Formal Knowledge Representation

Formal knowledge representation is a field of artificial intelligence (AI), which captures the semantics (meaning) of concepts, properties, relationships, and entities of specific knowledge domains, i.e., fields of interest or areas of concern, as structured data. Machine-interpretable statements are typically written in RDF, taxonomical structures (concept hierarchies) in RDFS controlled vocabularies, and complex relationships between concepts and entities in OWL ontologies. Using such definitions to codify expert knowledge, implicit knowledge can be inferred from explicit statements using purpose-written entailment rules via automated reasoning. OWL axioms can be formally grounded using description logics, which allow understanding computational properties, such as reasoning complexity. Ultimately, these representations are used in semantic formalisms, enabling computer systems to solve complex tasks automatically (and without errors).

Table 1 shows an example of codifying cyber-knowledge for communication networks, defining a Boolean property to describe whether a particular router is an Autonomous System Boundary Router (ASBR).

Table 1. Representing Expert Knowledge with DL Axioms and Corresponding RDF Triples (in Turtle Serialization)
DL Axioms RDF Triples (Turtle)
∃isASBR.T ⊑ Router
⊤ ⊑ ∀isASBR.boolean
    :isASBR a owl:FunctionalProperty ;
rdfs:domain :Router ;
rdfs:range xsd:boolean .