Semankey: A Semantics-Driven Approach for Querying RDF Repositories Using Keywords

Abstract

The Web of Data aims at linking Internet data repositories. Semantic Web technologies make data easily readable by computer agents, enabling the automation of complex tasks and facilitating data integration. They facilitate the achievement of the Web of Data in which users can query the connected datasets in the search engine style, i.e. by using keywords. However, querying semantic repositories in a friendly way, not requiring the mastering of query languages such as SPARQL, is still a challenging task. In this work, we present Semankey, an approach for the automatic building of SPARQL queries from a list of keywords entered by the user. Semankey identifies semantic entities in the keywords by using a domain ontology to interpret the query meaning and automatically builds a set of queries by connecting the entities through the relationships described in the ontology and by applying query size-based heuristics. The main contributions of Semankey are the use of query filters and the generation of multiple SPARQL queries derived from the different interpretations of the given input, according to the underlying domain ontology. We used the data from the Question Answering over Linked Data challenge for evaluating our approach in different execution modes and for analysing the query trees generated, obtaining a precision of 0.52 and a recall of 0.60 when considering the best answer provided per test case.

Design of a blockchain-based secure and efficient ontology generation model for multiple data genres using augmented stratification in the healthcare industry

“…Ontologies can be used for multimedia data representation [16], Gastroenterology and other medical elds which include infectious disease ontologies [17,18], transaction ontologies based on smart contracts [19], and health record management ontologies [20], which assist in reducing system dependency on external sources, thereby improving their query performance. Similarly, the work de ned in [21,22,23,24] also proposes ontology models for renewable energy sources, keyword-based search applications, eLearning applications, and internet of medical things (IoMT) applications, where high e ciency of system design with minimum system complexity is needed. E cient models are also proposed in [25,26,27,28] which discuss use of secure blockchains for real-time scenarios under different applications.…”

Question Answer System: A State-of-Art Representation of Quantitative and Qualitative Analysis

“…Dependency tree and question analysis done in initial data transformation help generate the query. Semankey [126] generates the multiple trees for each combination of extracted entities by considering the underlying ontology and using BFS-based algorithm. Various trees are generated by starting the BFS from the main entity of interest.…”