ArticleIEEE internet of things journal2019
Automatic Knowledge Extraction to build Semantic Web of Things Applications.
Article in IEEE internet of things journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed.
- Design and evaluation of an intent-based web of things query framework for smart device discovery.Scientific reports · 2026Article
- Towards Semantic Smart Cities: A Study on the Conceptualization and Implementation of Semantic Context Inference Systems.Sensors (Basel, Switzerland) · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.