Southampton and The Open University. Preface(9)

Advanced Knowledge Technologies is recognised as a leading research programme conducted at some of the foremost informatics departments in Britain. It is also a training ground for a new generation of researchers. To highlight the work of these students, a

This is done in order to download the resources containing RDF iteratively. To enable embedding in other tools, RDF Crawler provides a high-level programmable interface (Java API). Other work done in this field includes the Hackdiary 2

of downloading simultaneously from many sources while the aggregation thread does the processing. It RDF Crawler which is a multithreaded java implementation capable builds a model that remembers the provenance of the RDF and takes care to delete and replace triples if it hits the same URL twice. Hence the data is up-to-date all the times even after many runs. Our proposed work is different from the above mentioned in two ways, first and foremost our work is focussed in how to classify and categorize semantic data in-order to increase search precision. Secondly we propose a method for clustering semantic data and there is no reported work done in this area to date and is an open research topic. This process is better demonstrated by the experimental tool we developed the RDF Analyser as shown in Figure 2. The RDF Analyser is able to extract all information from structured data including information from anonymous nodes. In the next section we describe how information obtained from the semantic web can be used for reporting data, i.e. the ability for IE systems to be able to track changes in information over time and predict new information.

Fig. 2. RDF Analyser

3. Future Work

The Information Extraction systems E.G Armadillo3

information on the web by using multiple citations as a way of validating the data. The valid data is , work on the principle of utilizing the redundant then used to bootstrap the annotation process by using IE Annotation engines such as Amilcare (Ciravegna et al.). Hence producing machine-readable content for the Semantic Web i.e. Semantic Web Documents. Armadillo outputs RDF documents after crawling the web. Armadillo is currently able to learn over the HTML content of the World Wide Web. However if the IE system is able to learn from semantic data E.G RSS and XML feeds which are now increasingly being provided by most websites and implement a mechanism to track the changes information over this data. Say for example If an Article ‘A’ says that the cost of an Item ‘X’ is £ 300 in the year 1994 , and article ‘B’ says its cost has now risen to £ 350 in 1995 and article ‘C’ quotes that the cost of item ‘X’ is now £ 400 in 1996, now if the Intelligent System is able to track and match these changes in information , then it can successfully predict that the cost of the item ‘X’ in 1997 will be £ 450, after observing the trend. This is a simple example of what the system can aim to achieve. This concept can be used for more important tasks such as E-Commerce applications where the prices of goods need to be monitored at a regular basis or in the stock market where the stock quotes are updated frequently. If the system is able to give the user an idea of how the information might change in the near future after learning from changes in the past, then it can be of great help in the above mentioned areas. 23 /archives/000030.html http://nlp.shef.ac.uk/wig/armadillo_home.html

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