Research of Intelligent Search Engine Based on Multi-Ontology

2012 ◽  
Vol 241-244 ◽  
pp. 1659-1663
Author(s):  
Shu Dong Zhang ◽  
Can Zhang ◽  
Jing Wang

With the development of the Semantic Web, ontology has become the primary means of expression of many fields of knowledge. Introducing the Semantic Web technology into the field of search engine is a valuable research topic. In order to meet the complex semantic retrieval demands, the paper proposes a search engine model based on multi-domain ontology, the model using ontology mapping rewrite the user query to achieve multiple ontology query, and provide a richer and accurate semantic information for the retrieval of cross-domain knowledge; And the paper proposes a method of cross-domain ontology annotation, providing a basis for the user semantic retrieval. The experimental results show that the search results improve the precision and recall rate.

2012 ◽  
Vol 220-223 ◽  
pp. 3058-3063
Author(s):  
Zhong Liu ◽  
Xian Guo Yan ◽  
Hong Guo ◽  
Shi Su ◽  
Chang Gui Xu ◽  
...  

Retrieval, recall rate and precision are not acceptable according to the general search engine in searching networked manufacturing resource. Manufacturing Resource Domain Ontology was derived and established based on the concept of the ontology in this paper. Model description was completed with protégé.


2013 ◽  
Vol 717 ◽  
pp. 736-741 ◽  
Author(s):  
Xue Jiang ◽  
Ying Liu ◽  
Ying Zhang ◽  
Zhen Fang ◽  
De Peng Dang

Domain ontology, which is widely used in knowledge engineering, artificial intelligence and semantic Web domain, describes concepts of the entities and mutual relationships between them. However, there is no existing research on construction of database domain ontology. Domain ontology of database will help to optimize associative knowledge learning in teaching and develop the intelligent tutoring system. It can also maximize domain knowledge sharing and reusing. In this paper, database domain ontology is constructed, and then we achieve the visualization. Finally we also implement the persistent storage of ontology and query.


2017 ◽  
pp. 030-050
Author(s):  
J.V. Rogushina ◽  

Problems associated with the improve ment of information retrieval for open environment are considered and the need for it’s semantization is grounded. Thecurrent state and prospects of development of semantic search engines that are focused on the Web information resources processing are analysed, the criteria for the classification of such systems are reviewed. In this analysis the significant attention is paid to the semantic search use of ontologies that contain knowledge about the subject area and the search users. The sources of ontological knowledge and methods of their processing for the improvement of the search procedures are considered. Examples of semantic search systems that use structured query languages (eg, SPARQL), lists of keywords and queries in natural language are proposed. Such criteria for the classification of semantic search engines like architecture, coupling, transparency, user context, modification requests, ontology structure, etc. are considered. Different ways of support of semantic and otology based modification of user queries that improve the completeness and accuracy of the search are analyzed. On base of analysis of the properties of existing semantic search engines in terms of these criteria, the areas for further improvement of these systems are selected: the development of metasearch systems, semantic modification of user requests, the determination of an user-acceptable transparency level of the search procedures, flexibility of domain knowledge management tools, increasing productivity and scalability. In addition, the development of means of semantic Web search needs in use of some external knowledge base which contains knowledge about the domain of user information needs, and in providing the users with the ability to independent selection of knowledge that is used in the search process. There is necessary to take into account the history of user interaction with the retrieval system and the search context for personalization of the query results and their ordering in accordance with the user information needs. All these aspects were taken into account in the design and implementation of semantic search engine "MAIPS" that is based on an ontological model of users and resources cooperation into the Web.


2021 ◽  
Vol 13 (5) ◽  
pp. 124
Author(s):  
Jiseong Son ◽  
Chul-Su Lim ◽  
Hyoung-Seop Shim ◽  
Ji-Sun Kang

Despite the development of various technologies and systems using artificial intelligence (AI) to solve problems related to disasters, difficult challenges are still being encountered. Data are the foundation to solving diverse disaster problems using AI, big data analysis, and so on. Therefore, we must focus on these various data. Disaster data depend on the domain by disaster type and include heterogeneous data and lack interoperability. In particular, in the case of open data related to disasters, there are several issues, where the source and format of data are different because various data are collected by different organizations. Moreover, the vocabularies used for each domain are inconsistent. This study proposes a knowledge graph to resolve the heterogeneity among various disaster data and provide interoperability among domains. Among disaster domains, we describe the knowledge graph for flooding disasters using Korean open datasets and cross-domain knowledge graphs. Furthermore, the proposed knowledge graph is used to assist, solve, and manage disaster problems.


2016 ◽  
Vol 25 (3) ◽  
pp. 460-466 ◽  
Author(s):  
Jiajia Hou ◽  
Hui Han ◽  
Chengjing Qiu ◽  
Dongmei Li

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