Lorince, J.; Joseph, K. & Todd, P. (2015), Analysis of Music Tagging and Listening Patterns: Do Tags Really Function as Retrieval Aids?, in Nitin Agarwal; Kevin Xu & Nathaniel Osgood, ed., 'Social Computing, Behavioral-Cultural Modeling, and Prediction' , Springer International Publishing, , pp. 141-152 .
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In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do

Jäschke, R. & Rudolph, S. (2013), Attribute Exploration on the Web, in Peggy Cellier; Felix Distel & Bernhard Ganter, ed., 'Contributions to the 11th International Conference on Formal Concept Analysis' , pp. 19--34 .
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2012

Gomes, D.; Cruz, D.; Miranda, J.; Costa, M. & Fontes, S. (2012), 'Creating a searchable web archive' , Technical report, Foundation for National Scientific Computing , Portugal .
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The web became a mass means of publication that has been replacing printed media. However, its information is extremely ephemeral. Currently, most of the information available on the web is less than 1 year old. There are several initiatives worldwide that struggle to archive information from the web before it vanishes. However, search mechanisms to access this information are still limited and do not satisfy their users that demand performance similar to live- web search engines. This paper presents some of the work developed to create an effi�cient and effective searchable web archive service, from data acquisition to user interface design. The results of research were applied in practice to create the Portuguese Web Archive that is publicly available since January 2010. It supports full-text search over 1 billion contents archived from 1996 to 2010. The developed software is available as an open source project.

2011

Poelmans, J.; Elzinga, P.; Viaene, S.; Dedene, G. & Kuznetsov, S. O. (2011), Text Mining Scientific Papers: a Survey on FCA-based Information Retrieval Research., in Petra Perner, ed., 'Industrial Conference on Data Mining - Poster and Industry Proceedings' , IBaI Publishing, , pp. 82--96 .
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Formal Concept Analysis (FCA) is an unsupervised clustering technique and many scientific papers are devoted to applying FCA in Information Retrieval (IR) research. We collected 103 papers published between 2003-2009 which mention FCA and information retrieval in the abstract, title or keywords. Using a prototype of our FCA-based toolset CORDIET, we converted the pdf-files containing the papers to plain text, indexed them with Lucene using a thesaurus containing terms related to FCA research and then created the concept lattice shown in this paper. We visualized, analyzed and explored the literature with concept lattices and discovered multiple interesting research streams in IR of which we give an extensive overview. The core contributions of this paper are the innovative application of FCA to the text mining of scientific papers and the survey of the FCA-based IR research.

2010

Croft, W. B.; Metzler, D. & Strohman, T. (2010), Search engines: information retrieval in practice , Addison-Wesley , Boston .
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2009

Dong, X.; Chen, X.; Guan, Y.; Yu, Z. & Li, S. (2009), An Overview of Learning to Rank for Information Retrieval., in Mark Burgin; Masud H. Chowdhury; Chan H. Ham; Simone A. Ludwig; Weilian Su & Sumanth Yenduri, ed., 'CSIE (3)' , IEEE Computer Society, , pp. 600-606 .
[BibTeX] [Endnote]

Ritchie, A. (2009), 'Citation context analysis for information retrieval' (744) , Technical report, University of Cambridge , Cambridge, UK .
[BibTeX] [Endnote]

This thesis investigates taking words from around citations to scientific papers in order to create an enhanced document representation for improved information retrieval. This method parallels how anchor text is commonly used in Web retrieval. In previous work, words from citing documents have been used as an alternative representation of the cited document but no previous experiment has combined them with a full-text document representation and measured effectiveness in a large scale evaluation. The contributions of this thesis are twofold: firstly, we present a novel document representation, along with experiments to measure its effect on retrieval effectiveness, and, secondly, we document the construction of a new, realistic test collection of scientific research papers, with references (in the bibliography) and their associated citations (in the running text of the paper) automatically annotated. Our experiments show that the citation-enhanced document representation increases retrieval effectiveness across a range of standard retrieval models and evaluation measures. In Chapter 2, we give the background to our work, discussing the various areas from which we draw together ideas: information retrieval, particularly link structure analysis and anchor text indexing, and bibliometrics, in particular citation analysis. We show that there is a close relatedness of ideas between these areas but that these ideas have not been fully explored experimentally. Chapter 3 discusses the test collection paradigm for evaluation of information retrieval systems and describes how and why we built our test collection. In Chapter 4, we introduce the ACL Anthology, the archive of computational linguistics papers that our test collection is centred around. The archive contains the most prominent publications since the beginning of the field in the early 1960s, consisting of one journal plus conferences and workshops, resulting in over 10,000 papers. Chapter 5 describes how the PDF papers are prepared for our experiments, including identification of references and citations in the papers, once converted to plain text, and extraction of citation information to an XML database. Chapter 6 presents our experiments: we show that adding citation terms to the full-text of the papers improves retrieval effectiveness by up to 7.4%, that weighting citation terms higher relative to paper terms increases the improvement and that varying the context from which citation terms are taken has a significant effect on retrieval effectiveness. Our main hypothesis that citation terms enhance a full-text representation of scientific papers is thus proven. There are some limitations to these experiments. The relevance judgements in our test collection are incomplete but we have experimentally verified that the test collection is, nevertheless, a useful evaluation tool. Using the Lemur toolkit constrained the method that we used to weight citation terms; we would like to experiment with a more realistic implementation of term weighting. Our experiments with different citation contexts did not conclude an optimal citation context; we would like to extend the scope of our investigation. Now that our test collection exists, we can address these issues in our experiments and leave the door open for more extensive experimentation.

2008

Krause, B.; Jäschke, R.; Hotho, A. & Stumme, G. (2008), Logsonomy - Social Information Retrieval with Logdata, in 'HT '08: Proceedings of the nineteenth ACM conference on Hypertext and hypermedia' , ACM, New York, NY, USA , pp. 157--166 .
[BibTeX] [Endnote]

Social bookmarking systems constitute an established part of the Web 2.0. In such systems users describe bookmarks by keywords called tags. The structure behind these social systems, called folksonomies, can be viewed as a tripartite hypergraph of user, tag and resource nodes. This underlying network shows specific structural properties that explain its growth and the possibility of serendipitous exploration. Today’s search engines represent the gateway to retrieve information from the World Wide Web. Short queries typically consisting of two to three words describe a user’s information need. In response to the displayed results of the search engine, users click on the links of the result page as they expect the answer to be of relevance. This clickdata can be represented as a folksonomy in which queries are descriptions of clicked URLs. The resulting network structure, which we will term logsonomy is very similar to the one of folksonomies. In order to find out about its properties, we analyze the topological characteristics of the tripartite hypergraph of queries, users and bookmarks on a large snapshot of del.icio.us and on query logs of two large search engines. All of the three datasets show small world properties. The tagging behavior of users, which is explained by preferential attachment of the tags in social bookmark systems, is reflected in the distribution of single query words in search engines. We can conclude that the clicking behaviour of search engine users based on the displayed search results and the tagging behaviour of social bookmarking users is driven by similar dynamics.

Krause, B.; Jäschke, R.; Hotho, A. & Stumme, G. (2008), Logsonomy - Social Information Retrieval with Logdata, in 'HT '08: Proceedings of the Nineteenth ACM Conference on Hypertext and Hypermedia' , ACM, New York, NY, USA , pp. 157--166 .
[BibTeX] [Endnote]

Manning, C. D.; Raghavan, P. & Schütze, H. (2008), Introduction to Information Retrieval , Cambridge University Press .
[BibTeX] [Endnote]

Manning, C. D.; Raghavan, P. & Schütze, H. (2008), Introduction to Information Retrieval , Cambridge University Press , New York .
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"Class-tested and coherent, this textbook teaches classical and web information retrieval, including web search and the related areas of text classification and text clustering from basic concepts. It gives an up-to-date treatment of all aspects of the design and implementation of systems for gathering, indexing, and searching documents; methods for evaluating systems; and an introduction to the use of machine learning methods on text collections. All the important ideas are explained using examples and figures, making it perfect for introductory courses in information retrieval for advanced undergraduates and graduate students in computer science. Based on feedback from extensive classroom experience, the book has been carefully structured in order to make teaching more natural and effective. Slides and additional exercises (with solutions for lecturers) are also available through the book's supporting website to help course instructors prepare their lectures." -- Publisher's description.

Manning, C. D.; Raghavan, P. & Schütze, H. (2008), Introduction to Information Retrieval , Cambridge University Press , New York .
[BibTeX] [Endnote]

2007

Cha, S.-H. (2007), 'Comprehensive Survey on Distance/Similarity Measures between Probability Density Functions', International Journal of Mathematical Models and Methods in Applied Sciences 1 (4) , 300--307 .
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2006

Hotho, A.; J?schke, R.; Schmitz, C. & Stumme, G. (2006), Information Retrieval in Folksonomies: Search and Ranking, in York Sure & John Domingue, ed., 'The Semantic Web: Research and Applications' , Springer, Heidelberg , pp. 411-426 .
[BibTeX] [Endnote]

Hotho, A.; Jäschke, R.; Schmitz, C. & Stumme, G. (2006), Information Retrieval in Folksonomies: Search and Ranking, in York Sure & John Domingue, ed., 'The Semantic Web: Research and Applications' , Springer, Heidelberg , pp. 411-426 .
[BibTeX] [Endnote]

Koester, B. (2006), FooCA: web information retrieval with formal concept analysis , Verlag Allgemeine Wissenschaft , Mühltal .
[BibTeX] [Endnote]

2002

Broder, A. (2002), 'A taxonomy of web search', SIGIR Forum 36 (2) , 3--10 .
[BibTeX] [Endnote]

Joachims, T. (2002), Optimizing search engines using clickthrough data, in 'Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining' , ACM, New York, NY, USA , pp. 133--142 .
[BibTeX] [Endnote]

This paper presents an approach to automatically optimizing the retrieval quality of search engines using clickthrough data. Intuitively, a good information retrieval system should present relevant documents high in the ranking, with less relevant documents following below. While previous approaches to learning retrieval functions from examples exist, they typically require training data generated from relevance judgments by experts. This makes them difficult and expensive to apply. The goal of this paper is to develop a method that utilizes clickthrough data for training, namely the query-log of the search engine in connection with the log of links the users clicked on in the presented ranking. Such clickthrough data is available in abundance and can be recorded at very low cost. Taking a Support Vector Machine (SVM) approach, this paper presents a method for learning retrieval functions. From a theoretical perspective, this method is shown to be well-founded in a risk minimization framework. Furthermore, it is shown to be feasible even for large sets of queries and features. The theoretical results are verified in a controlled experiment. It shows that the method can effectively adapt the retrieval function of a meta-search engine to a particular group of users, outperforming Google in terms of retrieval quality after only a couple of hundred training examples.

1999

Lawrence, S.; Bollacker, K. & Giles, C. L. (1999), Indexing and retrieval of scientific literature, in 'Proceedings of the eighth international conference on Information and knowledge management' , ACM, New York, NY, USA , pp. 139--146 .
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<par>The web has greatly improved access to scientific literature. However, scientific articles on the web are largely disorganized, with research articles being spread across archive sites, institution sites, journal sites, and researcher homepages. No index covers all of the available literature, and the major web search engines typically do not index the content of Postscript/PDF documents at all. This paper discusses the creation of digital libraries of scientific literature on the web, including the efficient location of articles, full-text indexing of the articles, autonomous citation indexing, information extraction, display of query-sensitive summaries and citation context, hubs and authorities computation, similar document detection, user profiling, distributed error correction, graph analysis, and detection of overlapping documents. The software for the system is available at no cost for non-commercial use.</par>

1992

Steenweg, H. (1992), Computers and Social History: Building a Database from Mediaeval Tax Registers for improved Information Retrieval in Göttingen, in G. Lock & J. Moffet, ed., 'CAA 91, Computer Applications and Quantitative Methods in Archaeology (BAR International Series S 577),' , pp. 29-38. .
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