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Journal of the ACM (JACM)
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Modern Information Retrieval
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The VLDB Journal — The International Journal on Very Large Data Bases
Product Data Integration in B2B E-Commerce
IEEE Intelligent Systems
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Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
A hybrid approach for searching in the semantic web
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Handbook on Ontologies (International Handbooks on Information Systems)
Handbook on Ontologies (International Handbooks on Information Systems)
A Semantic Classification Model for E-Catalogs
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Improving recommendation lists through topic diversification
WWW '05 Proceedings of the 14th international conference on World Wide Web
SemRank: ranking complex relationship search results on the semantic web
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Ranking Complex Relationships on the Semantic Web
IEEE Internet Computing
Ontological Modeling of e-Catalogs using EER and Description Logics
DEEC '05 Proceedings of the International Workshop on Data Engineering Issues in E-Commerce
Practical Issues for Building a Product Ontology System
DEEC '05 Proceedings of the International Workshop on Data Engineering Issues in E-Commerce
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
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GoodRelations: An Ontology for Describing Products and Services Offers on the Web
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GREENCOM-CPSCOM '10 Proceedings of the 2010 IEEE/ACM Int'l Conference on Green Computing and Communications & Int'l Conference on Cyber, Physical and Social Computing
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International Journal of Knowledge-based and Intelligent Engineering Systems
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Expert Systems with Applications: An International Journal
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An ontology-based product-recommender system can help catalog administrators in B2B marketplaces maintain up-to-date product databases by acquiring mapping information between the new product data and existing data. The proposed approach is keyword-based and independent of the underlying physical structure of product ontology. With a Bayesian belief network as its basis, the ranking algorithm utilizes semantics embedded within relationships defined in ontology to probabilistically determine the ranking scores. The methodology is implemented on a practical ontology system powerful enough to assist users in B2B marketplaces. Its effectiveness is demonstrated in comparison to the conventional search engines.