Through precise definitions of terms, ontologies enable shared understanding in conversations among agents to collect, process, fuse, and exchange information. For example, ontologies can be used to improve search accuracy through query expansion to clarify the search context. Typically, search accuracy includes both precision and recall, meaning that correct query results are returned and relevant answers are not missing. Ontologies designed for information sharing may be used in a number of ways, including but not limited to:
• on their own as terminologies or common vocabularies to assist in communications within and across groups of people;
• to codify, extend, and improve flexibility in XML2 and/or RDF Schema-based3 agreements;
• for information organization, for example for websites that are designed to support search engine optimization (SEO) and/ or those that use mark-up per schema.org;4 or
• to describe resources in a content management system, for example for archival, corporate website management, or for scientific experimentation and reuse.
Ontologies that describe information resources, processes, or applications are frequently designed to support question answering, either through traditional query languages such as SQL5 or SPARQL,6 or through business rules, including rule languages such as RuleML,7 Jess,8 Flora-2,9 and commercial production rule languages. They may also be designed to support more complex applications, including:
• recommender systems, for example, for garden planning, product selection, service provider selection, etc. as part of an event planning system;
• configuration systems such as product configurators or systems engineering design verification and validation;
• policy analysis and enforcement, such as for investment banking compliance and risk management;
• situational analysis systems, such as to understand anomalous behaviors for track and trace, fraud detection, or other business intelligence applications; and
• other complex analyses, such as those required for understanding drug formularies, disease characteristics, human genetics, and individual patient profiles to determine the best therapies for addressing certain diseases.
In other words, ontologies and the technologies that leverage them are well suited to solve problems that are cross-organizational, cross-domain, multi-disciplinary, or that span multiple systems. They are particularly useful in cases where traditional information technologies are insufficiently precise, where flexibility is needed, where there is uncertainty in the information, or where there are rich relationships across processes, systems, and or services that can’t be addressed in other ways. Ontologies can connect silos of data, people, places, and things.
In the sections that follow, we will provide examples and modeling patterns that are commonly used to support both lightweight use cases that do not involve much reasoning, as well as richer applications such as recommender systems or systems for policy analysis and enforcement that depend on more representation and reasoning power.
1.4 KNOWLEDGE REPRESENTATION LANGUAGES
Today’s approaches to knowledge representation (KR) emerged from 1970s and 1980s research in artificial intelligence, including work in areas of semantic networks, question-answering, neural networks, formal linguistics and natural language processing, theorem proving, and expert systems.
The term knowledge representation is often used to talk about representation of information for consumption by machines, although “good” knowledge representations should also be readable by people. Every KR language has a number of features, most of which are common to software engineering, query, and other languages. They include: (1) a vocabulary, consisting of some set of logical symbols and reserved terms plus variables and constants; (2) a syntax that provides rules for combining the symbols into well-formed expressions; (3) a formal semantics, including a theory of reference that determines how the constants and variables are associated with things in the universe of discourse and a theory of truth that distinguishes true statements from false ones; and (4) rules of inference, that determine how one pattern can be inferred from another. If the logic is sound, the rules of inference must preserve truth as determined by the semantics. It is this fourth element, the rules of inference and the ability to infer new information from what we already know, that distinguishes KR languages from others.
Many logic languages and their dialects have been used for KR purposes. They vary from classical first order logic (FOL) in terms of: (1) their syntax; (2) the subsets of FOL they implement (for example, propositional logic without quantifiers, Horn-clause, which excludes disjunctions in conclusions such as Prolog, and terminological or definitional logics, containing additional restrictions); (3) their proof theory, such as monotonic or non-monotonic logic (the latter allows defaults), modal logic, temporal logic, and so forth; and (4) their model theory, which as we mentioned above, determines how expressions in the language are evaluated with respect to some model of the world.
Classical FOL is two-valued (Boolean); a three-valued logic introduces unknowns; four-valued logic introduces inconsistency. Fuzzy logic uses the same notation as FOL but with an infinite range of certainty factors (0.0–1.0). Also, there are differences in terms of the built-in vocabularies of KR languages: basic ISO/IEC 24707:2018 (2018) is a tight, first-order language with little built in terminology, whereas the Web Ontology Language (Bao et al., 2012) includes support for some aspects of set theory.10
1.4.1 DESCRIPTION LOGIC LANGUAGES
Description logics (DLs) are a family of logic-based formalisms that represent a subset of first order logic. They were designed to provide a “sweet spot” in that they have a reasonable degree of expressiveness on the ontology spectrum, while not having so much expressive power that it is difficult to build efficient reasoning engines for them. They enable specification of ontologies in terms of concepts (classes), roles (relationships), and individuals (instances).
Description logics are distinguished by (1) the fact that they have a formal semantics, representing decidable fragments of first order logic, and (2) their provisions for inference services, which include sound and complete decision procedures for key problems. By decidable, we mean that there are effective algorithms for determining the truth value of the expressions stated in the logic. Description logics are highly optimized to support specific kinds of reasoning for implementation in operational systems.11
Example types of applications of description logics include:
• configuration systems—product configurators, consistency checking, constraint propagation, etc., whose first significant industrial application was called PROSE (Mc-Guinness and Wright, 1998) and used the CLASSIC knowledge representation system, a description logic, developed by AT&T Bell Laboratories in the late 1980s (Borgida et al., 1989);
• question answering and recommendation systems, for suggesting sets of responses or options depending on the nature of the queries; and
• model engineering applications, including those that involve analysis of the ontologies or other kinds of models (systems engineering models, business process models, and so forth) to determine whether or not they meet certain methodological or other design criteria.
1.5 KNOWLEDGE BASES, DATABASES, AND ONTOLOGY
An ontology is a conceptual model of some aspect of a particular universe of discourse (or of a domain of discourse). Typically, ontologies contain only “rarified” or “special” individuals, representing elemental concepts critical to the domain. In other words, they are comprised primarily of concepts, relationships, and axiomatic expressions.
One of the questions that we are often asked is, “What is the difference between an ontology and a knowledge base?” Sometimes people refer to the knowledge