Ontological Knowledge Base

An ontological knowledge base is a machine-readable with a formal semantic model (a formally-defined KB).

• AKA: Formally Structured KB.
• Context:
  • It can (typically) be a Costly Artifact (largely because it involves the knowledge acquisition bottleneck).
  • It can (typically) not guarantee completeness, e.g. of queries and assertions.
  • It can (typically) be a Knowledge Sharing Artifact (e.g. knowledge base, controlled vocabulary, canonical dataset, etc.).
  • It can be classified as and composed of the following sub-categories:
    • an Upper-Level Ontology - consisting of general knowledge to higher-level concepts that are not domain-specific,
    • a Middle-Level Ontology - consisting only of general knowledge concepts;
    • a Core Ontology - consisting only of the minimal/basic concepts required to understand the other specific-domain concepts;
    • a Domain Ontology - consisting domain-specific concepts;
    • a Hybrid Ontology - combines upper and domain Ontology;
    • a Task Ontology - containing task-specific concepts;
    • a Application Ontology - containing application-specific concepts;
  • It can be created, managed and validated by Ontology Design System.
  • It can be located using an Ontology Search Service.
  • It can be used by Intelligent Systems.
  • It can be a Human-Readable Ontology (e.g. a semantic wiki).
  • It can use a Corpus as database and can be used as database by a Knowledge Base.
  • It can range from Conceptual Ontology to being a Linguistic Ontology.
  • It can range from being a Lightweight Ontology (is-a) to being a Mediumweight Ontology (with propositional knowledge) to being a Formal Ontology.
  • It can range from being an Axiomized Ontology, to being a Terminology Ontology, to being a Prototype-based Ontology.
  • It can range from being an Inconsistent Ontology to a Consistent Ontology.
  • It can range from being a Public Ontology to being a Proprietary Ontology.
  • It can be used to facilitate Knowledge-Intensive Tasks.
  • ...
• Example(s):
  • Upper-Level Ontologies, such as: Suggested Upper Merged Ontology (SUMO) and Cyc.
  • Core Ontologies, such as: CIDOC-CRM (for cultural heritage documentation).
  • Domain Ontologies, such as: Gene Ontology (for genomics) and Dublin Core (for document metadata).
  • Hybrid Ontologies, such as: ConceptNet and Gellish Ontology.
  • Task Ontologies, such as: Plan Ontology (for planning in AI systems).
  • Middle-Level Ontologies, such as: WordNet and BabelNet.
  • ...
• Counter-Example(s):
  • a Knowledge Graph,
  • a Corpus,
  • a Knowledge Base,
  • a Semantic Web System,
  • an Informal Semantic Model, such as a logical data model (even though it has the entities and relationships of a domain, and can be normalized).
  • an Entity Database, such as a lexical database (of lexical entities).
  • an EDI standardizations, e.g. HR-XML, http://www.hr-xml.org/
• See: Natural Language Processing System, Semantic Wiki Data Model, Semantic Domain, Semantic Markup Language, Metadata, Ontology Mining, Ontology Discipline.

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References

2019

• (Wikipedia, 2019) ⇒ https://en.wikipedia.org/wiki/Ontology_(information_science) Retrieved:2019-6-30.
  • In computer science and information science, an ontology encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities that substantiate one, many or all domains of discourse.

    Every field creates ontologies to limit complexity and organize information into data and knowledge. As new ontologies are made, their use hopefully improves problem solving within that domain. Translating research papers within every field is a problem made easier when experts from different countries maintain a controlled vocabulary of jargon between each of their languages.

    Since Google started an initiative called Knowledge Graph, a substantial amount of research has used the phrase knowledge graph as a generalized term. Although there is no clear definition for the term knowledge graph, it is sometimes used as synonym for ontology. One common interpretation is that a knowledge graph represents a collection of interlinked descriptions of entities – real-world objects, events, situations or abstract concepts. Unlike ontologies, knowledge graphs, such as Google's Knowledge Graph, often contain large volumes of factual information with less formal semantics. In some contexts, the term knowledge graph is used to refer to any knowledge base that is represented as a graph.

2015

• (Navigli, 2015) ⇒ Roberto Navigli. (2015). “Ontologies.” In: Reference Book Journal.
  • QUOTE: This chapter is about ontologies, that is, knowledge models of a domain of interest. We introduce ontologies, view them from the perspective of several fields of knowledge, and present existing ontologies and the different tasks of ontology building, learning, matching, mapping and merging(...)

     Ontologies are composed of the following sections:

    • An upper ontology (or top ontology), that encodes high-level concepts and relations, which do not belong to a specific domain of interest. Upper ontologies aim to enable semantic interoperability between different ontologies by providing the most general concepts structured in a hierarchy and optionally associating general rules and axioms about those concepts. Existing upper ontologies – introduced in Section 20.4.1 – include SUMO, the WordNet top ontology, and the Cyc upper ontology.
    • A middle or general-purpose ontology that encodes general concepts (units of measurement, spatial and temporal relations, communication, mental and physical objects, etc.) which allow connections to be made between more specific concepts usually encoded in a domain ontology. Existing middle ontologies – introduced in Section 20.4.2 – include WordNet and Cyc.
    • A domain ontology that instead models concepts, individuals and relations about the knowledge domain of interest. Different domain ontologies can either use the same upper/middle ontology or provide a mapping to a common upper/middle ontology, thus enabling interoperability between them. Existing domain ontologies – introduced in Section 20.4.3 – include UMLS and the Gene Ontology.
    • An application ontology – an ontology developed for a specific use or application focus. Its scope is typically defined on the basis of use cases that can be used to test the ontology. Application ontologies depend both on domains and on a specific task of interest, and are typically used when crossing domains (e.g. the geospatial field).

2010

• (Google Code, 2010) ⇒ http://code.google.com/p/semanticscience/wiki/ODP
  • The following set of basic principles must be followed by any Semantic Science ontology.
    1. The ontology must be represented and correctly adhere to the strict semantics of the Web Ontology Language (OWL).
    2. All ontologies must be licensed with Creative Commons - Attribution. This ensures that people are free to share and create derivative works with the sole condition that derivative works must acknowledge sources.
    3. Resources, whether ontologies or entities described within them, must be uniquely and persistently identified by International Resource Identifiers (IRI). These should be dereferenceable. OWL documents should be versioned. The IRI syntax is suggested to follow that of the Banff Manifesto.
    4. Resources should be described with brief English labels (rdfs:label) and human readable definitions (dc:description) that are as accurate as possible, while not adding superfluous information or imposing unnecessary constraints. For consistency, labels should be lower case (unless a formal name) and words separated by whitespace. More elaborate syntax rules can be found here.
    5. Resources should be (to the extent possible) described through axioms that match their human readable descriptions.
    6. The scope of the ontology should be clearly described and must be motivated by itemized requirements (e.g. use cases), for each it will be expected to satisfy. These requirements may be documented on a public document such as a web page, a white paper, or a published work.

2009a

• (Wikipedia, 2009) ⇒ http://en.wikipedia.org/wiki/Ontology
  • In philosophy, ontology (from the Greek, genitive : of being (part. of : to be) and -λογία: science, study, theory) is the most fundamental branch rof metaphysics. Ontology is the study of being or existence and its basic categories and relationships. ...

2009b

• (WordNet, 2009) ⇒ http://wordnetweb.princeton.edu/perl/webwn?s=ontology
  • S: (n) ontology ((computer science) a rigorous and exhaustive organization of some knowledge domain that is usually hierarchical and contains all the relevant entities and their relations)
  • S: (n) ontology (the metaphysical study of the nature of being and existence)

2009c

• (W3, 2009) ⇒ http://www.w3.org/TR/owl2-overview/ OWL 2 Web Ontology Language, Document Overview
  • … Ontologies are formalized vocabularies of terms, often covering a specific domain and shared by a community of users. They specify the definitions of terms by describing their relationships with other terms in the ontology.

2009d

• (Sowa, 2009) ⇒ John F. Sowa (2009).http://www.jfsowa.com/ontology/
  • The subject of ontology is the study of the categories of things that exist or may exist in some domain. The product of such a study, called an ontology, is a catalog of the types of things that are assumed to exist in a domain of interest D from the perspective of a person who uses a language L for the purpose of talking about D. The types in the ontology represent the predicates, word senses, or concept and relation types of the language L when used to discuss topics in the domain D. An uninterpreted logic, such as predicate calculus, conceptual graphs, or KIF, is ontologically neutral. It imposes no constraints on the subject matter or the way the subject may be characterized. By itself, logic says nothing about anything, but the combination of logic with an ontology provides a language that can express relationships about the entities in the domain of interest.

    An informal ontology may be specified by a catalog of types that are either undefined or defined only by statements in a natural language. A formal ontology is specified by a collection of names for concept and relation types organized in a partial ordering by the type-subtype relation. Formal ontologies are further distinguished by the way the subtypes are distinguished from their supertypes: an axiomatized ontology distinguishes subtypes by axioms and definitions stated in a formal language, such as logic or some computer-oriented notation that can be translated to logic; a prototype-based ontology distinguishes subtypes by a comparison with a typical member or prototype for each subtype. Large ontologies often use a mixture of definitional methods: formal axioms and definitions are used for the terms in mathematics, physics, and engineering; and prototypes are used for plants, animals, and common household items.

2009e

• (Vossen, 2009) ⇒ Piek Vossen. (2009) "Building Wordnets." PowerPoint Presentation: https://slideplayer.com/slide/736731/
  • QUOTE: "Slide 6: Linguistic versus conceptual ontologies"
    • Conceptual ontology:
    • A particular level or structuring may be required to achieve a better control or performance, or a more compact and coherent structure.
      • Introduce artificial levels for concepts which are not lexicalized in a language (e.g. instrumentality, hand tool),
      • Neglect levels which are lexicalized but not relevant for the purpose of the ontology (e.g. tableware, silverware, merchandise).
      • What properties can we infer for spoons?
        • spoon -> container; artifact; hand tool; object; made rof metal or plastic; for eating, pouring or cooking
    • Linguistic ontology:
      • Exactly reflects the relations between all the lexicalized words and expressions in a language.
      • Valuable information about the lexical capacity of languages: what is the available fund of words and expressions in a language.
      • What words can be used to name spoons?
        • spoon -> object, tableware, silverware, merchandise, cutlery,

2009f

• (Staab & Studer, 2009) ⇒ Steffen Staab (editor), and Rudi Studer (editor). (2009). “Handbook on Ontologies (2nd edition)." Springer Verlag. ISBN:3540709991
  • QUOTE: An ontology is a formal description of concepts and relationships that can exist for a community of human and/or machine agents. The notion of ontologies is crucial for the purpose of enabling knowledge sharing and reuse.

2008a

• (Corbett, 2008) ⇒ Dan R. Corbett. (2008). “Graph-based Representation and Reasoning for Ontologies.” In: Studies in Computational Intelligence, Springer. 10.1007/978-3-540-78293-3 DOI:10.1007/978-3-540-78293-3
  • QUOTE: An ontology in a given domain [math]\displaystyle{ M }[/math] with respect to a canon is a tuple (T_CM, T_RM, I_M), where
    • T_CM is the set of concept types for the domain [math]\displaystyle{ M }[/math] and T_RM is the set of relation types for the domain M.
    • “I_M is the set of individuals for the domain M.

2008b

• (Dextre Clarke et al., 2008) ⇒ Stella Dextre Clarke, Alan Gilchrist, Ron Davies and Leonard Will. (2008). “Glossary of Terms Relating to Thesauri and Other Forms of Structured Vocabulary for Information Retrieval." Willpower Information
  • QUOTE: ontology
    • specification of the concepts of a domain and their relationships, structured to allow computer processing and reasoning
    • As the nature of the relationships can be specified as part of the ontology, many more types of relationship are possible than in a thesaurus.

2007

• (Obitko, 2007) ⇒ Marek Obitko. (2007). "Ontologies and Semantic Web" Retrieved from: "Translations between Ontologies in Multi-Agent Systems", Ph.D. dissertation, Faculty of Electrical Engineering, Czech Technical University in Prague.
  • QUOTE: The term “ontology” can be defined as an explicit specification of conceptualization. Ontologies capture the structure of the domain, i.e. conceptualization. This includes the model of the domain with possible restrictions. The conceptualization describes knowledge about the domain, not about the particular state of affairs in the domain. In other words, the conceptualization is not changing, or is changing very rarely. Ontology is then specification of this conceptualization - the conceptualization is specified by using particular modeling language and particular terms. Formal specification is required in order to be able to process ontologies and operate on ontologies automatically.

     Ontology describes a domain, while a knowledge base (based on an ontology) describes particular state of affairs. Each knowledge based system or agent has its own knowledge base, and only what can be expressed using an ontology can be stored and used in the knowledge base. When an agent wants to communicate to another agent, he uses the constructs from some ontology. In order to understand in communication, ontologies must be shared between agents (...)

    The purpose of authoring ontologies is also reusing of knowledge. Once ontology is created for a domain, it should be (at least to some degree) reusable for other applications in the same domain. To simplify both ontology development and reuse, modular design is beneficial. The modular design uses inheritance of ontologies - upper ontologies describe general knowledge, and application ontologies describe knowledge for a particular application, as illustrated in the figure below.

    

    Depending on the scope of the ontology, ontology may be classified as follows (see also figure above):

    • upper, generic, top-level ontology - describing general knowledge, such as what is time and what is space
    • domain ontology - describing a domain, such as medical domain or electrical engineering domain, or narrower domains, such as personal computers domain.
    • task - ontology suitable for a specific task, such as assembling parts together
    • application - ontology developed for a specific application, such as assembling personal computers

2006

• (Staab, 2006) ⇒ Steffen Staab. (2006). “Ontologies and the Semantic Web." Tutorial at SMBM-2006.

2005

• (Woodley, 2005b) ⇒ Mary S. Woodley, Gail Clement, and Pete Winn. (2005). “DCMI Glossary." Dublin Core Metadata Initiative.
  • QUOTE: Ontology: A hierarchical structure that formally defines the semantic relationship of a set of concepts. Used to create structured / controlled vocabularies for the discovery or exchange of information. A thesaurus, like the AAT is an example.

2004a

• (Navigli & Velardi, 2004) ⇒ Roberto Navigli, Paola Velardi. (2004). “Learning Domain Ontologies from Document Warehouses and Dedicated Web Sites.” In: Computational Linguistics, 50. doi:10.1162/089120104323093276
  • QUOTE: Creating ontologies is, however, a difficult and time-consuming process that involves specialists from several fields. Philosophical ontologists and artificial intelligence logicians are usually involved in the task of defining the basic kinds and structures of concepts (objects, properties, relations, and axioms) that are applicable in every possible domain. The issue of identifying these very few “basic” principles, now often referred to as foundational ontologies (FOs) (or top, or upper ontologies; see Figure 1) (Gangemi et al. 2002), meets the practical need of a model that has as much generality as possible, to ensure reusability across different domains (Smith and Welty 2001).

     Domain modelers and knowledge engineers are involved in the task of identifying the key domain conceptualizations and describing them according to the organizational backbones established by the foundational ontology. The result of this effort is referred to as the core ontology (CO), which usually includes a few hundred application domain concepts. While many ontology projects eventually succeed in the task of defining a core ontology , populating the third level, which we call the specific domain ontology (SDO), is the actual barrier that very few projects have been able to overcome (e.g., WordNet Fellbaum 1995, Cyc Lenat 1993, and EDR Yokoi 1993), but they pay a price for this inability in terms of inconsistencies and limitations .

    

    Figure 1 The three levels of generality of a domain ontology.

2004b

• (Staab & Studer, 2004) ⇒ Steffen Staab (editor), and Rudi Studer (editor). (2004). “Handbook on Ontologies (1st edition)." Springer Verlag. ISBN:3540408347
  • NOTE: It defines an ontology as a 4-tuple of a set of concepts, a set of relations, a set of instances and a set of axioms.

2003

• (Kalfoglou & Schorlemmer, 2003) ⇒ Yannis Kalfoglou, and Marco Schorlemmer. (2003). “Ontology mapping: the State of the Art.” In: The Knowledge Engineering Review.
  • QUOTE: We shall adopt an algebraic approach and present ontologies as logical theories. An ontology is then a pair O = (S,A), where [math]\displaystyle{ S }[/math] is the (ontological) signature describing the vocabulary — and [math]\displaystyle{ A }[/math] is a set of (ontological) axioms — specifying the intended interpretation of the vocabulary in some domain of discourse. Typically, an ontological signature will be modelled by some mathematical structure. For instance, it could consist of a hierarchy of concept or class symbols modelled as a partial ordered set (poset), together with a set of relations symbols whose arguments are defined over the concepts of the concept hierarchy. The relations themselves might also be structured into a poset. For the purposes of this survey we shall not commit to any particular definition of ontological signature; we refer to the definitions of ‘ontology’, ‘core ontology’, or ‘ontology signature’ in (Kalfoglou and Schorlemmer 2002; Stumme and Maedche 2001; Bench-Capon and Malcolm 1999), respectively, for some examples of what we consider here an ontological signature. In addition to the signature specification, ontological axioms are usually restricted to a particular sort or class of axioms, depending on the kind of ontology.

2002

• (Chella et al., 2002) ⇒ Antonio Chella, Massimo Cossentino, Roberto Pirrone, and Andrea Ruisi. (2002). “Modeling Ontologies for Robotic Environments.” In: Proceedings of the 14th International Conference on Software Engineering and Knowledge Engineering. doi:10.1145/568760.568775
  • QUOTE: An ontology can be defined as a formally specified model of bodies of knowledge defining the concepts used to describe a domain and the relations that hold between them.

1999

• (Chandrasekaran et al, 1999) ⇒ Balakrishnan Chandrasekaran, Jorn R. Josephson, V. and Richard Benjamins. (1999). “What Are Ontologies, and Why Do We Need Them?” IEEE Intelligent Systems. January 1999.
  • QUOTE: Ontological analysis clarifies the structure of knowledge. Given a domain, its ontology forms the heart of any system of knowledge representation for that domain. Without ontologies, or the conceptualizations that underlie knowledge, there cannot be a vocabulary for representing knowledge....Second, ontologies enable knowledge sharing.

1995

• (Guarino & Giaretta, 1995) ⇒ Nicola Guarino, and Pierdaniele Giaretta. (1995). “Ontologies and Knowledge Bases: Towards a Terminological Clarification.” In: "Towards Very Large Knowledge Bases". N.J.L. Mars. (editor). IOS Press. ISBN:78-90-5199-217-5
  • QUOTE: Figure 1: Possible interpretations of the term “ontology”.
    • 1. Ontology as a philosophical discipline
    • 2. Ontology as an informal conceptual system
    • 3. Ontology as a formal semantic account
    • 4. Ontology as a specification of a "conceptualization"
    • 5. Ontology as a representation of a conceptual system via a logical theory
      • 5.1 characterized by specific formal properties
      • 5.2 characterized only by its specific purposes.
    • 6. Ontology as the vocabulary used by a logical theory.
    • 7. Ontology as a (meta-level) specification of a logical theory.
  • The interpretation 1 is radically different from all the others, and its implications are discussed in the next section. The current debate regards the interpretations 2-7: 2 and 3 conceive an ontology as a conceptual "semantic" entity, either formal or informal, while according to the interpretations 5-7 an ontology is a specific "syntactic" object. The interpretation 4, which has been recently proposed as a definition of what an ontology is for the AI community [4, 5], is one of the more problematic, and it will be discussed in detail in the present paper. It may be classified as "syntactic" but its precise meaning depends on the understanding of the terms "specification" and "conceptualization".

1993

• (Gruber, 1993) ⇒ Tom Gruber. (1993). “A Translation Approach to Portable Ontology Specifications.” In: Knowledge Acquisition, 2(5).
  • QUOTE: … A conceptualization is an abstract, simplified view of the world that we wish to represent for some purpose. Every knowledge base, knowledge-based system, or knowledge-level agent is committed to some conceptualization, explicitly or implicitly. An ontology is an explicit specification of a conceptualization.

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