A Proposal for a Two-Way Journey on Validating Locations in Unstructured and Structured Data

Keles, Ilkcan; Qawasmeh, Omar; Tietz, Tabea; Marinucci, Ludovica; Reda, Roberto; van Erp, Marieke

doi:10.4230/OASIcs.LDK.2019.13

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Document Identifiers

DOI: 10.4230/OASIcs.LDK.2019.13
URN: urn:nbn:de:0030-drops-103778

Author Details

Ilkcan Keles

Aalborg University, Dept. of Computer Science, Denmark

Omar Qawasmeh

Univ. Lyon, CNRS, Lab. Hubert Curien UMR 5516, F-42023 Saint-Étienne, France

Tabea Tietz

FIZ Karlsruhe - Leibniz Institute for Information Infrastructure, Germany
Karlsruhe Institute of Technology, Germany

Ludovica Marinucci

Semantic Technology Laboratory (STLab), Istituto di Scienze e Tecnologie della Cognizione-Consiglio Nazionale delle Ricerche (ISTC-CNR), Rome, Italy

Roberto Reda

Department of Computer Science and Engineering, University of Bologna, Italy

Marieke van Erp

KNAW Humanities Cluster, DHLab, The Netherlands

Acknowledgements

This work was made possible by the http://stlab.istc.cnr.it/isws/wordpress/ in Bertinoro, July 2018. The authors would like to thank the Summer School directors, Valentina Presutti and Harald Sack, as well as the tutors, the organizing team and the fellow students, in particular Amanda Pacini de Moura, Amr Azzam and Amina Annane for their suggestions and input.

Cite AsGet BibTex

Ilkcan Keles, Omar Qawasmeh, Tabea Tietz, Ludovica Marinucci, Roberto Reda, and Marieke van Erp. A Proposal for a Two-Way Journey on Validating Locations in Unstructured and Structured Data. In 2nd Conference on Language, Data and Knowledge (LDK 2019). Open Access Series in Informatics (OASIcs), Volume 70, pp. 13:1-13:8, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2019)
https://doi.org/10.4230/OASIcs.LDK.2019.13

Abstract

The Web of Data has grown explosively over the past few years, and as with any dataset, there are bound to be invalid statements in the data, as well as gaps. Natural Language Processing (NLP) is gaining interest to fill gaps in data by transforming (unstructured) text into structured data. However, there is currently a fundamental mismatch in approaches between Linked Data and NLP as the latter is often based on statistical methods, and the former on explicitly modelling knowledge. However, these fields can strengthen each other by joining forces. In this position paper, we argue that using linked data to validate the output of an NLP system, and using textual data to validate Linked Open Data (LOD) cloud statements is a promising research avenue. We illustrate our proposal with a proof of concept on a corpus of historical travel stories.

Subject Classification

ACM Subject Classification

Computing methodologies → Natural language processing

Keywords

data validity
natural language processing
linked data

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