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Completeness and Consistency Analysis for Evolving Knowledge Bases

30 Pages Posted: 23 Nov 2018 First Look: Accepted

See all articles by Mohammad Rifat Ahmmad Rashid

Mohammad Rifat Ahmmad Rashid

Istituto Superiore Mario Boella (ISMB)

Giuseppe Rizzo

Istituto Superiore Mario Boella (ISMB)

Marco Torchiano

Polytechnic University of Turin

Nandana Mihindukulasooriya

Universidad Politécnica de Madrid - Ontology Engineering Group

Oscar Corcho

Universidad Politécnica de Madrid - Ontology Engineering Group; University of Manchester - School of Computer Science

Raul Garcıa-Castro

Universidad Politécnica de Madrid - Ontology Engineering Group

Abstract

Assessing the quality of an evolving knowledge base is a challenging task as it often requires to identify correct quality assessment procedures. Since data is often derived from autonomous, and increasingly large data sources, it is impractical to manually curate the data, and challenging to continuously and automatically assess their quality. In this paper, we explore two main areas of quality assessment related to evolving knowledge bases: (i) identification of completeness issues using knowledge base evolution analysis, and (ii) identification of consistency issues based on integrity constraints, such as minimum and maximum cardinality, and range constraints. For completeness analysis, we use data profiling information from consecutive knowledge base releases to estimate completeness measures that allow predicting quality issues. Then, we perform consistency checks to validate the results of the completeness analysis using integrity constraints and learning models. The approach has been tested both quantitatively and qualitatively by using a subset of datasets from both DBpedia and 3cixty knowledge bases. The performance of the approach is evaluated using precision, recall, and F1 score. From completeness analysis, we observe a 94% precision for the English DBpedia KB and 95% precision for the 3cixty Nice KB. We also assessed the performance of our consistency analysis by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. We observed that the best performing model in our experimental setup is the Random Forest, reaching an F1 score greater than 90% for minimum and maximum cardinality and 84% for range constraints.

Keywords: Quality Assessment, Evolution Analysis, Validation, Knowledge Base, RDF Shape, Machine Learning

Suggested Citation

Rashid, Mohammad Rifat Ahmmad and Rizzo, Giuseppe and Torchiano, Marco and Mihindukulasooriya, Nandana and Corcho, Oscar and Garcıa-Castro, Raul, Completeness and Consistency Analysis for Evolving Knowledge Bases (November 23, 2018). Available at SSRN: https://ssrn.com/abstract=3289666 or http://dx.doi.org/10.2139/ssrn.3289666

Mohammad Rifat Ahmmad Rashid (Contact Author)

Istituto Superiore Mario Boella (ISMB)

Via Pier Carlo Boggio
Turin
Italy

Giuseppe Rizzo

Istituto Superiore Mario Boella (ISMB)

Via Pier Carlo Boggio
Turin
Italy

Marco Torchiano

Polytechnic University of Turin

Corso Duca degli Abruzzi, 24
Torino, Torino 10129
Italy

Nandana Mihindukulasooriya

Universidad Politécnica de Madrid - Ontology Engineering Group

Madrid
Spain

Oscar Corcho

Universidad Politécnica de Madrid - Ontology Engineering Group ( email )

Madrid
Spain

University of Manchester - School of Computer Science

Kilburn Building, Oxford Road
Manchester M13 9GH, M13 9PL
United Kingdom

Raul Garcıa-Castro

Universidad Politécnica de Madrid - Ontology Engineering Group ( email )

Madrid
Spain

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