文章基本信息

标题：A Proposal for a Two-Way Journey on Validating Locations in Unstructured and Structured Data
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作者：Ilkcan Keles ; Omar Qawasmeh ; Tabea Tietz 等
期刊名称：OASIcs : OpenAccess Series in Informatics
电子版ISSN：2190-6807
出版年度：2019
卷号：70
页码：13:1-13:8
DOI：10.4230/OASIcs.LDK.2019.13
出版社：Schloss Dagstuhl -- Leibniz-Zentrum fuer Informatik
摘要：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.
关键词：data validity; natural language processing; linked data