In recent years, the Resource Description Framework (RDF) has become the de-facto standard to represent heterogeneous semi-structured data on the web. RDF datasets are interrogated with SPARQL, a structured query language which is often not intuitive for the nonexpert users, due to its syntax and the necessity to know the structure of the underlying graph. A simpler paradigm like keyword search can help in this regard to access these databases. Moreover, nowadays datasets constitute the backbone of the scientific research, and thus they should be cited as any other scholarly publication. RDF presents a new challenge in the automatic creation of textual citation since it lacks the structure of RDB and XML databases. In this work, we discuss the design and development of a system which will perform keyword-search on RDF graphs and, given the results, will create the textual citation for the final user.

A Keyword Search and Citation System for RDF Graphs

Dennis Dosso
2019

Abstract

In recent years, the Resource Description Framework (RDF) has become the de-facto standard to represent heterogeneous semi-structured data on the web. RDF datasets are interrogated with SPARQL, a structured query language which is often not intuitive for the nonexpert users, due to its syntax and the necessity to know the structure of the underlying graph. A simpler paradigm like keyword search can help in this regard to access these databases. Moreover, nowadays datasets constitute the backbone of the scientific research, and thus they should be cited as any other scholarly publication. RDF presents a new challenge in the automatic creation of textual citation since it lacks the structure of RDB and XML databases. In this work, we discuss the design and development of a system which will perform keyword-search on RDF graphs and, given the results, will create the textual citation for the final user.
2019
Proceedings of the 9th PhD Symposium on Future Directions in Information Access co-located with 12th European Summer School in Information Retrieval {ESSIR 2019)
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11577/3346871
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