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the ontology has been used in the interaction, that is which elements have been queried, which paths have been navigated, etc. By tracking the users interactions with the application in a log le, it is possible to collect useful information that can be used to assess the main interests of the users. In this way, we are able to obtain implicit feedback and to extract ontology change requirements to improve the interaction with the application. Evolution Management: The process of ontology evolution is supported by the evolution management infrastructure. The rst important aspect is the discovery of changes. While in some cases changes to the ontology may be requested explicitly, the actual challenge is to obtain and to examine the nonexplicit but available knowledge about the needs of the end-users. This can be done by analyzing various data sources related to the content that is described using the ontology. It can also be done by analyzing the end-user s behavior which leads to information about her likes, dislikes, preferences or the way she behaves. Based on the analysis of this information, suggested ontology changes can be made to the knowledge worker. This results in an ontology better suited to the needs of end-users. In the following sections, we will discuss the possibility of continuous ontology improvement by semi-automatic discovery of such changes, that is data-driven and usage-driven ontology evolution.
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Since many real-world data sets tend to be highly dynamic, ontology management systems have to deal with potential inconsistencies between the knowledge modeled by ontologies and the knowledge given by the underlying data. Data-driven change discovery targets this problem by providing methods for automatic or semi-automatic adaptation of ontologies according to modi cations being applied to the underlying data set. Suppose, for example, a user wants to nd information about the SEKT project. When searching for SEKT (as a search string) with a typical search engine he will probably nd a lot of pages, mostly about sparkling wine (since this is the most common meaning of the word SEKT in German), which are not relevant with respect to his actual information need. Given a more sophisticated semantically enhanced search engine he would have several ways of specifying the semantics of what he wants to nd:  Ontology-based searching: The user selects the concept Project from a domain ontology which might have been manually constructed or (semi-)automatically learned from the document base. Then he searches for SEKT as an instance of that concept. The search engine
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examines the ontological metadata which has previously been added to the content of each document in order to nd those documents which are most likely to be relevant to his query.  Topic hierarchy/browsing: Suppose a hierarchy of topics, one of which is The SEKT project, is used to classify a corpus of documents. The classi cation of the documents could, for example, have been done automatically based on ontological knowledge extracted from the documents. The user can choose the topic in which he is interested, in this case The SEKT Project, from the topic hierarchy.  Contextualized search: The user simply searches for SEKT and the system concludes from his semantic user pro le and his current working context that he is looking for information about a certain (research) project. Of course, having found some relevant documents the user s information need is not yet satis ed completely, but the number of documents he has to read to nd the relevant information about the SEKT project has decreased signi cantly. Nevertheless, depending on his query and the size of the document base some hundreds of documents might be left. Ontology learning algorithms can be used to provide the user with an aggregated view of the knowledge contained in these documents, showing the user the concepts, instances and relations which were extracted from the text. For this purpose a number of tools such as Text2Onto (Cimiano and Volker, 2005) are available which apply natural language processing as well as machine learning techniques in order to build ontologies in an automatic or semi-automatic fashion. Consider the following example: PROTON is a exible, lightweight upper level ontology that is easy to adopt and extend for the purposes of the tools and applications developed within [the] SEKT project (SEKT Deliverable D1.8.1). From the text fragment cited above you can conclude that SEKT is an instance of the concept project. It also tells you that PROTON is an instance of upper-level ontology, which in turn is a special kind of ontology. But such an ontology cannot only be used for browsing. It might also serve as a basis for document classi cation, metadata generation, ontology-based searching, and the construction of a semantic user pro le. All of these applications require a tight relationship between the ontology and the underlying data, that is the ontology must explicitly represent the knowledge which is more or less implicitly given by the document base. Therefore changes to the data should be immediately re ected by the ontology. Suppose now that the document base is extended, for example by focussed crawling, the inclusion of knowledge stored on the user s desktop or Peer-to-Peer techniques. In this case all ontologies which are affected by these changes have to be adapted in order to re ect the knowledge gained through the additional information available.
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