Search (3 results, page 1 of 1)

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Abstract

The book covers multimedia ontology in heritage preservation with intellectual explorations of various themes of Indian cultural heritage. The result of more than 15 years of collective research, Multimedia Ontology: Representation and Applications provides a theoretical foundation for understanding the nature of media data and the principles involved in its interpretation. The book presents a unified approach to recent advances in multimedia and explains how a multimedia ontology can fill the semantic gap between concepts and the media world. It relays real-life examples of implementations in different domains to illustrate how this gap can be filled. The book contains information that helps with building semantic, content-based search and retrieval engines and also with developing vertical application-specific search applications. It guides you in designing multimedia tools that aid in logical and conceptual organization of large amounts of multimedia data. As a practical demonstration, it showcases multimedia applications in cultural heritage preservation efforts and the creation of virtual museums. The book describes the limitations of existing ontology techniques in semantic multimedia data processing, as well as some open problems in the representations and applications of multimedia ontology. As an antidote, it introduces new ontology representation and reasoning schemes that overcome these limitations. The long, compiled efforts reflected in Multimedia Ontology: Representation and Applications are a signpost for new achievements and developments in efficiency and accessibility in the field.

Footnote

Rez. in: Annals of Library and Information Studies 62(2015) no.4, S.299-300 (A.K. Das)

LCSH

Information storage and retrieval systems

Subject

Information storage and retrieval systems

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Abstract

Currently, there is no structured data standard for representing elements commonly found in transmedia fictional worlds. Although there are websites dedicated to individual universes, the information found on these sites separate out the various formats, concentrate on only the bibliographic aspects of the material, and are only searchable with full text. We have created an ontological model that will allow various user groups interested in transmedia to search for and retrieve the information contained in these worlds based upon their structure. We conducted a domain analysis and user studies based on the contents of Harry Potter, Lord of the Rings, the Marvel Universe, and Star Wars in order to build a new model using Ontology Web Language (OWL) and an artificial intelligence-reasoning engine. This model can infer connections between transmedia properties such as characters, elements of power, items, places, events, and so on. This model will facilitate better search and retrieval of the information contained within these vast story universes for all users interested in them. The result of this project is an OWL ontology reflecting real user needs based upon user research, which is intuitive for users and can be used by artificial intelligence systems.

Source

Journal of the Association for Information Science and Technology. 68(2017) no.12, S.2771-2782

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Abstract

Recent technological developments have increased the use of machine learning to solve many problems, including many in information retrieval. Multimedia information retrieval as a problem represents a significant challenge to machine learning as a technological solution, but some problems can still be addressed by using appropriate AI techniques. We review the technological developments and provide a perspective on the use of machine learning in conjunction with knowledge organization to address multimedia IR needs. The semantic gap in multimedia IR remains a significant problem in the field, and solutions to them are many years off. However, new technological developments allow the use of knowledge organization and machine learning in multimedia search systems and services. Specifically, we argue that, the improvement of detection of some classes of lowlevel features in images music and video can be used in conjunction with knowledge organization to tag or label multimedia content for better retrieval performance. We provide an overview of the use of knowledge organization schemes in machine learning and make recommendations to information professionals on the use of this technology with knowledge organization techniques to solve multimedia IR problems. We introduce a five-step process model that extracts features from multimedia objects (Step 1) from both knowledge organization (Step 1a) and machine learning (Step 1b), merging them together (Step 2) to create an index of those multimedia objects (Step 3). We also overview further steps in creating an application to utilize the multimedia objects (Step 4) and maintaining and updating the database of features on those objects (Step 5).