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

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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

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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.
Original languageEnglish
Title of host publication2nd Conference on Language, Data and Knowledge (LDK 2019)
EditorsMaria Eskevich, Gerard de Melo, Christian Fath, John P. McCrae, Paul Buitelaar, Christian Chiarcos, Bettina Klimek, Milan Dojchinovski
Number of pages8
PublisherSchloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Publication date2019
Pages13:1-13:8
Article number13
ISBN (Print)978-3-95977-105-4
ISBN (Electronic)9783959771054
DOIs
Publication statusPublished - 2019
EventConference on Language, Data and Knowledge - Leipzig, Germany
Duration: 20 May 201923 May 2019
Conference number: 2nd
http://2019.ldk-conf.org/

Conference

ConferenceConference on Language, Data and Knowledge
Number2nd
CountryGermany
CityLeipzig
Period20/05/201923/05/2019
Internet address
SeriesOpen Access Series in Informatics
Volume70
ISSN2190-6807

Keywords

  • Data validity
  • Linked data
  • Natural language processing

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