 1.

    Normalizing the incoming documents in XML format (Salton et al., 1996)

  • 2.

    (Optional) Clustering the documents according to their structure using a Fuzzy Bag representation of the XML tree (Ceravolo et al., 2004; Damiani et al., 2004)

  • 3.

    Refining the structural clustering analyzing the content of the documents, producing a semantic clustering of the documents

In addition, it is possible to analyze the produced hierarchy in order to discover is-a and part-of relations among the cluster representative documents (ClusterHeads) (Ceravolo et al., 2006).

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