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Abstract

The information available in languages other than English in the World Wide Web is increasing significantly. According to a report from Computer Economics in 1999, 54% of Internet users are English speakers ("English Will Dominate Web for Only Three More Years," Computer Economics, July 9, 1999, http://www.computereconomics. com/new4/pr/pr990610.html). However, it is predicted that there will be only 60% increase in Internet users among English speakers verses a 150% growth among nonEnglish speakers for the next five years. By 2005, 57% of Internet users will be non-English speakers. A report by CNN.com in 2000 showed that the number of Internet users in China had been increased from 8.9 million to 16.9 million from January to June in 2000 ("Report: China Internet users double to 17 million," CNN.com, July, 2000, http://cnn.org/2000/TECH/computing/07/27/ china.internet.reut/index.html). According to Nielsen/ NetRatings, there was a dramatic leap from 22.5 millions to 56.6 millions Internet users from 2001 to 2002. China had become the second largest global at-home Internet population in 2002 (US's Internet population was 166 millions) (Robyn Greenspan, "China Pulls Ahead of Japan," Internet.com, April 22, 2002, http://cyberatias.internet.com/big-picture/geographics/article/0,,5911_1013841,00. html). All of the evidences reveal the importance of crosslingual research to satisfy the needs in the near future. Digital library research has been focusing in structural and semantic interoperability in the past. Searching and retrieving objects across variations in protocols, formats and disciplines are widely explored (Schatz, B., & Chen, H. (1999). Digital libraries: technological advances and social impacts. IEEE Computer, Special Issue an Digital Libraries, February, 32(2), 45-50.; Chen, H., Yen, J., & Yang, C.C. (1999). International activities: development of Asian digital libraries. IEEE Computer, Special Issue an Digital Libraries, 32(2), 48-49.). However, research in crossing language boundaries, especially across European languages and Oriental languages, is still in the initial stage. In this proposal, we put our focus an cross-lingual semantic interoperability by developing automatic generation of a cross-lingual thesaurus based an English/Chinese parallel corpus. When the searchers encounter retrieval problems, Professional librarians usually consult the thesaurus to identify other relevant vocabularies. In the problem of searching across language boundaries, a cross-lingual thesaurus, which is generated by co-occurrence analysis and Hopfield network, can be used to generate additional semantically relevant terms that cannot be obtained from dictionary. In particular, the automatically generated cross-lingual thesaurus is able to capture the unknown words that do not exist in a dictionary, such as names of persons, organizations, and events. Due to Hong Kong's unique history background, both English and Chinese are used as official languages in all legal documents. Therefore, English/Chinese cross-lingual information retrieval is critical for applications in courts and the government. In this paper, we develop an automatic thesaurus by the Hopfield network based an a parallel corpus collected from the Web site of the Department of Justice of the Hong Kong Special Administrative Region (HKSAR) Government. Experiments are conducted to measure the precision and recall of the automatic generated English/Chinese thesaurus. The result Shows that such thesaurus is a promising tool to retrieve relevant terms, especially in the language that is not the same as the input term. The direct translation of the input term can also be retrieved in most of the cases.

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Abstract

Interdisciplinary research has been attracting more attention in recent decades. In this article, we compare the similarity between scientific research domains and quantifying the temporal similarities of domains. We narrowed our study to three research domains: information retrieval (IR), database (DB), and World Wide Web (W3), because the rapid development of the W3 domain substantially attracted research efforts from both IR and DB domains and introduced new research questions to these two areas. Most existing approaches either employed a content-based technique or a cocitation or coauthorship network-based technique to study the development trend of a research area. In this work, we proposed an effective way to quantify the similarities among different research domains by incorporating content similarity and coauthorship network similarity. Experimental results on DBLP (DataBase systems and Logic Programming) data related to IR, DB, and W3 domains showed that the W3 domain was getting closer to both IR and DB whereas the distance between IR and DB remained relatively constant. In addition, comparing to IR and W3 with the DB domain, the DB domain was more conservative and evolved relatively slower.

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Abstract

Informational support and nurturant support are two basic types of social support offered in online health communities. This study identifies types of social support in the QuitStop forum and brings insights to exchange patterns of social support and user behaviors with content analysis and social network analysis. Motivated by user information behavior, this study defines two patterns to describe social support exchange: initiated support exchange and invited support exchange. It is found that users with a longer quitting time tend to actively give initiated support, and recent quitters with a shorter abstinent time are likely to seek and receive invited support. This study also finds that support givers of informational support quit longer ago than support givers of nurturant support, and support receivers of informational support quit more recently than support receivers of nurturant support. Usually, informational support is offered by users at late quit stages to users at early quit stages. Nurturant support is also exchanged among users within the same quit stage. These findings help us understand how health consumers are supporting each other and reveal new capabilities of online intervention programs that can be designed to offer social support in a timely and effective manner.

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Abstract

For the sake of national security, very large volumes of data and information are generated and gathered daily. Much of this data and information is written in different languages, stored in different locations, and may be seemingly unconnected. Crosslingual semantic interoperability is a major challenge to generate an overview of this disparate data and information so that it can be analyzed, shared, searched, and summarized. The recent terrorist attacks and the tragic events of September 11, 2001 have prompted increased attention an national security and criminal analysis. Many Asian countries and cities, such as Japan, Taiwan, and Singapore, have been advised that they may become the next targets of terrorist attacks. Semantic interoperability has been a focus in digital library research. Traditional information retrieval (IR) approaches normally require a document to share some common keywords with the query. Generating the associations for the related terms between the two term spaces of users and documents is an important issue. The problem can be viewed as the creation of a thesaurus. Apart from this, terrorists and criminals may communicate through letters, e-mails, and faxes in languages other than English. The translation ambiguity significantly exacerbates the retrieval problem. The problem is expanded to crosslingual semantic interoperability. In this paper, we focus an the English/Chinese crosslingual semantic interoperability problem. However, the developed techniques are not limited to English and Chinese languages but can be applied to many other languages. English and Chinese are popular languages in the Asian region. Much information about national security or crime is communicated in these languages. An efficient automatically generated thesaurus between these languages is important to crosslingual information retrieval between English and Chinese languages. To facilitate crosslingual information retrieval, a corpus-based approach uses the term co-occurrence statistics in parallel or comparable corpora to construct a statistical translation model to cross the language boundary. In this paper, the text based approach to align English/Chinese Hong Kong Police press release documents from the Web is first presented. We also introduce an algorithmic approach to generate a robust knowledge base based an statistical correlation analysis of the semantics (knowledge) embedded in the bilingual press release corpus. The research output consisted of a thesaurus-like, semantic network knowledge base, which can aid in semanticsbased crosslingual information management and retrieval.

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Abstract

The category-tree document-classification structure is widely used by enterprises and information providers to organize, archive, and access documents for effective knowledge management. However, category trees from various sources use different hierarchical structures, which usually make mappings between categories in different category trees difficult. In this work, we propose a category-tree integration technique. We develop a method to learn the relationships between any two categories and develop operations such as mapping, splitting, and insertion for this integration. According to the parent-child relationship of the integrating categories, the developed decision rules use integration operations to integrate categories from the source category tree with those from the master category tree. A unified category tree can accumulate knowledge from multiple resources without forfeiting the knowledge in individual category trees. Experiments have been conducted to measure the performance of the integration operations and the accuracy of the integrated category trees. The proposed category-tree integration technique achieves greater than 80% integration accuracy, and the insert operation is the most frequently utilized, followed by map and split. The insert operation achieves 77% of F1 while the map and split operations achieves 86% and 29% of F1, respectively.

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Date

22.11.2008 17:57:22

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Date

22. 3.2009 12:51:47