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The World Wide Web is one of the most widely used information resources. Understanding the web better will enable us to benefit more of it. In this thesis we develop techniques to learn the properties of the web pages like language and topic using only the ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. This identific ...
Document ranking for scientific publications involves a variety of specialized resources (e.g. author or citation indexes) that are usually difficult to use within standard general purpose search engines that usually operate on large-scale heterogeneous do ...
Ranking in scientific publication databases involves a variety of additional resources that are usually not applied in standard general purpose search engines. Moreover, community-specific expectations of users influence the perception of the adequacy of r ...
The suitability of peer-to-peer (P2P) approaches for full-text Web retrieval has recently been questioned because of the claimed unacceptable bandwidth consumption induced by retrieval from very large document collections. In this contribution we formalize ...
Efficient and effective search in large-scale data repositories requires complex indexing solutions deployed on a large number of servers. Web search engines such as Google and Yahoo! already rely upon complex systems to be able to return relevant query re ...
We describe a query-driven indexing framework for scalable text retrieval over structured P2P networks. To cope with the bandwidth consumption problem that has been identified as the major obstacle for full-text retrieval in P2P networks, we truncate posti ...
In this thesis, we explore the use of machine learning techniques for information retrieval. More specifically, we focus on ad-hoc retrieval, which is concerned with searching large corpora to identify the documents relevant to user queries. Thisidentifica ...
In this paper we present the AlvisP2P IR engine, which enables efficient retrieval with multi-keyword queries from a global document collection available in a P2P network. In such a network, each peer publishes its local index and invests a part of its loc ...
