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Very often effective presentations can be produced directly from the logical data model. However, sometimes the requirements go beyond the capabilities of declarative templates. For example it may be necessary to perform calculations or text processing. For this reason, the framework has a three tier, Model-View-Control architecture. The rst layer is the logical data model. The second layer contains the business logic which performs any necessary processing. The third and nal layer is the presentation layer where the data is transformed into a suitable format for presentation on the target device. This architecture addresses the separation of concerns issue discussed in Section In the current implementation of the DIWAF, device identi cation uses the RDF-based CC/PP (an open standard from W3C), with an open source Java implementation. In this framework, device pro le information is made available to Java servlets as a collection of attributes, such as screen size, browser name etc. These attributes can be used to inform the subsequent selection and adaptation of content, by combining them in Boolean expressions. Figure 8.4 shows exactly the same content (located
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at the same URL) rendered via DIWAF on a standard web browser and on a WAP browser emulator. We have used this framework to support delivery of knowledge to users on a variety of devices in the SEKTAgent system, as discussed later in this chapter. Further details of this approach are available in Glover and Davies (2005).
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We have seen in Section 8.2 how some semantic search tools use an ontological knowledge base to enhance their search capability. We discussed in Sections 8.3 and 8.4 the use of natural language generation to describe ontological knowledge in a more natural format and the delivery of knowledge to the user in a format appropriate to the terminal device to which they currently have access. In this section, we describe a semantic search agent, SEKTAgent, which brings together the exploitation of an ontological knowledge base, natural language generation and device independence to proactively deliver relevant information to its users. Search agents can reduce the overhead of completing a manual search for information. The best known commercial search agent is perhaps Google Alerts , based on syntactic queries of the Google index. Using an API provided by the KIM system (see Section 8.2.5 above), SEKTAgent allows users to associate with each agent a semantic query based upon the PROTON ontology (see 7). Some examples of agent queries that could be made would be for documents mentioning:  A named person holding a particular position, within a certain organisation.  A named organisation located at a particular location.  A particular person and a named location.  A named company, active in a particular industry sector. This mode of searching for types of entity can be complemented with a full text search, allowing the user to specify terms which should occur in the text of the retrieved documents. In addition to the use of subsumption reasoning provided by KIM, it is also planned that SEKTAgent will incorporate the use of explicitly de ned domain-speci c rules. The SEKT search tool uses KAON29 as its reasoning engine. KAON2 is an infrastructure for managing OWL-DL ontologies. It provides an API for the programmatic management of OWL-DL and an inference engine for answering conjunctive queries expressed using SPARQL10 syntax.
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KAON2 allows new knowledge to be inferred from existing, explicit knowledge with the application of rules over the ontology. Consider a semantic query to determine who has collaborated with a particular author on a certain topic. This query could be answered through the existence of a rule of the form: If ( personX isAuthorOf document) & ( personY isAuthorOf document) -> ( personX collaboratesWith personY) & ( personY collaboratesWith personX) This rule states that if two people are authors of the same document then they are collaborators. When a query involving the collaborateswith predicate is submitted to KAON2, the above rule is enforced and the resulting inferred knowledge returned as part of the query. Figure 8.5 illustrates the results page for an agent which is searching for a person named Ben Verwaayen within the organisation BT . SEKTAgent is automatically run of ine11 at a periodicity speci ed by the user (daily, weekly etc.). When new results (i.e. ones not previously
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