Many would agree that sheer scale is part of what sets an ontology apart from a knowledge graph. Many experts would agree that the Knowledge Graph isn’t semantic in any meaningful way.īesides semantics, there’s a whole other, more fundamental battleground on which the debate is being waged: size. However, ’s use of inferential semantics is very limited. Today, the Knowledge Graph still uses, a collaborative effort between multiple tech giants to develop a schema for tagging content online. In its early days, the Knowledge Graph was partially based off of Freebase, a famous general-purpose knowledge base that Google acquired in 2010. With that said, Google has largely foregone semantics in building the Knowledge Graph - the piece of technology that popularized the term in the first place. At that point, it’s just a fancy database. If it’s just a bunch of labeled arrows, then that doesn’t comport with the concept of a knowledge graph as an artificial intelligence technique. A knowledge graph isn’t like any other database it is supposed to provide new insights, which can be used to infer new things about the world. It’s the difference between something that generates new knowledge and a database laying dormant, waiting to be queried. Semantics, they argue, is the basis for creating new inferences from the data which would otherwise go unseen. The knowledge representation experts who specialize in semantics-driven ontologies will make no bones about it: a knowledge graph is necessarily built on semantics. What are the components of knowledge graphs? In truth, no one is really sure - or at least there isn’t a consensus. But that new widespread attention from the research community has helped foment a significant debate among knowledge representation experts: what even is a knowledge graph? Knowledge graphs have been embraced by numerous tech giants, most notably Google, which is responsible for popularizing the term. But in the past decade, two words have pushed ontologies and semantic data back into the spotlight: knowledge graphs. However, interest in ontologies waned by the 2000s as machine learning became the hot new technology for search engines and advertising. Ontologies have been present in artificial intelligence research for at least forty years, coming into their own in the ’80s on the back of a research wave that catapulted them into popularity by the mid-‘90s. Where Ontologies End and Knowledge Graphs Begin
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