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Figure 2.2 Visual representation of an automatically generated summary of a news story about earthquake. The summarization is based on deep parsing used for obtaining semantic graph of the document, followed by machine learning used for deciding which parts of the graph are to be included in the document summary.
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Figure 2.3 Visual representation of relationships (edges in the graph) between the named entities (vertices in the graph) appearing in a collection of news stories. Each edge shows intensity of comentioning of the two named entities. The graph is an example focused on the named entity Semantic Web that was extracted from the 11.000 ACM Technology news stories from 2000 to 2004.
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2.7. RELATED WORK ON ONTOLOGY CONSTRUCTION
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Different approaches have been used for building ontologies, most of them to date using mainly manual methods. An approach to building ontologies was set up in the CYC project (Lenat and Guha, 1990), where the main step involved manual extraction of common sense knowledge from different sources. There have been some methodologies for building ontologies developed, again assuming a manual approach. For instance, the methodology proposed in (Uschold and King, 1995) involves the following stages: identifying the purpose of the ontology (why to build it, how will it be used, the range of the users), building the ontology, evaluation and documentation. Building of the ontology is further divided into three steps. The rst is ontology capture, where key concepts and relationships are identi ed, a precise textual de nition of them is written, terms to be used to refer to the concepts and relations are identi ed, the involved actors agree on the de nitions and terms. The second step involves coding of the ontology to represent the de ned conceptualization in some formal language (committing to some meta-ontology, choosing a representation language and coding). The third step involves possible integration with existing ontologies. An overview of methodol ogies for building ontologies is provided in Fernandez (1999), where several methodologies, including the above described one, are presented and analyzed against the IEEE Standard for Developing Software Life Cycle Processes, thus viewing ontologies as parts of some software product. As there are some speci cs to semi-automatic ontology construction compared to the manual approaches to ontology construction, the methodology that we have de ned (see Section 2.4) has six phases. If we relate them to the stages in the methodology de ned in Uschold and King (1995), we can see that the rst two phases referring to domain and data understanding roughly correspond to identifying the purpose of the ontology, the next two phases (tasks de nition and ontology learning) correspond to the stage of building the ontology, and the last two phases on ontology evaluation and re nement correspond to the evaluation and documentation stage. Several workshops at the main Arti cial Intelligence and Knowledge Discovery conferences (ECAI, IJCAI, KDD, ECML/PKDD) have been organized addressing the topic of ontology learning. Most of the work presented there addresses one of the following problems/ tasks:  Extending the existing ontology: Given an existing ontology with concepts and relations (commonly used is the English lexical ontology WordNet), the goal is to extend that ontology using some text, for example Web documents are used in (Agirre et al., 2000). This can t under the ontology learning scenario 5 in Section 2.5.
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 Learning relations for an existing ontology: Given a collection of text documents and ontology with concepts, learn relations between the concepts. The approaches include learning taxonomic, for example isa, (Cimiano et al., 2004) and nontaxonomic, for example hasPart relations (Maedche and Staab, 2001) and extracting semantic relations from text based on collocations (Heyer et al., 2001). This ts under the ontology learning scenario 2 in Section 2.5.  Ontology construction based on clustering: Given a collection of text documents, split each document into sentences, parse the text and apply clustering for semi-automatic construction of an ontology (Bisson et al., 2000; Reinberger and Spyns, 2004). Each cluster is labeled by the most characteristic words from its sentences or using some more sophisticated approach (Popescul and Ungar, 2000). Documents can be also used as a whole, without splitting them into sentences, and guiding the user through a semi-automatic process of ontology construction (Fortuna et al., 2005a). The system provides suggestions for ontology concepts, automatically assigns documents to the concepts, proposed naming of the concepts, etc. In Hotho et al. (2003), the clustering is further re ned by using WordNet to improve the results by mapping the found sentence clusters upon the concepts of a general ontology. The found concepts can be further used as semantic labels (XML tags) for annotating documents. This ts under the ontology learning scenario 4 in Section 2.5.  Ontology construction based on semantic graphs: Given a collection of text documents, parse the documents; perform coreference resolution, anaphora resolution, extraction of subject-predicate-object triples, and construct semantic graphs. These are further used for learning summaries of the documents (Leskovec et al., 2004). An example summary obtained using this approach is given in Figure 2.2. This can t under the ontology learning scenario 4 in Section 2.5.  Ontology construction from a collection of news stories based on named entities: Given a collection of news stories, represent it as a collection of graphs, where the nodes are named entities extracted from the text and relationships between them are based on the context and collocation of the named entities. These are further used for visualization of news stories in an interactive browsing environment (Grobelnik and Mladenic, 2004). An example output of the proposed approach is given in Figure 2.3. This can t under the ontology learning scenario 4 in Section 2.5. More information on ontology learning from text can be found in a collection of papers (Buitelaar et al., 2005) addressing three perspectives: methodologies that have been proposed to automatically extract information from texts, evaluation methods de ning procedures and metrics for a quantitative evaluation of the ontology learning task, and application scenarios that make ontology learning a challenging area in the context of real applications.
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