Linked Open Data Validity -- A Technical Report from ISWS 2018

TL;DR

This technical report from ISWS 2018 explores the challenges of ensuring validity in Linked Open Data, highlighting issues like data quality and heterogeneity, through collaborative research by students and senior researchers.

Contribution

It presents a comprehensive investigation into LOD validity, combining multiple perspectives and approaches from a collaborative academic effort.

Findings

Identified key challenges in LOD validity such as data quality and semantic heterogeneity.

Proposed multiple approaches to assess and improve LOD validity.

Highlighted the importance of collaborative efforts in addressing LOD issues.

Abstract

Linked Open Data (LOD) is the publicly available RDF data in the Web. Each LOD entity is identfied by a URI and accessible via HTTP. LOD encodes globalscale knowledge potentially available to any human as well as artificial intelligence that may want to benefit from it as background knowledge for supporting their tasks. LOD has emerged as the backbone of applications in diverse fields such as Natural Language Processing, Information Retrieval, Computer Vision, Speech Recognition, and many more. Nevertheless, regardless of the specific tasks that LOD-based tools aim to address, the reuse of such knowledge may be challenging for diverse reasons, e.g. semantic heterogeneity, provenance, and data quality. As aptly stated by Heath et al. Linked Data might be outdated, imprecise, or simply wrong": there arouses a necessity to investigate the problem of linked data validity. This work reports a collaborative effort performed by nine teams of students, guided by an equal number of senior researchers, attending the International Semantic Web Research School (ISWS 2018) towards addressing such investigation from different perspectives coupled with different approaches to tackle the issue.