Living document - "Semantic Web: Past, Present, and Future"

Dear Semantic Web community

As the year ends, the question remains: How will the Semantic Web look in 2025?

Together with Katja Hose, Maria-Esther Vidal, Gerd Groener, and Petr Škoda, we contributed this year an article titled "Semantic Web: Past, Present, and Future", see

https://drops.dagstuhl.de/entities/document/10.4230/TGDK.2.1.3 to the new Diamond Open Access journal on Transactions on Graph Data and Knowledge (TGDK, https://tgdk.org/). This primer has been a living document for 13 years; see the link for details!

As many of us cannot resist checking emails over the break, I ask you to consider the question:
What is our future in 2025?

Many "classical" Semantic Web researchers have witnessed the rise of Linked Open Data, Freebase being bought by Google (largely seen as a big win), and the beginning and success of the Knowledge Graph era. Many of us have moved on to or added topics like graph representation learning, graph neural networks, language models, etc., to our research portfolio. So, how much of the classical topics are left? How much will come back? Should we work more on knowledge graph embeddings obeying OWL axioms? Shall we have federated queries not only over multiple data sources but also include hybrid queries using similarities in graph embeddings?

In the current 2024 version of the primer, we include the latest W3C standards developed by the Semantic Web community. But we also explain the journey from the famous Linked Data principles via Knowledge Graphs to the hot topic of machine learning on graphs!

*The article linked above is an invitation to contribute. Contact me if you are interested!* 

It will be updated from time to time. We like to receive your feedback, and perhaps you would like to contribute to a future version.

Best wishes and happy holidays,

Ansgar

PS: For 2025, there is a plan to update the German version of the article in an introductory textbook on artificial intelligence. It will add Shallow Graph Embeddings, Graph Neural Networks, and the interplay of Knowledge Graphs and Language Models. Once ready, I plan to ping this back to the English version.

My source:

Ansgar Scherp
From:mail AT ansgarscherp.net
To:semantic-web AT w3.org

4.3   3-12

'Domain Ontologies represent knowledge specific to a particular domain [48, 109]. Domain ontologies are used as external sources of background knowledge [48]. They can be built on foundational ontologies [110] or core ontologies [131], which provide precise structuring to the domain ontology and thus improve interoperability between different domain ontologies. Domain ontologies can be simple such as the FOAF ontology or the event ontology mentioned above, or very complex and extensive, having been developed by domain experts, such as the SNOMED medical ontology.' ...

3-13

'Foundational Ontologies have a very wide scope and can be reused in a wide variety of modeling scenarios [24]. They are therefore used for reference purposes [109] and aim to model the most general and generic concepts and relations that can be used to describe almost any aspect of our world [24, 109], such as objects and events. An example is the Descriptive Ontology for Linguistic and Cognitive Engineering (DOLCE) [24]. Such basic ontologies have a rich axiomatization that is important at the developmental stage of ontologies. They help ontology engineers to have a formal and internally consistent conceptualization of the world, which can be modeled and checked for consistency. For the use of foundational ontologies in a concrete application, i.e., during the runtime of an application, the rich axiomatization can often be removed and replaced by a more lightweight version of the foundational ontology.
 In contrast, domain ontologies are built specifically to allow automatic reasoning at runtime. Therefore, when designing and developing ontologies, completeness and complexity on the one hand must always be balanced with the efficiency of reasoning mechanisms on the other. In order to represent structured knowledge, such as the scenario depicted in Figure 1, interconnected ontologies are needed, which are spanned in a network over the Internet. For this purpose, the ontologies used must match and be aligned with each other.'

Health - See also 4.2 (A post in 2025 ...re. SKOS?).