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show only URIs of the related class. Where properties have literal types (e.g., string) as their range, QuizRDF enables users to query against these properties. So for example the class Painting has a property hasTechnique with range of type string . If a set of documents has been returned of class Painting, a user could enter oil on canvas as a value for this property and the document list would be ltered to show only those documents which have the value oil on canvas for this property. Thus QuizRDF has two retrieval channels: a keyword query against the text and a much more focussed query against speci c RDF properties, as well as supporting ontology browsing. Searching the full-text of the documents ensures the desired high recall in the initial stages of the information-seeking process. In the later stages of the search, more emphasis can be put on searching the RDF annotations (property values) to improve the precision of the search. Experiments with QuizRDF on an RDF-annotated web site showed improvements in performance as compared to a conventional keywordbased search facility (Iosif et al., 2003).

Bayes theorem is stated here although, as we have seen, problems involving it can be done geometrically

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Although message data is heavily dependent on the type of the message, navigation around the Message Store is a generic requirement and so the TMsvEntry and CMsvEntry classes provide generic ways of navigating around the message store. A TMsvEntry object contains some standard pieces of information about a message, but it also contains the index identi er for the entry. Many of the MTM methods apply to a current context, that is, a currently selected message entry that corresponds to a TMsvEntry object and a CMsvEntry object, and obviously all non-static TMsvEntry and CMsvEntry methods apply to speci c message entries. Not all entries in the Message Store are messages. Message folders, such as the Inbox, Outbox, Drafts folder and Sent Messages folder, are also entries, as are attachments. In these cases they are entries that have child entries (the messages themselves). Message services are also entries, but we do not need to examine them in this chapter. Some entries have other entries as children (folder entries have message entries as children, and message entries may have attachments as children), and the Message Store can be navigated either as a tree of entries or directly to a desired entry. The index identi er in each TMsvEntry object is a unique identi er that identi es each entry. There are some standard identi ers that are de ned by constants. These are de ned in msvids.h and include: KMsvNullIndexEntryId for a null, unused, entry

As discussed above, conventional search engines have no model of how the concepts denoted by query terms may be linked semantically. When searching for a paper published by a particular author, for example it may be helpful to retrieve additional information that relates to that author, such as other publications, curriculum vitae, contact details etc. A number of search engines are now emerging that use techniques to apply ontology-based domain-speci c knowledge to the indexing, similarity evaluation, results augmentation and query enrichment processes. Rocha et al. (2004) describe a search architecture that applies a combination of spread activation and conventional information retrieval to a domain-speci c semantic model in order to nd concepts relevant to a keyword-based query. The essence of spread activation, as applied in conventional textual searching, is that a document may be returned by a query, even if it contains none of the query keywords. This happens if the document is linked to by many other documents which do contain the keywords. The inference is that such a document will very likely be relevant to the query, despite not possessing the keywords. In the case described here, the user expresses his query as a set of keywords. This query is forwarded to a conventional search engine which assigns a score to each document in its index in the usual way. In addition to a conventional index, the system contains a domainspeci c knowledge base. This knowledge base consists of a model of the domain, with instance nodes pointing to web resources, each node having additional data in the form of linked properties as speci ed in the domain model (ontology). Weightings that express the strength of

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