JOURNAL OF DIGITAL INFORMATION MANAGEMENT

(ISSN 0972-7272) The peer reviewed  journal

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Volume 3 Issue 4  Online October 2005 Print December 2005

Abstracts

ON SEMANTICS AS A SOCIAL CONSTRUCTION

Simone Santini
Escuela Politcnica Superior, Universidad Autnoma de Madrid, Spain
simone.santini@uam.es
San Diego Supercomputer Center, University of California, San Diego, USA
Email: ssantini@sdsc.edu

Abstract

This paper argues the idea that semantics in an information system is built by a community of users during the activities that the system support. Information systems, therefore, should be limited to find meaningful syntactic relations between the document spaces of interest to the application, and let the users create their own semantics. An example of design along these lines is presented.


SEMANTIC NOTATION AND RETRIEVAL IN ART AND ARCHITECTURE IMAGE COLLECTIONS

Peter L. Stanchev, David Green Jr., Boyan Dimitrov
Kettering University, Flint, MI 48504, USA
Email: {pstanche, dgreen, bdimitro}@kettering.edu

Abstract

In this paper we analyze various methods used for semantic annotation and search in a collection of art and architecture images. We discuss the Art and Architecture Thesaurus, WordNet, ULAN and Iconclass ontology. Systems for searching and retrieval art and architecture image collections are presented. We explore if the MPEG 7 descriptors are useful for art and architecture image annotations. For illustrations we use images from Antoni Gaudi architecture and Claude Monet paintings.


VIDEO EVENT REPRESENTATION ASSISTED BY DOMAIN KNOWLEDGE

Dan Song1, Hai Tao Liu1 , Miyoung Cho1, Moosong Oh2 , Sangdong Ra2 , Pankoo Kim3
1 Dept. of Computer Science
Chosun University, 375 Seosuk-dong Dong-Ku Gwangju 501-759 Korea
Email: {songdan, htliu, irune80}@stmail.chosun.ac.kr
2 Dept. of Computer Science & Engineering
Chosun University375 Seosuk-dong Dong-Ku Gwangju 501-759 Korea
Email: msoh@chosun.ac.kr  , sdna@mail.chosun.ac.kr
3 Corresponding Author, Dept. of CSE , Chosun University, Korea
Email: pkkim@chosun.ac.kr

Abstract

A novel method for video event analysis and description based on the domain knowledge has been put forward in this paper. Semantic concepts in the context of the video event are described in one specific domain enriched with qualitative attributes of the semantic objects, multimedia processing approaches and domain independent factors: low level features (pixel color, motion vectors and spatio-temporal relationship). In this work, we consider one shot (episode) in the Billiard Game of video as the specific domain to explain the process of video event detection. In addition, our another main contribution is exploiting the video object ontology to map the gap from the high-level descriptors to low level features descriptors which have been defined in the MPEG’s logical structure.


SEMANTIC IMAGE RETRIEVAL BASED ON ONTOLOGY AND RELEVANCE MODEL: A PRELIMINARY STUDY


Ernest Weke Maina†, Manabu Ohta‡, Kaoru Katayama‡, Hiroshi Ishikawa‡
Tokyo Metropolitan University 1-1 Minami-Osawa, Hachioji-shi Tokyo, Japan 192-039
E-mail: †ewmaina@ieee.org, ‡{ohta,katayama,ishikawa}@eei.metro-u.ac.jp


Abstract

We present the preliminary results on a framework for applying semantics to enhance image retrieval on the World Wide Web. We consider this problem on two separate levels. First, we use Ontology to define the semantic query space where image search and navigation take place. Secondly, we use the web search engine to locate textual documents to train a Relevance Model. The Model is used to rank images from an image search. In this preliminary study, we investigate how application of Ontology affects the quantity and quality the retrieved images and also the effects to the experience of image search. We contrast the results with Relevance Modelling for exactly similar search terms. The probability based Relevance Model applies language models to the text linking to the image. This Model can be learnt from the Web without any preparation of training data and is independent of the underlying algorithm of the image search engines. We show that Ontology can enhance image browsing by providing semantic relations and also improving recall, while relevance feedback applies semantic relations embedded in text to improve precision by effectively re-ranking.
 


LINGOES: A LINGUISTIC ONTOLOGY MANAGEMENT SYSTEM


F. Mostowfi1, F. Fotouhi1, S. Lu1, A. Aristar2

1 Computer Science Department, Wayne State University, Detroit, Michigan, USA.
Email: {fmostowfi, Fotouhi, shiyong}@wayne.edu
2 Department of English, Wayne State University, Detroit, Michigan, USA.
Email: aristar@linguistlist.org

Abstract

LINGuistic Ontology managEment System (LINGOES) is a framework to enable linguists to take full advantage of the Semantic Web technologies. Together with OntoGloss, a text annotation tool, and an RDF database with versioning and querying capabilities, it allows a linguist to markup any document with classes in one or more ontologies at the morpheme’s level. Textual documents can be in any language as long as they are accessible via a URI (Universal Resource Identifier). The annotated data can be queried across these languages or can be used to annotate other documents. Saving the annotated data in an RDF repository with inference, querying and change management capabilities makes annotations in LINGOES accessible by machines and useful to the wider Semantic Web community.


SEARCHING MULTIMEDIA DOCUMENTS: AN APPLICATION IN PATENT EXAMINATION


Forouzan Golshani
Wright State University, Computer Science & Engineering
3640 Colonel, Glenn Highway Dayton
Ohio 45435, USA
Email: golshani@wright.edu

Youngchoon Park
Johnson Controls, Inc. Digital Vision Networks
507 E. Michigan Street, Milwaukee. WI 53202, USA
Email: youngchoon.park@jci.com

 


Abstract

Cross-cutting multimedia information integration is essential in many fields, and is indispensable in analysis and comparison of multimedia documents. In this paper, we outline how integrated search in multimedia repositories can be applied toward automation of patent examination process, which is currently done with text search tools and manual examination of images and drawings.


DISTRIBUTED CONSTRUCTION OF WEAKLY CONNECTED DOMINATING SETS FOR CLUSTERING MOBILE AD HOC NETWORKS


Fredrick Japhet Mtenzi
School of Computing
Dublin Institute of technology
Kevin Street, Dublin 8
Ireland
Email: Fred.Mtenzi@comp.dit.ie

 


Abstract

Weakly connected dominating set (WCDS) has been proposed to cluster mobile ad hoc networks and be used as a virtual backbone. There have been some distributed approximation algorithms proposed in the literature for minimum WCDS. But none of them have constant approximation factors. Thus these algorithms can not guarantee to generate a WCDS of small size. Their message complexities may also be as large as . In this paper, we design a new distributed algorithm that outperforms the existing algorithms. This algorithm has an approximation factor of at most 5 and linear message complexity. Our algorithm requires only single-hop neighborhood knowledge and a message length of . So it is practical.


YAFIMA: YET ANOTHER FREQUENT ITEMSET MINING ALGORITHM


Mohammad El-Hajj, Osmar R. Zaļane
Department of Computing Science
University of Alberta, Edmonton, AB, Canada
Email: {mohammad, zaiane}@cs.ualberta.ca

Abstract

Efficient discovery of frequent patterns from large databases is an active research area in data mining with broad applications in industry and deep implications in many areas of data mining. Although many efficient frequent-pattern mining techniques have been developed in the last decade, most of them assume relatively small databases, leaving extremely large but realistic datasets out of reach. A practical and appealing direction is to mine for closed or maximal itemsets. These are subsets of all frequent patterns but good representatives since they eliminate what is known as redundant patterns. The practicality of discovering closed or maximal itemsets comes from the relatively inexpensive process to mine them in comparison to finding all patterns. In this paper we introduce a new approach for traversing the search space to discover all frequent patterns, the closed or the maximal patterns efficiently in extremely large datasets. We present experimental results for finding all three types of patterns with very large database sizes never reported before. Our implementation tested on real and synthetic data shows that our approach outperforms similar state-of-the-art algorithms by at least one order of magnitude in terms of both execution time and memory usage, in particular when dealing with very large databases.


IMPROVING EXAM TIME TABLING SOLUTION USING TABU SEARCH


Ahamad Tajudin Khader, Ang Siew See
School of Computer Sciences,
Universiti Sains Malaysia, Penang, Malaysia
Email: tajudin@cs.usm.my, siewsee@hotmail.com

 

Abstract

A feasible exam timetable is generated using a method based on constraint satisfaction and heuristics. We investigate the usage of tabu search to further improve the quality of the exam timetable. Different length of short term tabu list and the long term tabu list is examined. Short term tabu list without long term tabu list and vice versa is also tested. Different search iteration based on maximum null iteration and maximum tabu relaxation is also considered. Experiments are carried out on an actual dataset from Universiti Sains Malaysia. Results from these experiments show the relative significance of long term tabu list relative to short term tabu list for this dataset.


THE PARADIGMA APPROACH FOR COOPERATIVE WORK IN THE MEDICAL DOMAIN


Antonio Di Leva
Dipartimento di Informatica, Università di Torino,
corso Svizzera 185 – 10149
Torino, Italy
Email: dileva@di.unito.it

Carla Reyneri
Studio Perotti Professionisti Associati,
Torino, Italy
Email: creyneri@yahoo.it

Michele Sonnessa
Dipartimento di Informatica, Università di Torino,
corso Svizzera 185 – 10149
Torino, Italy
Email: sonnessa@di.unito.it

Abstract

PARADIGMA (PARticipative Approach to DIsease Global Management) is a pilot project which aims to develop and demonstrate an Internet based reference framework to share scientific resources and findings in the treatment of major diseases. PARADIGMA defines and disseminates a common methodology and optimised protocols (Clinical Pathways) to support service functions directed to patients and individuals on matters like prevention, post-hospitalisation support and awareness. PARADIGMA will provide a platform of information services - user oriented and optimised against social, cultural and technological constraints - supporting the Health Care Global System of the Euro-Mediterranean Community in a continuous improvement process.


MK-TREE: AN EFFECTIVE ACCESS METHOD FOR INDEXING HIGH DIMENSIONAL DATA


Guoren Wang, Xiangmin Zhou, Bin Wang, Baiyou Qiao, Donghong Han
Northeastern University, Shenyang, China
Email: {wanggr, zhouxm, binwang@mail.neu.edu.cn} {qiaobaiyou, handonghong}@ise.neu.edu.cn


Abstract

In this paper, we propose an efficient access method, named MK-tree, to dynamically index large data sets in high dimensional spaces. It is an extension of M-tree with key dimension to improve the efficiency of space partition and reduce the response time of similarity search for high dimensional data. The main idea behind the key dimension is to make the fanout of tree larger by partitioning a subspace further into two subspaces, called a twin-node, according to the key dimension. To get a high space utilization, we conduct data reallocation within a twin-node dynamically, therefore further improve the
performance of MK-tree. Our experimental results show that a higher filtering efficiency can be obtained by using the concept of key dimension for both R-neighbor search and K-nearest neighbor search.


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