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The KIM architecture also facilitates the indexing and retrieval of documents with respect to particular named entities such as people, companies, organisations and locations. Formal background knowledge (held in the KIM ontological knowledgebase) can be linked to the entities identi ed in Web documents as described in 3. The knowledge base can host two types of entity knowledge: pre-populated descriptions and knowledge acquired from trusted sources, and automatically extracted descriptions derived through knowledge discovery and acquisition methods such as data mining. The KIM platform enables the ontology to be extended and knowledge base to be populated to meet the domainspeci c needs of a semantic annotation application. The semantic annotation process in KIM assigns to the named entities in the text links to semantic descriptions of those entities in the ontological knowledgebase. The semantic descriptions provide both class and instance information about the entities referred to in the documents. KIM analyses the text, recognises references to named entities and matches the reference with a known entity from the knowledgebase (i.e. an ontological instance). The reference in the document is annotated with the URI of the entity. In 3, Figure 3.2 shows the way in which a segment of text concerning a Bulgarian company might be associated with a number of entities in the ontological knowledge base.
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Figure 8.2 Semantic querying in KIM.
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The semantic annotations can then be used for indexing and retrieval, categorisation, visualisation and smooth traversal between unstructured text and available relevant knowledge. The application of entity coreference resolution means that the system would regard the strings Tony Blair Mr Blair the Prime Minister as referring to the same entity in the ontological knowledge base. Semantic querying is supported against the repository of semantically annotated documents. This would allow for example a query to be formulated that targets all documents that refer to Persons that hold a speci ed Position within an Organisation. Figure 8.2 shows a KIM ontological query concerning a person whose name begins with J , and who is a spokesman for IBM. Note that the user interface shown is for a speci c query type (regarding people with speci c positions in named organisations). A more general interface is also available, allowing the speci cation of queries about any type of entity, relations between such entities and required attribute values (e.g. nd all documents referring to a Person that hasPosition CEO within a Company, locatedIn a Country with name UK ). To answer the query, KIM applies the semantic restrictions over the entities in the knowledge base. The resulting set of entities is matched against the semantic index and the referring documents are retrieved with relevance ranking according to these named entities. Figure 8.3 Shows that four such ontological entities have been found in the documents indexed. It is then possible to browse a list of documents containing the speci ed entities and KIM renders the documents, with entities from the query highlighted (in this example IBM and the identi ed spokesperson).
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Figure 8.3 Semantic query results.
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It should be noted that the work surveyed here is not claimed to be comprehensive, but indicative of the research being carried out in a large number of groups worldwide. In other work, Berstein et al. (2005) describe a controlled language approach whereby a subset of English is entered by the user as a query and is then mapped into a semantic query via a discourse representation structure. Vallet et al. (2005) propose an ontology-based information retrieval model using a semantic indexing scheme based on annotation weighting techniques.
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8.2.6. Searching for Semantic Web Resources
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We have seen in the earlier sections a variety of approaches for searching semantically annotated information resources. The Swoogle search engine (Ding et al., 2004) is tackling a related but different problem: it is primarily concerned with nding ontologies and related instance data. Finding ontologies is seen as important to avoid the creation of new ontologies where serviceable ones already exist. It is hoped that this approach will lead to the emergence of widely-used canonical ontologies. Swoogle supports querying for ontologies containing speci ed terms. This can be re ned to nd ontologies where such terms occur as classes or properties, or to nd ontologies that are in some sense about the speci ed term (as determined by Swoogle s ontology retrieval engine). The ontologies thus found are ranked according to Swoogle s OntologyRank algorithm which attempts to measure the degree to which a given ontology is used.
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