Enhancing Virtual Ontology Based Access over Tabular Data with Morph-CSV

Tracking #: 2619-3833

David Chaves-Fraga
Edna Ruckhaus
Freddy Priyatna
Maria-Esther Vidal1
Oscar Corcho

Responsible editor: 
Guest Editors Web of Data 2020

Submission type: 
Full Paper
Ontology-Based Data Access (OBDA) has traditionally focused on providing a unified view of heterogeneous datasets (e.g., relational databases, CSV and JSON files), either by materializing integrated data into RDF or by performing on-the fly querying via SPARQL query translation. In the specific case of tabular datasets represented as several CSV or Excel files, query translation approaches have been applied by considering each source as a single table that can be loaded into a relational database management system (RDBMS). Nevertheless, constraints over these tables are not represented (e.g., referential integrity among sources, datatypes, or data integrity); thus, neither consistency among attributes nor indexes over tables are enforced. As a consequence, efficiency of the SPARQL-to-SQL translation process may be affected, as well as the completeness of the answers produced during the evaluation of the generated SQL query. Our work is focused on applying implicit constraints on the OBDA query translation process over tabular data. We propose Morph-CSV, a framework for querying tabular data that exploits information from typical OBDA inputs (e.g., mappings, queries) to enforce constraints that can be used together with any SPARQL-to-SQL OBDA engine. Morph-CSV relies on both a constraint component and a set of constraint operators. For a given set of constraints, the operators are applied to each type of constraint with the aim of enhancing query completeness and performance. We evaluate Morph-CSV in several domains: e-commerce with the BSBM benchmark; transportation with a benchmark using the GTFS dataset from the Madrid subway; and biology with a use case extracted from the Bio2RDF project. We compare and report the performance of two SPARQL-to-SQL OBDA engines, without and with the incorporation of MorphCSV. The observed results suggest that Morph-CSV is able to speed up the total query execution time by up to two orders of magnitude, while it is able to produce all the query answers.
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Solicited Reviews:
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Review #1
By Pieter Heyvaert submitted on 07/Jan/2021
Review Comment:

The authors addressed my comments. Great! Below are some minor remarks.

- H1: seems grammatically wrong: main verb missing?
- Plural of hypothesis is hypotheses
- Figure 3: what does green and red mean?

Review #2
Anonymous submitted on 10/Jan/2021
Review Comment:

I thank the authors for taking the time to reply to my comments. I think now the paper is even stronger and ready for publication.

Review #3
Anonymous submitted on 25/Jan/2021
Review Comment:

The authors successfully addressed my concerns and I recommend an accept.