Abstract: This paper describes the retrieval approach proposed by the SIG/EVI group of the IRIT research centre at INEX’2006. This XML approach is based on direct contribution of the components constituting an information need. This paper focuses on the impact of changes between INEX’2005 and INEX’2006 notably the corpus change. This paper describes the search engine configurations and evolutions resulting from training on previous INEX testbeds and used to participate to INEX’2006. It presents also the results of the official experiments carried out at INEX’2006 and additional results.
Abstract: This paper describes the retrieval approach proposed by the SIG/EVI group of the IRIT research centre at INEX’2005. This XML approach is based on direct contribution of the components constituting an information need. This paper focuses on the method evolutions since previous participation to INEX. It describes the official experiments done for each subtasks with the corresponding results and additional unofficial experiments.
Abstract: Common search engines process users’ queries (i.e., information needs) by retrieving documents from pre-built term-based indexes. For digital libraries, such approaches are limited regarding particular contexts, such as specialized collections (e.g., cultural heritage collections) or specific retrieval criteria (e.g., multidimensional criteria). In this paper, we consider Information Retrieval systems exploiting geographic dimensions: spatial, temporal, and topical dimensions. Our contribution is twofold as we propose a Geographic Information Retrieval system evaluation framework and test the following hypothesis: combining spatial and temporal dimensions along with the topical dimension improves the effectiveness of Information Retrieval systems.
Abstract: This paper introduces a new approach of query reuse in order to help the user to retrieve relevant informa-tion. Past search experiences are a source of information that can be useful for a user trying to find informa-tion answering his information need. For example, a user searching about a new subject can benefit from past search experiences carried out by previous users about the same subject. The approach presented in this paper is based on collecting the different search attempts submitted to a search engine by a user trying to fulfil an information need. This approach takes mainly advantage of implicit links that exist between the dif-ferent search attempts that try to satisfy a single information need. Search experiences are modelled accord-ing to the concepts defined in the domain of version management. This modelling provides multiple possi-bilities to reuse past experiences notably to recommend terms for query reformulation or documents judged relevant by other users.
Abstract: This paper explores information retrieval system variability and takes advantage of the fact two systems can retrieve different documents for a given query. More precisely, our approach is based on data fusion (fusion of system results) by taking into account local performances of each system. Our method considers the relevance of the very first documents retrieved by different systems and from this information selects the system that will perform the retrieval for the user. We found that this principle improves the performances of about 9%. Evaluation is based on different years of TREC evaluation program (TREC 3, 5, 6 and 7), TREC-adhoc tracks. It considers the two and five best systems that participate to TREC the corresponding year.
Abstract: XML usage is growing to describe documents. Consequently, systems to search in XML collections are necessary. Various proposals of systems for XML retrieval intend to provide solutions to handle XML documents. This paper describes an XML approach based on direct contribution of the components constituting an information need. The search engine is largely configurable in order to be adapted to different context of search. Beyond being globally adapted to a collection of documents an important objective is to define a search engine that can be adapted to different retrieval scenarios and to identify how to adapt it. This paper presents first experiments on INEX testbeds that show how the engine can be adapted to better respond to different retrieval scenarios.
