The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
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The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Uma das maneiras mais convenientes de consultar dados XML é uma pesquisa por palavra-chave, pois não requer nenhum conhecimento da estrutura XML ou aprendizado de uma nova interface de usuário. No entanto, a pesquisa por palavra-chave é ambígua. Os usuários podem usar termos diferentes para pesquisar as mesmas informações. Além disso, é difícil para um sistema decidir qual nó provavelmente será escolhido como nó de retorno e quanta informação deve ser incluída no resultado. Para enfrentar esses desafios, propomos uma busca semântica XML baseada em palavras-chave chamada XSemantic. Por um lado, damos três definições para completar em termos de semântica. Em primeiro lugar, a expansão do termo semântico, nosso sistema é robusto a partir de palavras-chave ambíguas usando a ontologia de domínio. Em segundo lugar, para retornar respostas semânticas significativas, inferimos automaticamente as informações de retorno das consultas do usuário e aproveitamos o caminho mais curto para retornar conexões significativas entre palavras-chave. Em terceiro lugar, apresentamos o ranking semântico que reflete o grau de similaridade, bem como o relacionamento semântico para que os resultados da pesquisa com maior relevância sejam apresentados primeiro aos usuários. Por outro lado, nas abordagens ACV e busca por proximidade, investigamos o problema da informação incluída nos resultados da busca. Portanto, introduzimos a noção de Menor Ancestral de Elemento Comum (LCEA) e definimos nossa regra simples sem qualquer exigência nas informações do esquema, como DTD ou Esquema XML. O primeiro experimento indicou que o XSemântico não apenas infere adequadamente as informações de retorno, mas também gera resultados compactos e significativos. Além disso, os benefícios da semântica proposta são demonstrados pelo segundo experimento.
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Umaporn SUPASITTHIMETHEE, Toshiyuki SHIMIZU, Masatoshi YOSHIKAWA, Kriengkrai PORKAEW, "XSemantic: An Extension of LCA Based XML Semantic Search" in IEICE TRANSACTIONS on Information,
vol. E92-D, no. 5, pp. 1079-1092, May 2009, doi: 10.1587/transinf.E92.D.1079.
Abstract: One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E92.D.1079/_p
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@ARTICLE{e92-d_5_1079,
author={Umaporn SUPASITTHIMETHEE, Toshiyuki SHIMIZU, Masatoshi YOSHIKAWA, Kriengkrai PORKAEW, },
journal={IEICE TRANSACTIONS on Information},
title={XSemantic: An Extension of LCA Based XML Semantic Search},
year={2009},
volume={E92-D},
number={5},
pages={1079-1092},
abstract={One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.},
keywords={},
doi={10.1587/transinf.E92.D.1079},
ISSN={1745-1361},
month={May},}
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TY - JOUR
TI - XSemantic: An Extension of LCA Based XML Semantic Search
T2 - IEICE TRANSACTIONS on Information
SP - 1079
EP - 1092
AU - Umaporn SUPASITTHIMETHEE
AU - Toshiyuki SHIMIZU
AU - Masatoshi YOSHIKAWA
AU - Kriengkrai PORKAEW
PY - 2009
DO - 10.1587/transinf.E92.D.1079
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E92-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2009
AB - One of the most convenient ways to query XML data is a keyword search because it does not require any knowledge of XML structure or learning a new user interface. However, the keyword search is ambiguous. The users may use different terms to search for the same information. Furthermore, it is difficult for a system to decide which node is likely to be chosen as a return node and how much information should be included in the result. To address these challenges, we propose an XML semantic search based on keywords called XSemantic. On the one hand, we give three definitions to complete in terms of semantics. Firstly, the semantic term expansion, our system is robust from the ambiguous keywords by using the domain ontology. Secondly, to return semantic meaningful answers, we automatically infer the return information from the user queries and take advantage of the shortest path to return meaningful connections between keywords. Thirdly, we present the semantic ranking that reflects the degree of similarity as well as the semantic relationship so that the search results with the higher relevance are presented to the users first. On the other hand, in the LCA and the proximity search approaches, we investigated the problem of information included in the search results. Therefore, we introduce the notion of the Lowest Common Element Ancestor (LCEA) and define our simple rule without any requirement on the schema information such as the DTD or XML Schema. The first experiment indicated that XSemantic not only properly infers the return information but also generates compact meaningful results. Additionally, the benefits of our proposed semantics are demonstrated by the second experiment.
ER -