I totally can imagine what a huge potential semantic technologies such as knowledge graphs, ontologies, SPARQL search and automated reasoning might have in different fields of science and engineering. However, apart from biology and medicine (which I have only very little insight on) they seem to be rarely used (e.g. compared to machine learning methods).
Of course it is likely that I am simply unaware of very useful applications of semantic technologies, therefor this question.
More precisely, I am interested in short descriptions which concrete knowldegebase/tool is used and which problem it helps to solve or which question it helps to answer.
Hi Carsten,
not sure about most "convincing" and what it means. In the sense of practicality or impact? It is also a vast domain, including manufacturing, digital twins, biology, medicine (there are many biomedical ontologies for example from bigger taxonomic structures to simple domain ontologies, obo foundry), research knowledge graphs etc.
In my project we build a knowledge base of systems and electronic engineering. Including means of ontology, reasoning, a distributed database, expert system, and constraint propagation.
One of the applications is to make estimations and predictions of future systems, and interactively explore different configurations, structures of systems, solution spaces and properties while simultaneously considering the system context. An example is looking at the effects the tweaking of neural net parameters might have.
This is just one example from what we do.
The state is often ontologies are developed by few specialized experts and often not reused by others, leaving them hanging, or siloed and just used for a personalized project. Then others do not want to share of their own knowledge or do not connect to the other graph. At least this seems to be two aspects of what I can see it.
Knowledge Graph Construction looks promising. I am currently looking into the boundaries and how it might scale. Also considering the quality of the constructed graph regarding the commitment to an ontology or proper semantic structures in general.
But I agree, overall outwardly it looks shy in comparison to the ML hype. There seem to be a couple of reasons which are multi-faceted. It was also discussed on one of the last ISWC conferences.
FYI I recently came across one workshop covering this theme/topic:
https://aaai.org/Conferences/AAAI-23/ws23workshops/#ws06
https://ai-2-ase.github.io/
Greetings, and have a nice day!
-Frank
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