API4KP Knowledge Representation and Reasoning Terminology Ontology
This ontology covers the primary knowledge representation and reasoning (KRR) terminology used for describing the concepts used by the API4KP specification.
Elisa Kendall (ekendall at thematix.com)
Davide Sottara (sottara.davide at mayo.edu)
The https://www.omg.org/spec/API4KP/20210201/api4kp-krr.rdf version of this ontology was modified to leverage the Commons Ontology Library (API4KP-22), including replacement of references to Specification Metadata and references to LCC with the Commons Annotation Vocabulary and other Commons ontologies, and to reduce the dependency on DOL as appropriate.
Copyright (c) 2014-2023 Fraunhofer Gesellschaft
Copyright (c) 2014-2023 Freie Universität Berlin
Copyright (c) 2014-2023 Mayo Foundation for Medical Education and Research (MFMER)
Copyright (c) 2014-2023 Raytheon Technologies
Copyright (c) 2014-2023 Thematix Partners LLC
Copyright (c) 2015-2023 Object Management Group, Inc.
Copyright (c) 2019-2023 Universität Leipzig
focused environment having exactly one member
basic machine-readable knowledge resource
unstructured native machine-readable knowledge resource
KE1: the instance of the OWL 2 DL abstract syntax for the latest version of a biomedical ontology from an ontology series KA1 defining observable entities, such as the 2015 international version of the SNOMED-CT knowledge base\footnote{http://browser.ihtsdotools.org/} (see also the definition of Basic Knowledge Asset). This ontology differs from other versions of the series only in the natural language definitions.
well-formed, stand-alone formula in the abstract syntax of a machine-readable language
The use of "basic" in API4KP differs from its usage in DOL - a DOL basic OMS (ontologies, models and specifications) is a set, and corresponds to a Set-structured piece of knowledge in API4KP.
environment with an associative composition operation for mappings, that is closed under composition and contains an identity mapping for every member
an environment having a composition operation where the composition of its mappings is a mapping of the environment whenever the composition is well-defined
The composition M2 o M1 of two mappings M1 and M2 is well-defined when M1 maps to the same thing that M2 maps from. (The codomain of M1 is the domain of M2).
collection of machine readable knowledge resources, which are not necessarily in the same language and may themselves have structure.
KE2: a heterogeneous collection of streaming data and RDF graphs, together with static OWL ontologies and CL texts, and ECA rules describing actions of a CDS
KE3: the OWL 2 DL ontology series KA1, viewed as a collection of expressions rather than an equivalence class
abstraction that relates the size of a problem to the amount of resources (space and time) needed to solve the problem using a given algorithm
mathematical structure of mappings and members, where the domain and codomains of the mappings are members of the environment
a KRR language environment containing semantics-preserving translations from RDF and OWL into CL, assisting in the integrated interpretation of a stream of RDF graphs and OWL ontologies
composable environment having an identity mapping for each member
an identity mapping is a mapping whose domain and codomain are the same, and when composed with another mapping is equal to the original mapping
non-empty environment which has a member F (called the focus or focus member) such that for every other member A, there is a mapping in the environment from A to F
heterogeneous computable knowledge resource
heterogeneous knowledge resource that uses a machine readable language as primary language
knowledge resource expressed by means of 2 or more languages
knowledge base composed by multiple parts that have different computational semantics and pragmatics, and thus are likely based on different logics
categorical, preserving language environment where multiple KRR languages, and mappings thereof, are supported
An KB can be immutable by nature. Otherwise, any snapshot of a KB at a given point in time (including the time of its creation) is considered immutable.
work of knowledge designed to support reasoning activities
A knowledge base is formed by one or more pieces of knowledge, related in different way, such that each piece plays some kind of role.
set of expressions in one or more machine readable languages, in the context of a logical environment that determines how these expressions are jointly interpreted.
Example A: OWL Ground fact expressing that Socrates is a philosopher.
Example B: Common Logic sentence expressing that Socrates is a philosopher.
Example C: RDF graph expressing that the author of Moby Dick is Herman Melville.
Example D: SPARQL query for the names of philosphers.
Example E: A and B, together with a mapping that allows OWL to be converted to Common Logic.
knowledge base that evolves over time as structuring operations are applied to it, 'in place'
computable knowledge resource that uses exactly 1 language that is a machine readable language
knowledge resource that uses exactly one language
environment with at least one member and one mapping
focused, categorical, preserving language environment where the focus is a KRR language
The special case where all languages in an knowledge environment are KRR languages supporting model-theoretic semantics without side-effects (logics), and the preserving property is characterized by a logical graph reduces to a heterogeneous logical environment as defined in DOL.
environment where every mapping preserves a specified property
has a closed subset of members and mappings that make up an environment
relationship between a collection and an environment whereby the results of application of a mapping of the environment to a member of the collection are also members of the environment
is sub-environment of
contains a subset of members and mappings with respect to the target environment that is a partial order, where the language and logic graphs of the source enviroment are subgraphs, respectively, of the language and logic graphs of the target environment
when applied, ensures that a property of the source entity is also a property of the target entity
Example: my copy in my Download folder of the encoding in UTF-8 of the serialization in Manchester syntax of an OWL ontology.