DART: the distributed agent based retrieval toolkit

The technology of search engines is evolving from indexing and classification of web resources based on keywords to more sophisticated techniques which take into account the meaning and the context of textual information and usage. Replying to query, commercial search engines face the user requests with a large amount of results, mostly useless or only partially related to the request; the subsequent refinement, operated downloading and examining as much pages as possible and simply ignoring whatever stays behind the first few pages, is left up to the user. Furthermore, architectures based on centralized indexes, allow commercial search engines to control the advertisement of online information, in contrast to P2P architectures that focus the attention on user requirements involving the end user in search engine maintenance and operation. To address such wishes, new search engines should focus on three key aspects: semantics, geo-referencing, collaboration/distribution. Semantic analysis lets to increase the results relevance. The geo-referencing of catalogued resources allows contextualisation based on user position. Collaboration distributes storage, processing, and trust on a world-wide network of nodes running on users’ computers, getting rid of bottlenecks and central points of failures. In this paper, we describe the studies, the concepts and the solutions developed in the DART project to introduce these three key features in a novel search engine architecture

Investigating the Effects of Exploratory Semantic Search on the Use of a Museum Archive

Recently, there has been a great deal of interest in how new technologies can support the more effective use of online museum content. Two particularly relevant developments are exploratory search and semantic web technologies. Exploratory search tools support a more undirected and serendipitous interaction with the content. Semantic web technology, when applied in this context, allows the exploitation of metadata and ontologies to provide more intelligent support for user interaction. Bletchley Park Text is a museum web application supporting a semantic driven, exploratory approach to the search and navigation of digital museum resources. Bletchley Park Text uses semantics to organise selected content (i.e. stories) into a number of composite pages that illustrate conceptual patterns in the content, and from which the content itself can be accessed. The use made of Bletchley Park Text over an eight month period was analysed in order to understand the kinds of trajectories across the available resources that users could make with such a system. The results identified two distinct strategies of exploratory search. A risky strategy was characterised as incorporating: conceptual jumps between successive queries, a larger number of shorter queries and the use of the stories themselves to acclimatise to a new set of search results. A cautious strategy was characterised as incorporating: small conceptual shifts between queries, a smaller number of longer queries and the use of composite pages to acclimatise to a set of new search results. These findings have implications for the intelligent scaffolding of exploratory search

Using fuzzy logic to handle the semantic descriptions of music in a content-based retrieval system

This paper explores the potential use of fuzzy logic for semantic music recommendation. We show that a set of affective/emotive, structural and kinaesthetic descriptors can be used to formulate a query which allows the retrieval of intended music. A semantic music recommendation system was built, based on an elaborate study of potential users and an analysis of the semantic descriptors that best characterize the user’s understanding of music. Significant relationships between expressive and structural semantic descriptions of music were found. Fuzzy logic was then applied to handle the quality ratings associated with the semantic descriptions. A working semantic music recommendation system was tested and evaluated. Real-world testing revealed high user satisfaction

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Issues in the Design of a Pilot Concept-Based Query Interface for the Neuroinformatics Information Framework

This paper describes a pilot query interface that has been constructed to help us explore a "concept-based" approach for searching the Neuroscience Information Framework (NIF). The query interface is concept-based in the sense that the search terms submitted through the interface are selected from a standardized vocabulary of terms (concepts) that are structured in the form of an ontology. The NIF contains three primary resources: the NIF Resource Registry, the NIF Document Archive, and the NIF Database Mediator. These NIF resources are very different in their nature and therefore pose challenges when designing a single interface from which searches can be automatically launched against all three resources simultaneously. The paper first discusses briefly several background issues involving the use of standardized biomedical vocabularies in biomedical information retrieval, and then presents a detailed example that illustrates how the pilot concept-based query interface operates. The paper concludes by discussing certain lessons learned in the development of the current version of the interface

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Semantic keyword search for expert witness discovery

In the last few years, there has been an increase in the amount of information stored in semantically enriched knowledge bases, represented in RDF format. These improve the accuracy of search results when the queries are semantically formal. However framing such queries is inappropriate for inexperience users because they require specialist knowledge of ontology and syntax. In this paper, we explore an approach that automates the process of converting a conventional keyword search into a semantically formal query in order to find an expert on a semantically enriched knowledge base. A case study on expert witness discovery for the resolution of a legal dispute is chosen as the domain of interest and a system named SKengine is implemented to illustrate the approach. As well as providing an easy user interface, our experiment shows that SKengine can retrieve expert witness information with higher precision and higher recall, compared with the other system, with the same interface, implemented by a vector model approach

Organizational challenges of the semantic web in digital libraries

The Semantic Web initiative holds large promises for the future. There is, however, a considerable gap in Semantic Web research between the contributions in the technological field and the research done in the organizational field. This paper examines, from a socio-technical point of view the impact of Semantic Web technology on the strategic, organizational and technological levels. Building on a comprehensive case study at the National Library in Norway our findings indicate that the highest impact will be at the organizational level. The reason is mainly because inter-organizational and cross-organizational structures have to be established to address the problems of ontology engineering, and a development framework for ontology engineering in digital libraries must be examined

Processing and Linking Audio Events in Large Multimedia Archives: The EU inEvent Project

In the inEvent EU project [1], we aim at structuring, retrieving, and sharing large archives of networked, and dynamically changing, multimedia recordings, mainly consisting of meetings, videoconferences, and lectures. More specifically, we are developing an integrated system that performs audiovisual processing of multimedia recordings, and labels them in terms of interconnected “hyper-events ” (a notion inspired from hyper-texts). Each hyper-event is composed of simpler facets, including audio-video recordings and metadata, which are then easier to search, retrieve and share. In the present paper, we mainly cover the audio processing aspects of the system, including speech recognition, speaker diarization and linking (across recordings), the use of these features for hyper-event indexing and recommendation, and the search portal. We present initial results for feature extraction from lecture recordings using the TED talks. Index Terms: Networked multimedia events; audio processing: speech recognition; speaker diarization and linking; multimedia indexing and searching; hyper-events. 1