Team Orpailleur

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Section: Scientific Foundations

Elements on Knowledge Systems and Semantic Web

Knowledge representation

is a process for representing knowledge within a knowledge representation formalism, giving knowledge units a syntax and a semantics. The semantic web is a framework for building knowledge-based systems for manipulating documents on the web by their contents, i.e. taking into account the semantics of the elements included in the documents.

Usually, people try to take advantage of the web by searching for information (navigation, exploration), and by querying documents using search engines (information retrieval). Then people try to analyze the obtained results, a task that may be very difficult and tedious. Nowadays, the web is becoming “semantic” in the sense that people search for information with the help of machines, that are in charge of asking questions, searching for answers, classifying and interpreting the answers. The web becomes a space for exchange of information between machines, allowing an “intelligent access” and “management” of information. However, a machine may be able to read, understand, and manipulate information on the web, if and only if the knowledge necessary for achieving those tasks is available. This is why ontologies are of main importance with respect to the task setting up a semantic web. Thus, there is a need for representation languages for annotating documents, i.e. describing the content of documents, and giving a semantics to this content. Knowledge representation languages are (the?) good candidates for achieving the task: they have a syntax with an associated semantics, and they can be used for retrieving information, answering queries, and reasoning.

Semantic web constitutes a good platform for experimenting ideas on knowledge representation, reasoning, and KDDK. In particular, the knowledge representation language associated with the semantic web is the OWL language, based on description logics (or DL [65] ). In OWL, knowledge units are represented within concepts (or classes), with attributes (properties of concepts, or relations, or roles), and individuals. The hierarchical organization of concepts (and relations) relies on a subsumption relation that is a partial ordering. The inference services are based on subsumption, concept and individual classification, two tasks related to “classification-based reasoning”. Concept classification is used for inserting a new concept at the right location in the concept hierarchy, searching for its most specific subsumers and its most general subsumees. Individual classification is used for recognizing the concepts an individual may be an instance of. Furthermore, classification-based reasoning may be extended into case-based reasoning (CBR), that relies on three main operations: retrieval, adaptation, and memorization. Given a target problem, retrieval consists in searching for a source (memorized) problem similar to the target problem. Then, the solution of the source problem is adapted to fulfill the constraints attached to the target problem. When there is enough interest, the target problem and its solution may be memorized in the case base to be reused. In the context of a concept hierarchy, retrieval and adaptation may be both based on classification and adaptation-guided retrieval [78] .


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