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Knowledge Science, Engineering and Management: First International Conference, KSEM 2006, Guilin, China, August 5-8, 2006, Proceedings

Jérôme Lang ; Fangzhen Lin ; Ju Wang (eds.)

En conferencia: 1º International Conference on Knowledge Science, Engineering and Management (KSEM) . Guilin, China . August 5, 2006 - August 8, 2006

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Institución detectada Año de publicación Navegá Descargá Solicitá
No detectada 2006 SpringerLink

Información

Tipo de recurso:

libros

ISBN impreso

978-3-540-37033-8

ISBN electrónico

978-3-540-37035-2

Editor responsable

Springer Nature

País de edición

Reino Unido

Fecha de publicación

Información sobre derechos de publicación

© Springer-Verlag Berlin Heidelberg 2006

Tabla de contenidos

Si-SEEKER: Ontology-Based Semantic Search over Databases

Jun Zhang; Zhaohui Peng; Shan Wang; Huijing Nie

Keyword Search Over Relational Databases(KSORD) has been widely studied. While keyword search is helpful to access databases, it has inherent limitations. Keyword search doesn’t exploit the semantic relationships between keywords such as hyponymy, meronymy and antonymy, so the recall rate and precision rate are often dissatisfactory. In this paper, we have designed an ontology-based semantic search engine over databases called Si-SEEKER based on our i-SEEKER system which is a KSORD system with our candidate network selection techniques. Si-SEEKER extends i-SEEKER with semantic search by exploiting hierarchical structure of domain ontology and a generalized vector space model to compute semantic similarity between a user query and annotated data. We combine semantic search with keyword search over databases to improve the recall rate and precision rate of the KSORD system. We experimentally evaluate our Si-SEEKER system on the DBLP data set and show that Si-SEEKER is more effective than i-SEEKER in terms of the recall rate and precision rate of retrieval results.

- Regular Papers | Pp. 599-611

Efficient Computation of Multi-feature Data Cubes

Shichao Zhang; Rifeng Wang; Yanping Guo

A Multi-Feature Cube (MF-Cube) query is a complex-data-mining query based on data cubes, which computes the dependent complex aggregates at multiple granularities. Existing computations designed for simple data cube queries can be used to compute distributive and algebraic MF-Cubes queries. In this paper we propose an efficient computation of holistic MF-Cubes queries. This method computes holistic MF-Cubes with (Part Distributive Aggregate Property). The efficiency is gained by using dynamic subset data selection strategy (Iceberg query technique) to reduce the size of materialized data cube. Also for efficiency, this approach adopts the chunk-based caching technique to reuse the output of previous queries. We experimentally evaluate our algorithm using synthetic and real-world datasets, and demonstrate that our approach delivers up to about twice the performance of traditional computations.

- Regular Papers | Pp. 612-624

NKIMathE – A Multi-purpose Knowledge Management Environment for Mathematical Concepts

Qingtian Zeng; Cungen Cao; Hua Duan; Yongquan Liang

In 2001, , as the mathematics knowledge component of National Knowledge Infrastructure, was initiated to elaborate in China. In order to help knowledge engineers acquire and manage the mathematical knowledge especially the conceptual knowledge, a knowledge management environment, has been designed and developed. integrates three main components: (1) a platform for knowledge acquisition, syntax checking and organization for mathematical concepts; (2) a module for multi-lingual knowledge translation and transform for mathematical concepts; and (3) a Web-based and a mobile knowledge Q-A platforms for mathematical concepts.

- Regular Papers | Pp. 625-636

Linguistic Knowledge Representation and Automatic Acquisition Based on a Combination of Ontology with Statistical Method

Dequan Zheng; Tiejun Zhao; Sheng Li; Hao Yu

Due to the complexity and flexibility of natural language, linguistic knowledge representation, automatic acquisition and its application research becomes difficult. In this paper, a combination of ontology with statistical method is presented for linguistic knowledge representation and acquisition from training data. In this study, linguistic knowledge representaiton is firstly defined using ontology theory, and then, linguistical knowledge is automatically acquired by statistical method. In document processing, the semantic evaluation value of the document can be get by linguistic knowledge. The experimention in Chinese information retrieval and text classification shows the proposed method improves the precision of nature language processing.

- Regular Papers | Pp. 637-649

Toward Formalizing Usefulness in Propositional Language

Yi Zhou; Xiaoping Chen

In this paper, we attempt to capture the notion of in propositional language. We believe that classical implication captures a certain kind of usefulness, and name it . We say that a formula is strictly useful to a formula under a formula set Γif and only if implies under Γ in classical propositional logic. We also believe that classical implication is too strict to capture the whole notion of usefulness. Therefore, we extend it in two ways. The first one is , which means that if is true, then will be partially true under the background of Γ. The second one is , which means that the probability of is true will increase by given is true under Γ. This paper provides semantic definitions of them respectively in propositional language, and discusses the fundamental properties of them.

- Regular Papers | Pp. 650-661