Incremental SPARQL Query Processing

Abstract

Abstract. The number of linked data sources available on the Web is growing at a rapid rate. Moreover, users are showing an interest for any framework that allows them to obtain answers, for a formulated query, accessing heterogeneous data sources without the need of explicitly specifying the sources to answer the query. Our proposal focus on that interest and its goal is to build a system capable of answering to user queries in an incremental way. Each time a different data source is accessed the previous answer is eventually enriched. Brokering across the data sources is enabled by using source mapping relationships. User queries are rewritten using those mappings in order to obtain translations of the original query across data sources. Semantically equivalent translations are first looked for, but semantically approximated ones are generated if equivalence is not achieved. Well defined metrics are considered to estimate the information loss, if any.Keywords: Semantic Web, Linked Open Data Sources, query reformulation, query rewriting, ontology mapping. Problem Statement and Research QuestionThe Linked Open Data (LOD) initiative has made available to the users a large number of data sources from various domains such as education, life sciences, government data, literature, geography and others. Two commonly used approaches for query processing in this context are: 1) to query the different data sources independently, one by one; or 2) to integrate first the data sources into a local centralized warehouse and then to process queries in a centralized way on the warehouse. Both approaches present relevant problems such as the user needed expertise following the first approach and the scalability problems that arise in the second one. In this scenario an alternative approach is appearing, the so called federated approach, in which a query is formulated and its answer is obtained from different sources but with the distinguishing feature that the technical details associated to the distributed query answering process are transparent to the user. The work developed in this thesis is placed in this approach, but our system will have the added feature that the user does not need to have specific knowledge of the language in whichthe different data sources are modeled. We summarize our research question as the following one: How P.

Self-adaptive Based Model for Ambiguity Resolution of The Linked Data Query for Big Data Analytics

“…Several researches have studied on various issues to map and fuse data on multiple sources as a means to translate the user query. The efforts include user interface design [20][21], usability [5], data management model [22][23][24][25][26], query language format (i.e., SPARQL) [27][28], query expressivity [4], [29][30], mapping [9], [26], [31][32], fusing [33][34][35] and ranking [3], [36][37][38]. Most of these approaches rely on linguistic triple (Subject-Predicate-Object) identification [38] which may be grammar and language dependent.…”