Ontologies: a Tool for Knowledge Representation
An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. It ensures a common understanding of information and makes explicit domain assumptions thus allowing organizations to make better sense of their data.
What is Ontology?
An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships that hold between them. To enable such a description, we need to formally specify components such as individuals (instances of objects), classes, attributes and relations as well as restrictions, rules and axioms. As a result, ontologies do not only introduce a sharable and reusable knowledge representation but can also add new knowledge about the domain.
The ontology data model can be applied to a set of individual facts to create a knowledge graph – a collection of entities, where the types and the relationships between them are expressed by nodes and edges between these nodes, By describing the structure of the knowledge in a domain, the ontology sets the stage for the knowledge graph to capture the data in it.
There are, of course, other methods that use formal specifications for knowledge representation such as vocabularies, taxonomies, thesauri, topic maps and logical models. However, unlike taxonomies or relational database schemas, for example, ontologies express relationships and enable users to link multiple concepts to other concepts in a variety of ways.
As one of the building blocks of Semantic Technology, ontologies are part of the W3C standards stack for the Semantic Web. They provide users with the necessary structure to link one piece of information to other pieces of information on the Web of Linked Data. Because they are used to specify common modeling representations of data from distributed and heterogeneous systems and databases, ontologies enable database interoperability, cross-database search and smooth knowledge management.
Ontologies for Better Data Management
Some of the major characteristics of ontologies are that they ensure a common understanding of information and that they make explicit domain assumptions. As a result, the interconnectedness and interoperability of the model make it invaluable for addressing the challenges of accessing and querying data in large organizations. Also, by improving metadata and provenance, and thus allowing organizations to make better sense of their data, ontologies enhance data quality.
Ontology Languages
(From Wikipedia)
In computer science and artificial intelligence, ontology languages are formal languages used to construct ontologies. They allow the encoding of knowledge about specific domains and often include reasoning rules that support the processing of that knowledge. Ontology languages are usually declarative languages, are almost always generalizations of frame languages, and are commonly based on either first-order logic or on description logic.
Description Logics
Description logics (DL) are a family of formal knowledge representation languages. Many DLs are more expressive than propositional logic but less expressive than first-order logic. In contrast to the latter, the core reasoning problems for DLs are (usually) decidable, and efficient decision procedures have been designed and implemented for these problems. There are general, spatial, temporal, spatiotemporal, and fuzzy description logics, and each description logic features a different balance between expressive power and reasoning complexity by supporting different sets of mathematical constructors.
DLs are used in artificial intelligence to describe and reason about the relevant concepts of an application domain (known as terminological knowledge). It is of particular importance in providing a logical formalism for ontologies and the Semantic Web: the Web Ontology Language (OWL) and its profiles are based on DLs. A major area of application of DLs and OWL is in biomedical informatics, where they assist in the codification of biomedical knowledge. DLs and OWL are also applied in other domains, including defense, climate modeling, and large-scale industrial knowledge graphs.
A DL models concepts, roles and individuals, and their relationships.
The fundamental modeling concept of a DL is the axiom—a logical statement relating roles and/or concepts. This is a key difference from the frames paradigm where a frame specification declares and completely defines a class.
Comparison of Terminology
The description logic community uses different terminology than the first-order logic (FOL) community for operationally equivalent notions; some examples are given below. The Web Ontology Language (OWL) uses again a different terminology, also given in the table below.