CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Using TREC for cross-comparison between classic IR and ontology-based search models at a Web scale

The construction of standard datasets and benchmarks to evaluate ontology-based search approaches and to compare then against baseline IR models is a major open problem in the semantic technologies community. In this paper we propose a novel evaluation benchmark for ontology-based IR models based on an adaptation of the well-known Cranfield paradigm (Cleverdon, 1967) traditionally used by the IR community. The proposed benchmark comprises: 1) a text document collection, 2) a set of queries and their corresponding document relevance judgments and 3) a set of ontologies and Knowledge Bases covering the query topics. The document collection and the set of queries and judgments are taken from one of the most widely used datasets in the IR community, the TREC Web track. As a use case example we apply the proposed benchmark to compare a real ontology-based search model (Fernandez, et al., 2008) against the best IR systems of TREC 9 and TREC 2001 competitions. A deep analysis of the strengths and weaknesses of this benchmark and a discussion of how it can be used to evaluate other ontology-based search systems is also included at the end of the paper

Meeting of the MINDS: an information retrieval research agenda

Since its inception in the late 1950s, the field of Information Retrieval (IR) has developed tools that help people find, organize, and analyze information. The key early influences on the field are well-known. Among them are H. P. Luhn's pioneering work, the development of the vector space retrieval model by Salton and his students, Cleverdon's development of the Cranfield experimental methodology, Spärck Jones' development of idf, and a series of probabilistic retrieval models by Robertson and Croft. Until the development of the WorldWideWeb (Web), IR was of greatest interest to professional information analysts such as librarians, intelligence analysts, the legal community, and the pharmaceutical industry

Why People Search for Images using Web Search Engines

What are the intents or goals behind human interactions with image search engines? Knowing why people search for images is of major concern to Web image search engines because user satisfaction may vary as intent varies. Previous analyses of image search behavior have mostly been query-based, focusing on what images people search for, rather than intent-based, that is, why people search for images. To date, there is no thorough investigation of how different image search intents affect users' search behavior. In this paper, we address the following questions: (1)Why do people search for images in text-based Web image search systems? (2)How does image search behavior change with user intent? (3)Can we predict user intent effectively from interactions during the early stages of a search session? To this end, we conduct both a lab-based user study and a commercial search log analysis. We show that user intents in image search can be grouped into three classes: Explore/Learn, Entertain, and Locate/Acquire. Our lab-based user study reveals different user behavior patterns under these three intents, such as first click time, query reformulation, dwell time and mouse movement on the result page. Based on user interaction features during the early stages of an image search session, that is, before mouse scroll, we develop an intent classifier that is able to achieve promising results for classifying intents into our three intent classes. Given that all features can be obtained online and unobtrusively, the predicted intents can provide guidance for choosing ranking methods immediately after scrolling

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research