Knowledge-based document retrieval with application to TEXPROS

Document retrieval in an information system is most often accomplished through keyword search. The common technique behind keyword search is indexing. The major drawback of such a search technique is its lack of effectiveness and accuracy. It is very common in a typical keyword search over the Internet to identify hundreds or even thousands of records as the potentially desired records. However, often few of them are relevant to users\u27 interests. This dissertation presents knowledge-based document retrieval architecture with application to TEXPROS. The architecture is based on a dual document model that consists of a document type hierarchy and, a folder organization. Using the knowledge collected during document filing, the search space can be narrowed down significantly. Combining the classical text-based retrieval methods with the knowledge-based retrieval can improve tremendously both search efficiency and effectiveness. With the proposed predicate-based query language, users can more precisely and accurately specify the search criteria and their knowledge about the documents to be retrieved. To assist users formulate a query, a guided search is presented as part of an intelligent user interface. Supported by an intelligent question generator, an inference engine, a question base, and a predicate-based query composer, the guided search collects the most important information known to the user to retrieve the documents that satisfy users\u27 particular interests. A knowledge-based query processing and search engine is presented as the core component in this architecture. Algorithms are developed for the search engine to effectively and efficiently retrieve the documents that match the query. Cache is introduced to speed up the process of query refinement. Theoretical proof and performance analysis are performed to prove the efficiency and effectiveness of this knowledge-based document retrieval approach

A knowledge engineering framework for intelligent retrieval of legal case studies

International audienceJuris-Data is one of the largest case-study base in France. The case studies are indexed by legal classification elaborated by the Juris-Data Group. Knowledge engineering was used to design an intelligent interface for information retrieval based on this classification. The aim of the system is to help users find the case-study which is the most relevant to their own. The approach is potentially very useful, but for standardising it for other legal document bases, it is necessary to extract a legal classification of the primary documents. Thus, a methodology for the construction of these classifications was designed together with a framework for index construction. The project led to the implementation of a Legal Case Studie, based on the accumulated experimentation and the methodologies designed. It consists of a set of computerised tools which support the life-cycle of the legal document from their processing by legal experts to their consultation by clients

Information retrieval evaluation in knowledge acquisition tasks

The Cranfield Paradigm is a widely adopted and the de-facto standard approach to the evaluation of IR systems. However, this approach does not inherently support situations in which the user is acquiring knowledge (is learning) during an information seeking session consisting of the submission of a sequence of queries into an information retrieval system. More specifically, during a situation in which the retrieval of a particular document at the beginning of a session can be considered not relevant (due to the user's lack of knowledge), while it can be considered relevant at a later point in the session (once the user acquired all required prerequisite knowledge). In this position paper, we reflect on the limitations of the Cranfield Paradigm in the context of knowledge acquisition tasks and propose several alternatives. These alternatives are based on the notion of evaluating a session consisting of a sequence of individual queries created to address a specific information need as part of a knowledge acquisition task