A concept space approach to semantic exchange

This dissertation work investigates the use of information technologies that clarify semantic meaning to help users elaborate their information needs by providing library-specific knowledge to the information seeking process. The research involved two interdependent semantic technologies: concept space consultation and library-specific, domain-specific, automatically generated concept spaces. The concept space consultation phase used spreading activation algorithms--branch-and-bound and Hopfield net algorithms--to explore knowledge sources in specific domains. This research demonstrated the comparable effectiveness of exploration of a library database using a man-made classification scheme and thesaurus as opposed to an automatically generated concept space. The results showed that the use of spreading activation algorithms identified more relevant concepts than the use of the manual browsing method. The concept space technique automatically identifies and extracts concept from a library collection while at the same time computing the strength of associations between concepts. This research demonstrated that the concept space technique was able to create human-recognizable concepts and their associations. In addition, the technique could be scaled to generate very large library-specific concept spaces for a very large underlying library collection. Moreover, the interdependent use of both semantic technologies creates a semantic medium for users and library-specific knowledge sources to exchange content with context--context in user information need and that in corporeal knowledge

An Algorithmic Approach to Concept Exploration in a Large Knowledge Network (Automatic Thesaurus Consultation): Symbolic Branch-and-Bound Search vs. Connectionist Hopfield Net Activation

Artificial Intelligence Lab, Department of MIS, University of ArizonaThis paper presents a framework for knowledge discovery and concept exploration. In order to enhance the concept exploration capability of knowledge-based systems and to alleviate the limitations of the manual browsing approach, we have developed two spreading activation-based algorithms for concept exploration in large, heterogeneous networks of concepts (e.g., multiple thesauri). One algorithm, which is based on the symbolic Al paradigm, performs a conventional branch-and-bound search on a semantic net representation to identify other highly relevant concepts (a serial, optimal search process). The second algorithm, which is based on the neural network approach, executes the Hopfield net parallel relaxation and convergence process to identify â convergentâ concepts for some initial queries (a parallel, heuristic search process). Both algorithms can be adopted for automatic, multiple-thesauri consultation. We tested these two algorithms on a large text-based knowledge network of about 13,000 nodes (terms) and 80,000 directed links in the area of computing technologies. This knowledge network was created from two external thesauri and one automatically generated thesaurus. We conducted experiments to compare the behaviors and performances of the two algorithms with the hypertext-like browsing process. Our experiment revealed that manual browsing achieved higher-term recall but lower-term precision in comparison to the algorithmic systems. However, it was also a much more laborious and cognitively demanding process. In document retrieval, there were no statistically significant differences in document recall and precision between the algorithms and the manual browsing process. In light of the effort required by the manual browsing process, our proposed algorithmic approach presents a viable option for efficiently traversing largescale, multiple thesauri (knowledge network)

Enriching Perspectives in Exploring Cultural Heritage Documentaries Using Informedia Technologies

Speech recognition, image processing, and language understanding technologies have successfully been applied to broadcast news corpora to automate the extraction of metadata and make use of it in building effective video news retrieval interfaces. This paper discusses how these multimedia technologies can be adapted to enrich perspectives in exploring cultural heritage documentaries

Video retrieval using speech and image information

Video contains multiple types of audio and visual information, which are difficult to extract, combine or trade-off in general video information retrieval. This paper provides an evaluation on the effects of different types of information used for video retrieval from a video collection. A number of different sources of information are present in most typical broadcast video collections and can be exploited for information retrieval. We will discuss the contributions of automatically recognized speech transcripts, image similarity matching, face detection and video OCR in the contexts of experiments performed as part of 2001 TREC Video Retrieval Track evaluation performed by the National Institute of Standards and Technology. For the queries used in this evaluation, image matching and video OCR proved to be the deciding aspects of video information retrieval

Generating, Integrating, and Activating Thesauri for Concept-based Document Retrieval

Artificial Intelligence Lab, Department of MIS, University of ArizonaThis Blackboard-based design uses a neural-net spreading-activation algorithm to traverse multiple thesauri. Guided by heuristics, the algorithm activates related terms in the thesauri and converges on the most pertinent concepts

Multi-modal Information Retrieval from Broadcast Video using OCR and Speech Recognition

We examine multi-modal information retrieval from broadcast video where text can be read on the screen through OCR and speech recognition can be performed on the audio track. OCR and speech recognition are compared on the 2001 TREC Video Retrieval evaluation corpus. Results show that OCR is more important that speech recognition for video retrieval. OCR retrieval can further improve through dictionary-based post-processing. We demonstrate how to utilize imperfect multi-modal metadata results to benefit multi-modal information retrieval

A Concept Space Approach to Addressing the Vocabulary Problem in Scientific Information Retrieval: An Experiment on the Worm Community System

Artificial Intelligence Lab, Department of MIS, University of ArizonaThis research presents an algorithmic approach to addressing the vocabulary problem in scientific information retrieval and information sharing, using the molecular biology domain as an example. We first present a literature review of cognitive studies related to the vocabulary problem and vocabuiary-based search aids (thesauri) and then discuss techniques for building robust and domain-specific thesauri to assist in cross-domain scientific information retrieval. Using a variation of the automatic thesaurus generation techniques, which we refer to as the concept space approach, we recently conducted an experiment in the molecular biology domain in which we created a C. elegans worm thesaurus of 7,657 worm-specific terms and a Drosofila fly thesaurus of 15,626 terms. About 30% of these terms overlapped, which created vocabulary paths from one subject domain to the other. Based on a cognitive study of term association involving four biologists, we found that a large percentage (59.6-85.6%) of the terms suggested by the subjects were identified in the conjoined fly-worm thesaurus. However, we found only a small percentage (8.4-18.1%) of the associations suggested by the subjects in the thesaurus. In a follow-up document retrieval study involving eight fly biologists, an actual worm database (Worm Community System), and the conjoined flyworm thesaurus, subjects were able to find more relevant documents (an increase from about 9 documents to 20) and to improve the document recall level (from 32.41 to 65.28%) when using the thesaurus, although the precision level did not improve significantly. Implications of adopting the concept space approach for addressing the vocabulary problem in Internet and digital libraries applications are also discussed