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EvOLoD: linked data evolution on the Semantic Web


The possibility of interconnecting different structured data repositories can significantly change the way knowledge is published and consumed on the Web. To this end, ontologies aim to represent semantics in computational systems. They consist of a syntactic structure that models the concepts of a knowledge domain, and serve as schemas that organize data expressing instances of concepts according to logical properties. In the Semantic Web, ontologies support the publication and explicit linkage of structured and semantically defined data elements from different sources. In addition to establishing correspondences between instance of concepts (i.e., instance-instance linking), ontologies are references for generating semantic annotations (i.e., instance-document relation). Annotations consist of semantically explicit relationships denoted from an ontology concept to a textual element of a Web document. This allows the semantic interpretation of annotated data and information, and the consequent logical reasoning to support sophisticated queries. However, the Web is dynamic and the defined structured data tend to evolve through new versions of knowledge bases that are regularly released. This scenario implies in complex research challenges because the definition of concepts' properties and their instances tend to suffer modifications (e.g, removals and changes of values) and it potentially impacts on the quality of a huge volume of existing data links and annotations.This research project aims to study the evolution of linked data in the Semantic Web. We aim to construct a framework that allows capturing and characterizing changes in structured data repositories and automatically implement adaptations on existing links and annotations affected by those changes. Firstly, we will conduct extensive experiments with real-world data to understand key factors that influence the evolution of linked data. Considering the requirements raised by the experiments, the proposal defines original and adequate methods to semi-automatically detect changes in linked data repositories and apply correction operations on links between different repositories, and on annotations. The framework explores Semantic Web technologies such as RDF (S) and ontology description language (OWL). In addition to the formalization of the methods, we will carry out experimental validations to empirically and thoroughly evaluate the solutions developed in realistic case studies. Evaluation measures such as precision and accuracy will be calculated and analyzed to examine the effectiveness of the methods. The expected contributions involve new algorithms and software tools implementing the proposed methods in a complete framework that might improve the consistency of the linked data evolution. (AU)

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Scientific publications (7)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
LUCCA TOSI, MAURO DALLE; DOS REIS, JULIO CESAR. nderstanding the evolution of a scientific field by clustering and visualizing knowledge graph. JOURNAL OF INFORMATION SCIENCE, v. 48, n. 1, . (17/02325-5, 13/08293-7)
DE ARAUJO, RICARDO JOSE; DOS REIS, JULIO CESAR; BONACIN, RODRIGO. Understanding interface recoloring aspects by colorblind people: a user study. UNIVERSAL ACCESS IN THE INFORMATION SOCIETY, v. 19, n. 1, p. 81-98, . (17/02325-5)
VICTORELLI, ELIANE ZAMBON; DOS REIS, JULIO CESAR; HORNUNG, HEIKO; PRADO, ALYSSON BOLOGNESI. Understanding human-data interaction: Literature review and recommendations for design. INTERNATIONAL JOURNAL OF HUMAN-COMPUTER STUDIES, v. 134, p. 13-32, . (17/02325-5, 15/24300-9)
ROSSANEZ, ANDERSON; DOS REIS, JULIO CESAR; TORRES, RICARDO DA SILVA; DE RIBAUPIERRE, HELENE. KGen: a knowledge graph generator from biomedical scientific literature. BMC MEDICAL INFORMATICS AND DECISION MAKING, v. 20, n. 4, SI, . (17/02325-5)
DE MENDONCA, RICARDO RESENDE; DE BRITO, DANIEL FELIX; ROSA, FERRUCIO DE FRANCO; DOS REIS, JULIO CESAR; BONACIN, RODRIGO. A Framework for Detecting Intentions of Criminal Acts in Social Media: A Case Study on Twitter. INFORMATION, v. 11, n. 3, . (17/02325-5)
REGINO, ANDRE GOMES; DOS REIS, JULIO CESAR; BONACIN, RODRIGO; MORSHED, AHSAN; SELLIS, TIMOS; CORCHO, OSCAR. Link maintenance for integrity in linked open data evolution: Literature survey and open challenges. SEMANTIC WEB, v. 12, n. 3, p. 517-541, . (13/08293-7, 18/08082-0, 18/14199-7, 17/02325-5)
TOSI, MAURO DALLE LUCCA; DOS REIS, JULIO CESAR. SciKGraph: A knowledge graph approach to structure a scientific field. Journal of Informetrics, v. 15, n. 1, . (13/08293-7, 17/02325-5)

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