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8.2.2. Role of Semantic Technology
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Semantic technology has the potential to offer solutions to many of the limitations described above, by providing enhanced knowledge access based on the exploitation of machine-processable metadata. Central to the vision of the Semantic Web are ontologies. These facilitate knowledge sharing and reuse between agents, be they human or arti cial. They offer this capability by providing a consensual and formal conceptualisation of a given domain. Information can then be annotated with respect to an ontology. This leads to distributed, heterogeneous information sources being uni ed through a machine-processable common domain model (ontology). Ontologies are populated with semantic metadata as discussed in more detail in 3. The PROTON ontology itself is introduced and discussed in 7. Search engines based on conventional information retrieval techniques alone tend to offer high recall but lower precision. The user is faced with too many results and many results that are irrelevant due to a failure to handle polysemy and synonymy, still less any richer semantic relations. As we will exemplify later in this chapter, the use of ontologies and associated metadata can allow the user to more precisely express their queries thus avoiding the problems identi ed above. Users can choose ontological concepts to de ne their query or select from a set of returned concepts following a search in order to re ne their query. They can specify queries over the metadata and indeed combine these with full text queries if desired. Furthermore, the use of semantic web technology offers the prospect of a more fundamental change to knowledge access. Current technology supports a process wherein the user attempts to frame an information need by specifying a query in the form of either a set of keywords or a piece of natural language text. Having submitted a query, the user is then presented with a ranked list of documents of relevance to the query. However, this is only a partial response to the user s actual requirement which is for information rather than lists of documents. It is suggested here, therefore, that the future of search engines lies in supporting more of the information management process, as opposed to
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seeking incremental and modest improvements to relevance ranking of documents. In this approach, software supports more of the process of analysing relevant documents rather than merely listing them and leaving the rest of the information analysis task to the user. Corporate knowledge workers need information de ned by its meaning, not by text strings ( bags of words ). They also need information relevant to their interests and to their current context. They need to nd not just documents, but sections and information entities within documents and even digests of information created from multiple documents. As described below, the exploitation of metadata and ontological information can offer this information-centric approach, as opposed to the prevailing document-centric technology.
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8.2.3. Searching XML
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The eXtensible Mark-up Language (XML), a speci cation for machinereadable documents, is one of the rst steps towards a Semantic Web. XML is a meta-language, as such, it provides a mechanism for representing other languages in a standardised way. XML mark-up describes (and prescribes) a document s data layout and structure as a tree of nested tags. XML-based search engines exploit this mark-up, enabling searches for documents where keywords and phrases appear within the elements of an XML document, for example search for the phrase Semantic Web within all htitlei elements of a set of XML documents. In an early implementation of XML-aware search, QuizXML (Davies, 2000) compiles a list of the tags that annotate and subdivide the documents within which document terms are found. QuizXML then creates a ner-grained index than traditional search engines: its index maps keywords to both the documents and the XML tags within which those keywords are found. QuizXML allows users to explore interactively the list of tags in which a given query occurs, and select a particular tag in order to re ne the search results to only those documents where the search query occurs in a part of an XML document marked up by the selected tag. In a more sophisticated approach, described in Cohen et al. (2003), the XSearch semantic search engine has been designed to return semantically-related document fragments in response to a user s query (in preference to returning a reference to the complete document). This is particularly useful in cases where a large document contains information in addition to that which matches the query, but which is not necessarily related to the query. XSearch provides a simple query interface that does not require a detailed knowledge of the structure of the XML documents being sought. The XSearch query syntax enables the user to specify how query terms must be related to the XML tags (but does not enforce this indeed, a query containing only keywords may be entered).
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