Search

Nowadays, the demand on querying and searching the Semantic Web is increasing. Some systems have adopted IR (Information Retrieval) approaches to index and search the Semantic Web data due to its capability to handle the Web-scale data and efficiency on query answering. Additionally, the huge volumes of data on the Semantic Web are frequently updated. Thus, it further requires effective update mechanisms for these systems to handle the data change. However, the existing update approaches only focus on document. It still remains a big challenge to update IR index specially designed for semantic data in the form of finer grained structured objects rather than unstructured documents. In this paper, we present a well-designed update mechanism on the IR index for triples. Our approach provides a flexible and effective update mechanism by dividing the index into blocks. It reduces the number of update operations during the insertion of triples. At the same time, it preserves the efficiency on query processing and the capability to handle large scale semantic data. Experimental results show that the index update time is a fraction of that by complete reconstruction w.r.t. the portion of the inserted triples. Moreover, the query response time is not notably affected. Thus, it is capable to make newly arrived semantic data immediately searchable for users. [ABSTRACT FROM AUTHOR]

As an important step towards bootstrapping the Semantic Web, many efforts have been made to extract triples from Wikipedia because of its wide coverage, good organization and rich knowledge. One kind of important triples is about Wikipedia articles and their non-isa properties, e.g. (Beijing, country, China). Previous work has tried to extract such triples from Wikipedia infoboxes, article text and categories. The infobox-based and text-based extraction methods depend on the infoboxes and suffer from a low article coverage. In contrast, the category-based extraction methods exploit the widespread categories. However, they rely on predefined properties, which is too effort-consuming and explores only very limited knowledge in the categories. This paper automatically extracts properties and triples from the less explored Wikipedia categories so as to achieve a wider article coverage with less manual effort. We manage to realize this goal by utilizing the syntax and semantics brought by super-sub category pairs in Wikipedia. Our prototype implementation outputs about 10M triples with a 12-level confidence ranging from 47.0% to 96.4%, which cover 78.2% of Wikipedia articles. Among them, 1.27M triples have confidence of 96.4%. Applications can on demand use the triples with suitable confidence. [ABSTRACT FROM AUTHOR]

The increasing amount of data on the Semantic Web offers opportunities for semantic search. However, formal query hinders the casual users in expressing their information need as they might be not familiar with the query΄s syntax or the underlying ontology. Because keyword interfaces are easier to handle for casual users, many approaches aim to translate keywords to formal queries. However, these approaches yet feature only very basic query ranking and do not scale to large repositories. We tackle the scalability problem by proposing a novel clustered-graph structure that corresponds to only a summary of the original ontology. The so reduced data space is then used in the exploration for the computation of top-k queries. Additionally, we adopt several mechanisms for query ranking, which can consider many factors such as the query length, the relevance of ontology elements w.r.t. the query and the importance of ontology elements. The experimental results performed against our implemented system Q2Semantic show that we achieve good performance on many datasets of different sizes. [ABSTRACT FROM AUTHOR]