Advances in Focused Retrieval 7th International Workshop of the Initiative for the Evaluation of XML Retrieval, INEX 2008, Dagstuhl Castle, Germany, December 15-18, 2009. Revised and Selected Papers

I write with pleasurethis forewordto the proceedings of the 7th workshopof the Initiative for the Evaluation of XML Retrieval (INEX). The increased adoption of XML as the standard for representing a document structure has led to the development of retrieval systems that are aimed at e?ectively accessing XML documents. Providing e?ective access to large collections of XML documents is therefore a key issue for the success of these systems. INEX aims to provide the necessary methodological means and worldwide infrastructures for evaluating how good XML retrieval systems are. Since its launch in 2002, INEX has grown both in terms of number of p- ticipants and its coverage of the investigated retrieval tasks and scenarios. In 2002, INEX started with 49 registered participating organizations, whereas this number was more than 100 for 2008. In 2002, there was one main track, c- cerned with the ad hoc retrieval task, whereas in 2008, seven tracks in addition to the main ad hoc track were investigated, looking at various aspects of XML retrieval, from book search to entity ranking, including interaction aspects.

Ad Hoc Track.- Overview of the INEX 2008 Ad Hoc Track.- Experiments with Proximity-Aware Scoring for XML Retrieval at INEX 2008.- Finding Good Elements for Focused Retrieval.- New Utility Models for the Garnata Information Retrieval System at INEX’08.- UJM at INEX 2008: Pre-impacting of Tags Weights.- Use of Multiword Terms and Query Expansion for Interactive Information Retrieval.- Enhancing Keyword Search with a Keyphrase Index.- CADIAL Search Engine at INEX.- Indian Statistical Institute at INEX 2008 Adhoc Track.- Using Collectionlinks and Documents as Context for INEX 2008.- SPIRIX: A Peer-to-Peer Search Engine for XML-Retrieval.- Book Track.- Overview of the INEX 2008 Book Track.- XRCE Participation to the Book Structure Task.- University of Waterloo at INEX 2008: Adhoc, Book, and Link-the-Wiki Tracks.- The Impact of Document Level Ranking on Focused Retrieval.- Adhoc and Book XML Retrieval with Cheshire.- Book Layout Analysis: TOC Structure Extraction Engine.- The Impact of Query Length and Document Length on Book Search Effectiveness.- Efficiency Track.- Overview of the INEX 2008 Efficiency Track.- Exploiting User Navigation to Improve Focused Retrieval.- Efficient XML and Entity Retrieval with PF/Tijah: CWI and University of Twente at INEX’08.- Pseudo Relevance Feedback Using Fast XML Retrieval.- TopX 2.0 at the INEX 2008 Efficiency Track.- Aiming for Efficiency by Detecting Structural Similarity.- Entity Ranking Track.- Overview of the INEX 2008 Entity Ranking Track.- L3S at INEX 2008: Retrieving Entities Using Structured Information.- Adapting Language Modeling Methods for Expert Search to Rank Wikipedia Entities.- Finding Entities in Wikipedia Using Links and Categories.- Topic Difficulty Prediction in Entity Ranking.- A Generative Language Modeling Approach for Ranking Entities.- Interactive Track.- Overview of the INEX 2008 Interactive Track.- Link the Wiki Track.- Overview of the INEX 2008 Link the Wiki Track.- Link-the-Wiki: Performance Evaluation Based on Frequent Phrases.- CMIC@INEX 2008: Link-the-Wiki Track.- Stealing Anchors to Link the Wiki.- Context Based Wikipedia Linking.- Link Detection with Wikipedia.- Wikisearching and Wikilinking.- CSIR at INEX 2008 Link-the-Wiki Track.- A Content-Based Link Detection Approach Using the Vector Space Model.- XML Mining Track.- Overview of the INEX 2008 XML Mining Track.- Semi-supervised Categorization of Wikipedia Collection by Label Expansion.- Document Clustering with K-tree.- Using Links to Classify Wikipedia Pages.- Clustering XML Documents Using Frequent Subtrees.- UJM at INEX 2008 XML Mining Track.- Probabilistic Methods for Link-Based Classification at INEX 2008.- Utilizing the Structure and Content Information for XML Document Clustering.- Self Organizing Maps for the Clustering of Large Sets of Labeled Graphs.