Automatic Knowledge Extraction to build Semantic Web of Things Applications

TitleAutomatic Knowledge Extraction to build Semantic Web of Things Applications
Publication TypeJournal Article
Year of Publication2019
AuthorsMahda Noura, Amelie Gyrard, Sebastian Heil, Martin Gaedke
JournalIEEE Internet of Things (IoT) Journal
Date Published05/2019
KeywordsInternet of Things (IoT), knowledge extraction, Machine Learning (ML), Natural Language Processing (NLP), Ontologies, Semantic Web of Things (SWoT), Web of Things (WoT)
Abstract

The Internet of Things (IoT) primary objective is to make a hyper-connected world for various application domains.
However, IoT suffers from a lack of interoperability leading to a substantial threat to the predicted economic value. Schema.org provides semantic interoperability to structure heterogeneous data on the Web. An extension of this vocabulary for the IoT domain (iot.schema.org) is an ongoing research effort to address semantic interoperability for the Web of Things (WoT). To design this vocabulary, a central challenge is to identify the main topics (concepts and properties) automatically from existing knowledge in IoT applications. We designed KE4WoT (Knowledge Extraction for the Web of Things) to automatically identify the most important topics from literature ontologies of 3 different IoT application domains -- smart home, smart city and smart weather -- based on our corpus consisting of 4500 full-text conference and journal articles to utilize domain-specific knowledge encoded within IoT publications. Despite the importance of automatically identifying the relevant topics for iot.schema.org, up to know there is no study dealing with this issue. To evaluate the extracted topics, we compare the descriptiveness of these topics for the 10 most popular ontologies in the 3 domains with empirical evaluations of 23 domain experts. The results illustrate that the identified main topics of IoT ontologies can be used to sufficiently describe existing ontologies as keywords.

Projects: 
IOT
kHealth
Semantic Web