Search (84 results, page 5 of 5)

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Abstract

The study aims to solve how to construct the semantic relations of specific domain terms by applying linguistic rules. The semantic structure analysis at the morpheme level was used for semantic measure, and a morpheme-based term association model was proposed by improving and combining the literal-based similarity algorithm and co-occurrence relatedness methods. This study provides a novel insight into the method of semantic analysis and calculation by morpheme parsing, and the proposed solution is feasible for the automatic association of compound terms. The results show that this approach could be used to construct appropriate term association and form a reasonable structural knowledge graph. However, due to linguistic differences, the viability and effectiveness of the use of our method in non-Chinese linguistic environments should be verified.

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Abstract

Search Engines have become an integral part of our day to day life. Our reliance on search engines increases with every passing day. With the amount of data available on Internet increasing exponentially, it becomes important to develop new methods and tools that help to return results relevant to the queries and reduce the time spent on searching. The results should be diverse but at the same time should return results focused on the queries asked. Relation Based Page Rank [4] algorithms are considered to be the next frontier in improvement of Semantic Web Search. The probability of finding relevance in the search results as posited by the user while entering the query is used to measure the relevance. However, its application is limited by the complexity of determining relation between the terms and assigning explicit meaning to each term. Trust Rank is one of the most widely used ranking algorithms for semantic web search. Few other ranking algorithms like HITS algorithm, PageRank algorithm are also used for Semantic Web Searching. In this paper, we will provide a comparison of few ranking approaches.

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