Conceptual structures: information processing in mind and machine
Conceptual structures: information processing in mind and machine
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
Conceptual Graphs: Draft Proposed American National Standard
ICCS '99 Proceedings of the 7th International Conference on Conceptual Structures: Standards and Practices
Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema
ISWC '02 Proceedings of the First International Semantic Web Conference on The Semantic Web
Information Extraction with HMM Structures Learned by Stochastic Optimization
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: The Semantic Web: an evolution for a revolution
Kernel methods for relation extraction
The Journal of Machine Learning Research
Mining Generalized Associations of Semantic Relations from Textual Web Content
IEEE Transactions on Knowledge and Data Engineering
Introduction to information extraction
AI Communications
Exploring various knowledge in relation extraction
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
Automatic extraction of hierarchical relations from text
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
SVM based learning system for information extraction
Proceedings of the First international conference on Deterministic and Statistical Methods in Machine Learning
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Text data, which are represented as free text in World Wide Web (WWW), are inherently unstructured and hence it becomes difficult to directly process the text data by computer programs. There has been great interest in text mining techniques recently for helping users to quickly gain knowledge from the Web. Text mining technologies usually involve tasks such as text refining which transforms free text into an intermediate representation form which is machine-processable and knowledge distillation which deduces patterns or knowledge from the intermediate form. These text representation methodologies consider documents as bags of words and ignore the meanings and ideas their authors want to convey. As terms are treated as individual items in such simplistic representations, terms lose their semantic relations and texts lose their original meanings. In this paper, we propose a system that overcomes the limitations of the existing technologies to retrieve the information from the knowledge discovered through data mining based on the detailed meanings of the text. For this, we propose a Knowledge representation technique, which uses Resources Description Framework (RDF) metadata to represent the semantic relations, which are extracted from textual web document using natural language processing techniques. The main objective of the creation of RDF metadata in this system is to have flexibility for easy retrieval of the semantic information effectively. We also propose an effective SEMantic INformation RETrieval algorithm called SEMINRET algorithm. The experimental results obtained from this system show that the computations of Precision and Recall in RDF databases are highly accurate when compared to XML databases. Moreover, it is observed from our experiments that the document retrieval from the RDF database is more efficient than the document retrieval using XML databases.