# Essential Features in a Theory of Context for Enabling Artificial General Intelligence
## 3 Research in Context-Rich Artificial Intelligence
### Knowledge Graphs and Semantic Web  
Graphs have been ubiquitous in AI since its founding, but have tended to be more associated with the planning community [62,63]. With the publishing of Google's influential post on \"things, not strings\" in 2011 [64], knowledge graphs (KGs) have come to be recognized as a flexible and data-driven way of modeling a domain using entities, relations, and events, rather than as collections of primitives (e.g., strings and numbers) that are lacking in semantics. Figure 1 above illustrates a simple domain-specific knowledge graph over the Books domain. Companies such as Google and Microsoft have taken this concept much further [65], and the Google Knowledge Graph is known to be a web-scale graph constructed in a data-driven fashion from numerous sources and over broad domains. The Google Knowledge Graph underlies at least some of the search artifacts (such as a knowledge panel) that are produced in response to queries such as \"[PERSON] da Vinci\". Knowledge graphs have led to enormous advanced in both generic and domain-specific (e.g., e-commerce) search [66], and are being constructed in some form in almost all the big technology companies. COVID-19 knowledge graphs were even constructed in the earliestdays of the pandemic [67]. Typically, KGs are constructed over text and Web data, though more recently, multi-modal and domain-specific KG construction has started to become popular [68; 69].  
KG research has a closer connection to context-rich AI than may initially meet the eye. First, KGs are (typically) populated according to a domain model, called an ontology, that contains the concepts, constraints and relations that serve to define both the scope and vocabulary of a domain. It would not be incorrect to say that a KG exists in the context of a domain and a task (or set of tasks), with the former defined using frameworks such as the Web Ontology Language (OWL) [70]. For example, the ontology underlying the KG in the figure may contain concepts such as \"book\", \"person\" and \"publisher\" (among others), and relations such as \"author_of\" and \"publisher_of\". Concepts and relations may have sub-concepts and sub-relations; for example, an \"author\" concept that is a sub-class of 'person' could be declared in the ontology. These relations connect the entities, which are formally instances of concepts, in the KG. Constraints, which are not visible on the surface, prevent unwanted declarations in the KG, e.g., one reasonable constraint might be that the range of the \"author_of\" relation must be an instance of a \"person\" concept.  
The Semantic Web community has conducted much research over the years on rigorous ways to formally express ontologies and knowledge graphs [71], and has even developed reasoning engines to infer more facts than are explicitly declared in the KG. More recently, KG research has intersected with both representation learning and commonsense reasoning [72]. Within the Semantic Web community, context is therefore often associated with correctly inferring knowledge that is implicit and possibly, only approximately correct. Recent advances in neuro-symbolic AI aim to combine the benefits of symbolic and logic-based AI, and machine learning methods such as deep neural networks [73].