Advances in Information Retrieval
Description
This book constitutes the refereed proceedings of the 29th annual European Conference on Information Retrieval Research, ECIR 2007, held in Rome, Italy in April 2007.
The 42 revised full papers and 19 revised short papers presented together with 3 keynote talks and 21 poster papers were carefully reviewed and selected from 220 article submissions and 72 poster paper submissions. The papers are organized in topical sections on theory and design, efficiency, peer-to-peer networks, result merging, queries, relevance feedback, evaluation, classification and clustering, filtering, topic identification, expert finding, XML IR, Web IR, and multimedia IR.
Written for: Researchers and professionals
Keywords: IR, Web query mining, Web search, XML retrieval, classification, clustering, collaborative Web searches, collaborative filtering, cross-language retrieval, distributed IR, document retrieval, image retrieval, information extraction, information retrieval, multimedia retrieval, question answering, results merging, semantic orientation, similarity search, text mining
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The Next Generation Web Search and the Demise of the Classic IR Model (p. 19)
Abstract. The classic IR model assumes a human engaged in activity that generates an "information need". This need is verbalized and then expressed as a query to search engine over a defined corpus. In the past decade, Web search engines have evolved from a first generation based on classic IR algorithms scaled to web size and thus supporting only informational queries, to a second generation supporting navigational queries using web specific information (primarily link analysis), to a third generation enabling transactional and other "semantic" queries based on a variety of technologies aimed to directly satisfy the unexpressed "user intent", thus moving further and further away from the classic model.
What is coming next? In this talk, we identify two trends, both representing "short-circuits" of the model: The first is the trend towards context driven Information Supply (IS), that is, the goal of Web IR will widen to include the supply of relevant information from multiple sources without requiring the user to make an explicit query. The information supply concept greatly precedes information retrieval, what is new in the web framework, is the ability to supply relevant information specific to a given activity and a given user, while the activity is being performed.
Thus the entire verbalization and query-formation phase are eliminated. The second trend is "social search" driven by the fact that the Web has evolved to being simultaneously a huge repository of knowledge and a vast social environment. As such, it is often more e.ective to ask the members of a given web milieu rather than construct elaborate queries. This short-circuits only the query formulation, but allows information finding activities such as opinion elicitation and discovery of social norms, that are not expressible at all as queries against a fixed corpus.
The Last Half-Century: A Perspective on Experimentation in Information Retrieval
Abstract. The experimental evaluation of information retrieval systems has a venerable history. Long before the current notion of a search engine, in fact before search by computer was even feasible, people in the library and information science community were beginning to tackle the evaluation issue. Sometimes it feels as though evaluation methodology has become fixed (stable or frozen, according to your viewpoint). However, this is far from the case. Interest in methodological questions is as great now as it ever was, and new ideas are continuing to develop. This talk will be a personal take on the field.
Learning in Hyperlinked Environments
Abstract. A remarkable number of important problems in different domains (e.g. web mining, pattern recognition, biology . . . ) are naturally modeled by functions de.ned on graphical domains, rather than on traditional vector spaces. Following the recent developments in statistical relational learning, in this talk, I introduce Diffusion Learning Machines (DLM) whose computation is very much related to Web ranking schemes based on link analysis. Using arguments from function approximation theory, I argue that, as a matter of fact, DLM can compute any conceivable ranking function on the Web.
