A semantic retrieval framework for engineering domain knowledge

Authors:
Xutang Zhang;Xiaofeng Chen;Xin Hou;Ting Zhuang
Affiliations:
School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China;School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China;Science and Technology on Electronmagnetic Scattering Laboratory, Beijing, China;School of Mechatronics Engineering, Harbin Institute of Technology, Harbin, Heilongjiang, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
Year:
2011

Citing 8
Cited 0

An Adaptation of the Vector-Space Model for Ontology-Based Information Retrieval

IEEE Transactions on Knowledge and Data Engineering
Ontology learning: state of the art and open issues

Information Technology and Management
A Latent Semantic Indexing-based approach to multilingual document clustering

Decision Support Systems
Ontology based personalized route planning system using a multi-criteria decision making approach

Expert Systems with Applications: An International Journal
Developing a semantic-enable information retrieval mechanism

Expert Systems with Applications: An International Journal
Ontology based semantic conflicts resolution in collaborative editing of design documents

Advanced Engineering Informatics
Clustered SVD strategies in latent semantic indexing

Information Processing and Management: an International Journal
GRAONTO: A graph-based approach for automatic construction of domain ontology

Expert Systems with Applications: An International Journal

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we propose a knowledge retrieval framework based on semantically annotated engineering ontology generated from domain documents. Particularly, we propose a scheme for build relations between ontology and domain documents. First, we build anthologies in engineering domain. Next, we transform the keywords into domain ontology concepts, and then find the synonyms of these keywords which are used as real queries to directly input into the query system. The semantic-based knowledge search and retrieval is then performed by ontology mapping and comparison. Using the semantic network of ontology, this system not only can conduct keyword-based retrieval, but also can understand the queries and answer questions by fuzzy inference based on domain ontology.