For builders and explorers who are already familiar with Semantic Web standards, leveraging them in Knowledge Graph projects can simplify development and expand capabilities, but this isn’t mandatory (or even viable) for all applications.
The Semantic Web (SW) emerged as a vision for a smarter, interconnected World Wide Web. Its primary goals are to integrate and share knowledge by embedding meaning within information to enable automated discovery and exchange. Semantic Web standards like RDF, OWL, and SPARQL provide a framework for linked data, allowing machines to understand and process information across domains.
Knowledge Graphs (KGs) evolved as a practical approach to structuring data that supports knowledge-based queries and inferencing. KGs integrate diverse information sources, often within a specific domain and frequently for enterprise use, with goals that overlap with SW use cases such as data discovery and integration.
However, KG and SW initiatives are not always aligned in their approach. Not all resources on the SW are KGs, and not all KGs use SW standards, meaning many KGs are not natively compatible with the SW’s linked data framework.
Building a KG does not require SW standards, so KG developers can make practical choices on whether or not to adopt them. Those using SW standards benefit from established interoperability and robust capabilities. Standards like RDF, OWL, and SPARQL are widely adopted, whereas alternatives remain less common.
While the goals of KGs and the SW often align, their technical approaches may differ, particularly in the use of SW ontologies and graph languages. SW-based KGs typically rely on ontologies (formal knowledge structures) to define entities and relationships. However, many KGs prioritize application-specific needs over universal interoperability, favoring flexible, task-oriented schemas rather than comprehensive ontologies.
Looking to the future, a hybrid approach may offer the best of both worlds. KGs could efficiently manage data within specific contexts, while SW-based ontologies could ensure interoperability across broader domains. This convergence would combine the adaptability of KGs with the SW’s structured data vision, fostering a more integrated framework for knowledge and reasoning across the Web.