The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

Ontologies are often viewed as the answer to the need for interoperable semantics in modern information systems. The explosion of textual information on the Read/Write Web coupled with the increasing demand for ontologies to power the Semantic Web have made (semi-)automatic ontology learning from text a very promising research area. This together with the advanced state in related areas, such as natural language processing, have fueled research into ontology learning over the past decade. This survey looks at how far we have come since the turn of the millennium and discusses the remaining challenges that will define the research directions in this area in the near future.

Ontology learning from text: A look back and into the future

Users on Twitter, a microblogging service, started the phenomenon of adding tags to their messages sometime around February 2008. These tags are distinct from those in other Web 2.0 systems because users are less likely to index messages for later retrieval. We compare tagging patterns in Twitter with those in Delicious to show that tagging behavior in Twitter is different because of its conversational, rather than organizational nature. We use a mixed method of statistical analysis and an interpretive approach to study the phenomenon. We find that tagging in Twitter is more about filtering and directing content so that it appears in certain streams. The most illustrative example of how tagging in Twitter differs is the phenomenon of the Twitter micro-meme: emergent topics for which a tag is created, used widely for a few days, then disappears. We describe the micro-meme phenomenon and discuss the importance of this new tagging practice for the larger real-time search context.

/pdf/conversational-tagging-in-twitter-xhduvg3lkj.pdf

Conversational tagging in twitter

In this paper, we describe how the BBC is working to integrate data and linking documents across BBC domains by using Semantic Web technology, in particular Linked Data, MusicBrainz and DBpedia. We cover the work of BBC Programmes and BBC Music building Linked Data sites for all music and programmes related brands, and we describe existing projects, ongoing development, and further research we are doing in a joint collaboration between the BBC, Freie Universitat Berlin and Rattle Research in order to use DBpedia as the controlled vocabulary and semantic backbone for the whole BBC.

/pdf/media-meets-semantic-web-how-the-bbc-uses-dbpedia-and-linked-1tbs2ix8an.pdf

Media Meets Semantic Web --- How the BBC Uses DBpedia and Linked Data to Make Connections

The Semantic Web is characterized by the existence of a very large number of distributed semantic resources, which together define a network of ontologies. These ontologies in turn are interlinked through a variety of different meta-relationships such as versioning, inclusion, and many more. This scenario is radically different from the relatively narrow contexts in which ontologies have been traditionally developed and applied, and thus calls for new methods and tools to effectively support the development of novel network-oriented semantic applications. This book by Surez-Figueroa et al. provides the necessary methodological and technological support for the development and use of ontology networks, which ontology developers need in this distributed environment. After an introduction, in its second part the authors describe the NeOn Methodology framework. The books third part details the key activities relevant to the ontology engineering life cycle. For each activity, a general introduction, methodological guidelines, and practical examples are provided. The fourth part then presents a detailed overview of the NeOn Toolkit and its plug-ins. Lastly, case studies from the pharmaceutical and the fishery domain round out the work. The book primarily addresses two main audiences: students (and their lecturers) who need a textbook for advanced undergraduate or graduate courses on ontology engineering, and practitioners who need to develop ontologies in particular or Semantic Web-based applications in general. Its educational value is maximized by its structured approach to explaining guidelines and combining them with case studies and numerous examples. The description of the open source NeOn Toolkit provides an additional asset, as it allows readers to easily evaluate and apply the ideas presented.

Ontology Engineering in a Networked World

In recent years several measures for the gold standard based evaluation of ontology learning were proposed. They can be distinguished by the layers of an ontology (e.g. lexical term layer and concept hierarchy) they evaluate. Judging those measures with a list of criteria we show that there exist some measures sufficient for evaluating the lexical term layer. However, existing measures for the evaluation of concept hierarchies fail to meet basic criteria. This paper presents a new taxonomic measure which overcomes the problems of current approaches.

/pdf/on-how-to-perform-a-gold-standard-based-evaluation-of-3ngulq5obp.pdf

On how to perform a gold standard based evaluation of ontology learning

An important aspect of ontology learning is a proper evaluation. Generally, one can distinguish between two scenarios: (i) quality assurance during an ontology engineering project in which also ontology learning techniques may be used and (ii) evaluating and comparing ontology learning algorithms in the laboratory during their development. This paper gives an overview of different evaluation approaches and matches them against the requirements of the scenarios. It will be shown that different evaluation approaches have to be applied depending on the scenario. Special attention will be paid to the second scenario and the gold standard based evaluation of ontology learning for which concrete measures for the lexical and taxonomic layer will be presented.

Strategies for the Evaluation of Ontology Learning

In recent literature, several models were proposed for reproducing and understanding the tagging behavior of users. They all assume that the tagging behavior is influenced by the previous tag assignments of other users. But they are only partially successful in reproducing characteristic properties found in tag streams. We argue that this inadequacy of existing models results from their inability to include user's background knowledge into their model of tagging behavior. This paper presents a generative tagging model that integrates both components, the background knowledge and the influence of previous tag assignments. Our model successfully reproduces characteristic properties of tag streams. It even explains effects of the user interface on the tag stream.

/pdf/an-epistemic-dynamic-model-for-tagging-systems-42hbxd20jg.pdf

An epistemic dynamic model for tagging systems

Creating and designing an ontology is a complex task requiring discussions between domain and ontology engineering experts as well as the users of an ontology. We present the Cicero tool, that facilitates efficient discussions and accelerates the convergence to decisions. Furthermore, by integrating it with an ontology editor, it helps to improve the documentation of an ontology.

/pdf/cicero-tracking-design-rationale-in-collaborative-ontology-2geqh0iip2.pdf

Cicero: tracking design rationale in collaborative ontology engineering

Nowadays Web 2.0 platforms like YouTube [6], Flickr [3] and del.icio.us [2] provide large amounts of resources such as videos, photographs and social bookmarks. Common to the platforms is the classification by so called tags that can be used for organization and retrieval. A current limitation of tagging platforms is their confinement to a single media type. Furthermore, a magnitude of platforms exists for each media type. Thus, in both cases of searching resources of either the same or of different media type, a user has to search multiple platforms [7]. For example, a user needs to search on del.icio.us, RawSugar [4] and Bibsonomy [1] to find bookmarks or on Flickr and YouTube to find media related to e.g. an artist. Another limitation results from the ranking of resources as implemented by platforms such as YouTube, Flickr, and del.icio.us. Usually, the overall popularity of a resource is used for ranking search results. A personalized search is currently missing that takes the interests of a user into account. MyTag aims at solving the previously described limitations of current tagging platforms by enabling cross-media search across images, video, and social bookmarks. It offers transparent access to different single-media platforms currently including Flickr, YouTube, and del.icio.us (cf. Section 2). Furthermore, it introduces personalization features such as a personalized ranking of search results (cf. Section 3). The architecture of MyTag ensures its extensibility towards further tagging platforms (cf. Section 4).

Klaas Dellschaft

Papers

On how to perform a gold standard based evaluation of ontology learning

Strategies for the Evaluation of Ontology Learning

An epistemic dynamic model for tagging systems

Cicero: tracking design rationale in collaborative ontology engineering

Personalized search and exploration with mytag