well-known researchers in the field attempted to arrive at a consensus on a definition of ontology. This spectrum is described in detail in McGuinness, Ontologies Come of Age (2003). We believe that an ontology can add value when defined at any level along the spectrum, which is usually determined by business or application requirements. Most of the ontologies we have developed, whether conceptual or application oriented, include at least a formal “is-a” or subclass hierarchy, and often additional expressions, such as restrictions on the number or type of values for a property, (i.e., they fall to the right of the red “squiggle” in the diagram).
Regardless of the level of expressivity and whether the ontology is conceptual in nature or application focused, we expect that an ontology will be: (1) encoded formally in a declarative knowledge representation language; (2) syntactically well-formed for the language, as verified by an appropriate syntax checker or parser; (3) logically consistent, as verified by a language-appropriate reasoner or theorem prover; and (4) will meet business or application requirements as demonstrated through extensive testing. The process of evaluating and testing an ontology is both science and art, with increasingly sophisticated methods available in commercial tools, but because no “one size fits all,” we typically need multiple tools to fully vet most ontologies. We will discuss some of the more practical and more readily available approaches to ontology evaluation in later chapters of this book.
1.2 LOGIC AND ONTOLOGICAL COMMITMENT
The primary reason for developing an ontology is to make the meaning of a set of concepts, terms, and relationships explicit, so that both humans and machines can understand what those concepts mean. The level of precision, breadth, depth, and expressivity encoded in a given ontology depends on the application: search applications over linked data tend to require broader ontologies and tolerate less precision than those that support data interoperability; some machine learning and natural language processing applications require more depth than others. Ontologies that are intended to be used as business vocabularies or to support data governance and interoperability require more metadata, including clearly stated definitions, provenance, and pedigree, as well as explanatory notes and other usage information than machine learning applications may need. The foundation for the machine-interpretable aspects of knowledge representation lies in a combination of set theory and formal logic. The basis for the metadata stems from library science and terminology work, which we discuss in Chapter 4.
Most people who are interested in knowledge representation took a course in logic at some point, either from a philosophical, mathematical, or linguistics perspective. Many of us also have basic knowledge of set theory, and can draw Venn diagrams showing set intersection when needed, but a little refresher may be helpful.
Logic can be more difficult to read than English, but is clearly more precise:
(forall ((x FloweringPlant))
(exists ((y Bloom)(z BloomColor))(and (hasPart x y)(hasCharacteristic y z))) )
Translation: Every flowering plant has a bloom which is a part of it, and which has a characteristic bloom color.
Language: Common Logic, CLIF syntax (ISO/IEC 24707:2018, 2018)
Logic is a simple language with few basic symbols. The level of detail depends on the choice of predicates made by the ontologist (e.g., FloweringPlant, hasPart, hasCharacteristic, in the logic, above); these predicates represent an ontology of the relevant concepts in the domain.
1.3 ONTOLOGY-BASED CAPABILITIES
An ontology defines the vocabulary that may be used to specify queries and assertions for use by independently developed resources, processes, and applications. “Ontological commitments are agreements to use a shared vocabulary in a coherent and consistent manner.”1 Agreements can be specified as formal ontologies, or ontologies with additional rules, to enforce the policies stated in those agreements. The meaning of the concepts included in the agreements can be defined precisely and unambiguously, sufficient to support machine interpretation of the assertions. By composing or mapping the terms contained in the ontologies, independently developed systems can work together to share information and processes consistently and accurately.
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
