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In order to offer such search facilities, Swoogle builds an index of semantic web documents (de ned as web-accessible documents written in a semantic web language). A specialised crawler has been built using a range of heuristics to identify and index semantic web documents. The creators of Swoogle are building an ontology dictionary based on the ontologies discovered by Swoogle.
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8.2.7. Semantic Browsing
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Web browsing complements searching as an important aspect of information-seeking behaviour. Browsing can be enhanced by the exploitation of semantic annotations and below we describe three systems which offer a semantic approach to information browsing. Magpie (Domingue et al., 2004) is an internet browser plug-in which assists users in the analysis of web pages. Magpie adds an ontologybased semantic layer onto web pages on-the- y as they are browsed. The system automatically highlights key items of interest, and for each highlighted term it provides a set of services (e.g. contact details, current projects, related people) when you right click on the item. This relies, of course, on the availability of a domain ontology appropriate to the page being browsed. CS AKTiveSpace (Glaser et al., 2004) is a semantic web application which provides a way to browse information about the UK Computer Science Research domain, by exploiting information from a variety of sources including funding agencies and individual researchers. The application exploits a wide range of semantically heterogeneous and distributed content. AKTiveSpace retrieves information related to almost two thousand active Computer Science researchers and over 24 000 research projects, with information being contained within 1000 published papers, located in different university web sites. This content is gathered on a continuous basis using a variety of methods including harvesting publicly available data from institutional web sites, bulk translation from existing databases, as well as other data sources. The content is mediated through an ontology and stored as RDF triples; the indexed information comprises around 10 million RDF triples in total. CS AKTive Space supports the exploration of patterns and implications inherent in the content using a variety of visualisations and multidimensional representations to give uni ed access to information gathered from a range of heterogeneous sources. Quan and Karger (2004) describe Haystack, a browser for semantic web information. The system aggregates and visualises RDF metadata from multiple arbitrary locations. In this respect, it differs from the two semantic browsing systems described above which are focussed on using metadata annotations to enhance the browsing and display of the data itself.
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Presentations styles in Haystack are themselves described in RDF and can be issued by the content server or by context-speci c applications which may wish to present the information in a speci c way appropriate to the application at hand. Data from multiple sites and particular presentation styles can be combined by Haystack on the client-side to form customised access to information from multiple sources. The authors demonstrate a Haystack application in the domain of bioinformatics. In other work (Karger et al., 2003), it is reported that Haystack also incorporates the ability to generate RDF data using a set of metadata extractors from a variety of other formats, including documents in various formats, email, Bibtex les, LDAP data, RSS feeds, instant messages and so on. In this way, Haystack has been used to produce a uni ed Personal Information Manager. The goal is to eliminate the partitioning which has resulted from having information scattered between e-mail client(s), lesystem, calendar, address book(s), the Web and other custom repositories.
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8.3. NATURAL LANGUAGE GENERATION FROM ONTOLOGIES
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Natural Language Generation (NLG) takes structured data in a knowledge base as input and produces natural language text, tailored to the pre-sentational context and the target reader (Reiter and Dale, 2000). NLG techniques use and build models of the context, and the user and use them to select appropriate presentation strategies, for example to deliver short summaries to the user s WAP phone or a longer multimodal text to the user s desktop PC. In the context of the semantic web and knowledge management, NLG is required to provide automated documentation of ontologies and knowledge bases. Unlike human-written texts, an automatic approach will constantly keep the text up-to-date which is vitally important in the semantic web context where knowledge is dynamic and is updated frequently. The NLG approach also allows generation in multiple languages without the need for human or automatic translation (see (Aguado et al., 1998)). Generation of natural language text from ontologies is an important problem. Firstly, because textual documentation is more readable than the corresponding formal notations and thus helps users who are not knowledge engineers to understand and use ontologies. Secondly, a number of applications have now started using ontologies for knowledge representation, but this formal knowledge needs to be expressed in natural language in order to produce reports, letters etc. In other words, NLG can be used to present structured information in a userfriendly way.
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