5 results on '"Ratprasartporn, Nattakarn"'
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2. Finding Related Papers in Literature Digital Libraries
- Author
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Ratprasartporn, Nattakarn, primary and Ozsoyoglu, Gultekin, additional
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3. Finding Related Papers in Literature Digital Libraries.
- Author
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Hutchison, David, Kanade, Takeo, Kittler, Josef, Kleinberg, Jon M., Mattern, Friedemann, Mitchell, John C., Naor, Moni, Nierstrasz, Oscar, Pandu Rangan, C., Steffen, Bernhard, Sudan, Madhu, Terzopoulos, Demetri, Tygar, Doug, Vardi, Moshe Y., Weikum, Gerhard, Kovács, László, Fuhr, Norbert, Meghini, Carlo, Ratprasartporn, Nattakarn, and Ozsoyoglu, Gultekin
- Abstract
This paper is about searching literature digital libraries to find "related" publications of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion across query output publications. In this paper, we propose a new way to measure "relatedness" by incorporating "contexts" (representing topics) of publications. We utilize existing ontology terms as contexts for publications, i.e., publications are assigned to their relevant contexts, where a context characterizes one or more publication topics. We define three ways of context-based relatedness, namely, (a) relatedness between two contexts (context-to-context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper-to-context relatedness), which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper-to-paper relatedness) by using both paper-to-context and context-to-context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics-oriented publications, our experiments indicate that the context-based approach is accurate, and solves the topic diffusion problem by effectively classifying and ranking related papers of a given paper based on the selected contexts of the paper. [ABSTRACT FROM AUTHOR]
- Published
- 2007
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4. Scalability of Databases for Digital Libraries.
- Author
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Fox, Edward A., Neuhold, Erich J., Premsmit, Pimrumpai, Wuwongse, Vilas, Chmura, John, Ratprasartporn, Nattakarn, and Ozsoyoglu, Gultekin
- Abstract
Search engines of main-stream literature digital libraries such as ACM Digital Library, Google Scholar, and PubMed employ file-based systems, and provide users with a basic boolean keyword search functionalities. As a result, new and powerful querying capabilities are not easy to implement on top of such systems, and not provided. In comparison, query languages of database systems traditionally have high expressive power. This paper evaluates the scalability of the approach of deploying relational databases as backend systems to digital libraries, and, thus, making use of the query languages and the query processing capabilities of database query engines for literature digital libraries. To evaluate our approach, we built a scalable prototype digital library built on top of a relational database management system, and its advanced query interface which allows users to specify dynamic text and path queries in an intuitive, hierarchical manner. This paper evaluates the scalability of two search query processing approaches, namely, ad-hoc queries, pre-compiled queries (stored-procedures). We demonstrate that, with reasonably priced hardware, we are able to build an RDBMS-based digital library search engine that can scale to handle millions of queries per day. Keywords: Scalability, Database, Metadata, Path Query, Query Interface. [ABSTRACT FROM AUTHOR]
- Published
- 2005
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5. CONTEXT-BASED PUBLICATION SEARCH PARADIGM IN LITERATURE DIGITAL LIBRARIES
- Author
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Ratprasartporn, Nattakarn
- Subjects
- Computer Science, Papers to Contexts, Search Results, Query Contexts, CONTEXT-BASED
- Abstract
This thesis identifies two problems with the task of searching literature digital libraries: (a) there are no effective paper-scoring and ranking mechanisms. Without a scoring and ranking system, users are often forced to scan a large and diverse set of publications listed as search results and potentially miss the important ones. (b) Topic diffusion is a common problem: publications returned by a keyword-based search query often fall into multiple topic areas, not all of which are of interest to users. As a response to the problems listed above, this thesis proposes a new literature digital library search paradigm, called context-based search, which effectively ranks search outputs and controls the topic diversity of keyword-based search query outputs. Our approach can be summarized as follows. During pre-querying, publications are classified to pre-specified ontology-based contexts, and query-independent context scores are attached to papers with respect to their assigned contexts. When a query is posed, relevant contexts are selected, search is performed within the selected contexts, context scores of publications are revised into relevancy scores with respect to the query at hand and the context that they are in, and query outputs are ranked within each relevant context. With the context-based search approach, (1) query output topic diversity is minimized, (2) query output size is reduced, (3) user time spent scanning query results is decreased, and (4) query output ranking accuracy is increased. In addition to keyword-based search, one important feature in searching literature digital libraries is to find “related publications” of a given publication. Existing approaches do not take into account publication topics in the relatedness computation, allowing topic diffusion to permeate across query output publications. In this thesis, we propose a new way to measure “relatedness” by incorporating “contexts” of publications. We define three ways of context-based relatedness, namely, (a) relatedness between two contexts (context-to-context relatedness) by using publications that are assigned to the contexts and the context structures in the context hierarchy, (b) relatedness between a context and a paper (paper-to-context relatedness), which is used to rank the relatedness of contexts with respect to a paper, and (c) relatedness between two papers (paper-to-paper relatedness) by using both paper-to-context and context-to-context relatedness measurements. Using existing biomedical ontology terms as contexts for genomics-oriented publications, our experiments indicate that the context-based approach is highly accurate and effectively solves the topic diffusion problem across search results.
- Published
- 2008
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