Enriching Existing Test Collections with OXPath

Extending TREC-style test collections by incorporating external resources is a time consuming and challenging task. Making use of freely available web data requires technical skills to work with APIs or to create a web scraping program specifically tailored to the task at hand. We present a light-weight alternative that employs the web data extraction language OXPath to harvest data to be added to an existing test collection from web resources. We demonstrate this by creating an extended version of GIRT4 called GIRT4-XT with additional metadata fields harvested via OXPath from the social sciences portal Sowiport. This allows the re-use of this collection for other evaluation purposes like bibliometrics-enhanced retrieval. The demonstrated method can be applied to a variety of similar scenarios and is not limited to extending existing collections but can also be used to create completely new ones with little effort.Comment: Experimental IR Meets Multilinguality, Multimodality, and Interaction - 8th International Conference of the CLEF Association, CLEF 2017, Dublin, Ireland, September 11-14, 201

Overview of the INEX 2014 Interactive Social Book Search Track

Abstract. Users looking for books online are confronted with both pro-fessional meta-data and user-generated content. The goal of the Interac-tive Social Book Search Track was to investigate how users used these two sources of information, when looking for books in a leisure context. To this end participants recruited by four teams performed two different tasks using one of two book-search interfaces. Additionally one of the two interfaces also investigated whether user performance can be improved by providing a user-interface that supports multiple search stages.

LaHC at INEX 2014: Social Book Search Track

http://ceur-ws.org/Vol-1180/CLEF2014wn-Inex-HafsiEt2014.pdfInternational audienceIn the article, we describe our participation in the INEX 201 4 Social Book Search track. We present the different approaches expl oiting user social information such as reviews, tags and ratings. These social informations are as- signed by users to the books. We optimize our models using the INEX Social Book Search 2013 collection and we test them on the INEX 2014 S ocial Book Search track