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2.6.4. Focused Crawling
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An important step in ontology construction can be collecting the relevant data from the Web and using it for populating (ontology learning scenario 3 in Section 2.5) or updating the ontology (ontology
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USING KNOWLEDGE DISCOVERY FOR ONTOLOGY LEARNING
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learning scenario 5 in Section 2.5). Collecting data relevant for the existing ontology can also be used in some other phases of the semiautomatic ontology construction process, such as ontology evaluation or ontology re nement (phases 5 and 6, Section 2.4), for instance, via associating new instances to the existing ontology in a process called ontology grounding (Jakulin and Mladenic, 2005). In the case of topic ontologies (see 7), where the concepts correspond to topics and documents are linked to these topics through an appropriate relation such as hasSubject (Grobelnik and Mladenic 2005a), one can use the Web to collect documents on a prede ned topic. In Knowledge Discovery, the approaches dealing with collecting documents based on the Web data are referred in the literature under the name Focused Crawling (Chakrabarti, 2002; Novak, 2004b). The main idea of these approaches is to use the initial seed information given by the user to nd similar documents by exploiting (1) background knowledge (ontologies, existing document taxonomies, etc.), (2) web topology (following hyperlinks from the relevant pages), and (3) document repositories (through search engines). The general assumption for most of the focused crawling methods is that pages with more closely related content are more inter-connected. In the cases where this assumption is not true (or we cannot reasonably assume it), we can still use the methods for selecting the documents through search engine querying (Ghani et al., 2005). In general, we could say that focused crawling serves as a generic technique for collecting data to be used in the next stages of data processing, such as constructing (ontology learning scenario 4 in Section 2.5) and populating ontologies (ontology learning scenario 3 in Section 2.5).
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2.6.5. Data Visualization
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Visualization of data in general and also visualization of document collections is a method for obtaining early measures of data quality, content, and distribution (Fayyad et al., 2001). For instance, by applying document visualization it is possible to get an overview of the content of a Web site or some other document collection. This can be useful especially for the rst phases of semi-automatic ontology construction aiming at domain and data understanding (see Section 2.4). Visualization can be also used for visualizing an existing ontology or some parts thereof, which is potentially relevant for all the ontology learning scenarios de ned in Section 2.5. One general approach to document collection visualization is based on clustering of the documents (Grobelnik and Mladenic, 2002) by rst representing the documents as word-vectors and performing k-means clustering on them (see Subsection 2.6.1). The obtained clusters are then represented as nodes in a graph, where each node in the graph is described by the set of most characteristic words in the
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Figure 2.1 An example output of a system for graph-based visualization of document collection. The documents are 1700 descriptions of European research projects in information technology (5FP IST).
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corresponding cluster. Similar nodes, as measured by their cosinesimilarity (Equation (2.2)), are connected by a link. When such a graph is drawn, it provides a visual representation of the document set (see Figure 2.1 for an example output of the system). An alternative approach that provides different kinds of document corpus visualization is proposed in Fortuna et al., 2005b). It is based on Latent Semantic Indexing, which is used to extract hidden semantic concepts from text documents and multidimensional scaling which is used to map the high dimensional space onto two dimensions. Document visualization can be also a part of more sophisticated tasks, such as generating a semantic graph of a document or supporting browsing through a news collection. For illustration, we provide two examples of document visualization that are based on Knowledge Discovery methods (see Figure 2.2 and Figure 2.3). Figure 2.2 shows an example of visualizing a single document via its semantic graph (Leskovec et al., 2004). Figure 2.3 shows an example of visualizing news stories via visualizing relationships between the named entities that appear in the news stories (Grobelnik and Mladenic, 2004).
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