LOD search engine: A semantic search over linked data

摘要

In the last few years, there has been a significant growth in the amount of data published in RDF and adoption of Linked Data principles. Every day, a large number of people and communities contribute to the publication of datasets as Linked Data on Linked Open Data (LOD) cloud. Due to a large size of LOD cloud on the Web and the RDF representation of linked dataset, searching and retrieving relevant data on the Web is a major challenge. Because the data is published in RDF triple format, i.e. an interlinked structure, traditional search engines are unable to perform searches on Linked Data. This article introduces LOD search engine, a novel semantic search engine that searches on Semantic Web documents (such as Linked Data or triples) to retrieve a set of relevant information based on user queries. For searching over triples, we proposed two semantic search methods: Forward Search and Backward Search. To improve search results, two new ranking methods have also been introduced: Domain Ranking and Triple Ranking. The proposed LOD search engine produced remarkable results and outperformed other semantic search engines. In the best-case scenario, the proposed LOD search engine outperforms the swoogle and falcons by 22.35%, 43.38% and 33.18% in terms of precision, recall, and F-Measure respectively.