Identifying motifs for evaluating open knowledge extraction on the Web

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摘要

Open Knowledge Extraction (OKE) is the process of extracting knowledge from text and representing it in formalized machine readable format, by means of unsupervised, open-domain and abstractive techniques. Despite the growing presence of tools for reusing NLP results as linked data (LD), there is still lack of established practices and benchmarks for the evaluation of OKE results tailored to LD. In this paper, we propose to address this issue by constructing RDF graph banks, based on the definition of logical patterns called OKE Motifs. We demonstrate the usage and extraction techniques of motifs using a broad-coverage OKE tool for the Semantic Web called FRED. Finally, we use identified motifs as empirical data for assessing the quality of OKE results, and show how they can be extended trough a use case represented by an application within the Semantic Sentiment Analysis domain.

论文关键词:Machine reading,Knowledge extraction,RDF,Semantic web,Linked open data

论文评审过程:Received 15 November 2015, Revised 10 May 2016, Accepted 11 May 2016, Available online 12 May 2016, Version of Record 12 August 2016.

论文官网地址:https://doi.org/10.1016/j.knosys.2016.05.023