Abstract
It has been recognized that semantic data and knowledge extraction will significantly improve the capability of natural language interfaces to the semantic search engine. Semantic Web technology offers a vast scale of sharing and integration of distributed data sources by combining information easily. This will enable the user to find the information easily and efficiently. In this paper, we will explore some issues of developing algorithms for the Semantic Web. The first one to build the semantic contextual meaning by scanning the text, extract knowledge and automatically infer the meaning of the information from text that contains the search words in any sentence and correlate with hierarchical classes defined in the Ontology as a result of input resources. The second to discover the hierarchical relationships among terms (i.e. discover the semantic relations across hierarchical classifications). The proposed algorithm will be relying on a number of resources including Ontology and WordNet.
Highlights
There are many different design methodologies for software development, each having several advantages and disadvantages
In the Object Oriented Design (OOD) methodology, the analysis and design phases are closely coupled together which helps in developing a prototype of the problem domain a lot quicker compared to more traditional design approaches
This paper describes how the methods of the system is developed for (i) a general algorithm to build the semantic contextual meaning by scanning the text, extract entity or knowledge and correlate with hierarchical classes defined in the Ontology as a result of input resources (ii) a specific algorithm to discover the hierarchical relationships among terms
Summary
There are many different design methodologies for software development, each having several advantages and disadvantages. During the research stage of the author became aware of a powerful of WordNet component and Jena component which represent the foundation of the proposed system This system was chosen to provide the Knowledge Extraction capabilities needed as it offered all of the features required including named entity recognition [4]. Another benefit of choosing WordNet and Jena components was that it has been designed to be incorporated into other applications and there is a lot of documentation available explaining how to use it [12]. 2) Knowledge Extraction The knowledge Extraction component of the system will analyze web pages and extract specific information found within the text This component developed using features provided by WordNet. 3) Automatic Annotator This is the main component of the system.
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