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Automatic Knowledge Extraction to build Semantic Web of Things Applications.

Mahda Noura1, Amelie Gyrard2, Sebastian Heil1

  • 1Technische Universität Chemnitz, Germany.

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Summary

We developed KE4WoT to automatically extract key topics from Internet of Things (IoT) literature, enhancing semantic interoperability for the Web of Things (WoT) and supporting the iot.schema.org vocabulary development.

Keywords:
Internet of Things (IoT)Knowledge ExtractionMachine Learning (ML)Natural Language Processing (NLP)OntologiesSemantic Web of Things (SWoT)Web of Things (WoT)

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Area of Science:

  • Computer Science
  • Semantic Web Technologies
  • Artificial Intelligence

Background:

  • The Internet of Things (IoT) aims for hyper-connectivity but faces interoperability challenges, threatening economic value.
  • Schema.org and its extension iot.schema.org aim to provide semantic interoperability for the Web of Things (WoT).
  • A key challenge for iot.schema.org is automatically identifying relevant topics from IoT application domains.

Purpose of the Study:

  • To design and implement KE4WoT (Knowledge Extraction for the Web of Things) for automatic topic identification in IoT literature.
  • To address the lack of studies on automatically identifying relevant topics for iot.schema.org.
  • To utilize domain-specific knowledge from IoT publications for vocabulary development.

Main Methods:

  • Developed KE4WoT to process 4500 full-text articles from smart home, smart city, and smart weather domains.
  • Extracted main topics (concepts and properties) from literature ontologies within these domains.
  • Evaluated extracted topics by comparing their descriptiveness for popular ontologies against expert evaluations.

Main Results:

  • KE4WoT successfully identified the most important topics from IoT application domain literature.
  • The identified topics were found to sufficiently describe existing IoT ontologies.
  • This study is the first to address the automatic identification of relevant topics for iot.schema.org.

Conclusions:

  • The identified main topics from IoT ontologies can effectively serve as keywords for describing them.
  • KE4WoT provides a method to utilize domain-specific knowledge for enhancing the iot.schema.org vocabulary.
  • This research contributes to improving semantic interoperability in the Web of Things.