Conceptualization (information science)

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Chart showing the relation between a conceptualization in information science, its various ontologies (each with its own specialized language), and their shared ontological commitment. Ontological commitments.png
Chart showing the relation between a conceptualization in information science, its various ontologies (each with its own specialized language), and their shared ontological commitment.

In information science a conceptualization is an abstract simplified view of some selected part of the world, containing the objects, concepts, and other entities that are presumed of interest for some particular purpose and the relationships between them. [2] [3] An explicit specification of a conceptualization is an ontology, and it may occur that a conceptualization can be realized by several distinct ontologies. [2] An ontological commitment in describing ontological comparisons is taken to refer to that subset of elements of an ontology shared with all the others. [4] [5] "An ontology is language-dependent", its objects and interrelations described within the language it uses, while a conceptualization is always the same, more general, its concepts existing "independently of the language used to describe it". [6] The relation between these terms is shown in the figure to the right.

Contents

Not all workers in knowledge engineering use the term ‘conceptualization’, but instead refer to the conceptualization itself, or to the ontological commitment of all its realizations, as an overarching ontology. [7]

Purpose and implementation

As a higher level abstraction, a conceptualization facilitates the discussion and comparison of its various ontologies, facilitating knowledge sharing and reuse. [7] [8] Each ontology based upon the same overarching conceptualization maps the conceptualization into specific elements and their relationships.

The question then arises as to how to describe the 'conceptualization' in terms that can encompass multiple ontologies. This issue has been called the 'Tower of Babel' problem, that is, how can persons used to one ontology talk with others using a different ontology? [3] [8] This problem is easily grasped, but a general resolution is not at hand. It can be a 'bottom-up' or a 'top-down' approach, or something in between. [9]

However, in more artificial situations, such as information systems, the idea of a 'conceptualization' and the 'ontological commitment' of various ontologies that realize the 'conceptualization' is possible. [6] [10] The formation of a conceptualization and its ontologies involves these steps: [11]

An example of moving conception into a language leading to a variety of ontologies is the expression of a process in pseudocode (a strictly structured form of ordinary language) leading to implementation in several different formal computer languages like Lisp or Fortran. The pseudocode makes it easier to understand the instructions and compare implementations, but the formal languages make possible the compilation of the ideas as computer instructions. [ citation needed ]

Another example is mathematics, where a very general formulation (the analog of a conceptualization) is illustrated with 'applications' that are more specialized examples. For instance, aspects of a function space can be illustrated using a vector space or a topological space that introduce interpretations of the 'elements' of the conceptualization and additional relationships between them but preserve the connections required in the function space. [ citation needed ]

See also

Related Research Articles

Knowledge representation and reasoning is the field of artificial intelligence (AI) dedicated to representing information about the world in a form that a computer system can use to solve complex tasks such as diagnosing a medical condition or having a dialog in a natural language. Knowledge representation incorporates findings from psychology about how humans solve problems and represent knowledge in order to design formalisms that will make complex systems easier to design and build. Knowledge representation and reasoning also incorporates findings from logic to automate various kinds of reasoning, such as the application of rules or the relations of sets and subsets.

<span class="mw-page-title-main">Semantic Web</span> Extension of the Web to facilitate data exchange

The Semantic Web, sometimes known as Web 3.0, is an extension of the World Wide Web through standards set by the World Wide Web Consortium (W3C). The goal of the Semantic Web is to make Internet data machine-readable.

In 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. More simply, an ontology is a way of showing the properties of a subject area and how they are related, by defining a set of terms and relational expressions that represent the entities in that subject area. The field which studies ontologies so conceived is sometimes referred to as applied ontology.

An ontological commitment of a language is one or more objects postulated to exist by that language. The 'existence' referred to need not be 'real', but exist only in a universe of discourse. As an example, legal systems use vocabulary referring to 'legal persons' that are collective entities that have rights. One says the legal doctrine has an ontological commitment to non-singular individuals.

The Resource Description Framework (RDF) is a World Wide Web Consortium (W3C) standard originally designed as a data model for metadata. It has come to be used as a general method for description and exchange of graph data. RDF provides a variety of syntax notations and data serialization formats, with Turtle currently being the most widely used notation.

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.

The Web Ontology Language (OWL) is a family of knowledge representation languages for authoring ontologies. Ontologies are a formal way to describe taxonomies and classification networks, essentially defining the structure of knowledge for various domains: the nouns representing classes of objects and the verbs representing relations between the objects.

A modeling language is any artificial language that can be used to express data, information or knowledge or systems in a structure that is defined by a consistent set of rules. The rules are used for interpretation of the meaning of components in the structure Programing language.

<span class="mw-page-title-main">Information model</span>

An information model in software engineering is a representation of concepts and the relationships, constraints, rules, and operations to specify data semantics for a chosen domain of discourse. Typically it specifies relations between kinds of things, but may also include relations with individual things. It can provide sharable, stable, and organized structure of information requirements or knowledge for the domain context.

The term conceptual model refers to any model that is formed after a conceptualization or generalization process. Conceptual models are often abstractions of things in the real world, whether physical or social. Semantic studies are relevant to various stages of concept formation. Semantics is fundamentally a study of concepts, the meaning that thinking beings give to various elements of their experience.

In information science, an upper ontology is an ontology that consists of very general terms that are common across all domains. An important function of an upper ontology is to support broad semantic interoperability among a large number of domain-specific ontologies by providing a common starting point for the formulation of definitions. Terms in the domain ontology are ranked under the terms in the upper ontology, e.g., the upper ontology classes are superclasses or supersets of all the classes in the domain ontologies.

Gellish is an ontology language for data storage and communication, designed and developed by Andries van Renssen since mid-1990s. It started out as an engineering modeling language but evolved into a universal and extendable conceptual data modeling language with general applications. Because it includes domain-specific terminology and definitions, it is also a semantic data modelling language and the Gellish modeling methodology is a member of the family of semantic modeling methodologies.

Frames are an artificial intelligence data structure used to divide knowledge into substructures by representing "stereotyped situations". They were proposed by Marvin Minsky in his 1974 article "A Framework for Representing Knowledge". Frames are the primary data structure used in artificial intelligence frame languages; they are stored as ontologies of sets.

DOGMA, short for Developing Ontology-Grounded Methods and Applications, is the name of research project in progress at Vrije Universiteit Brussel's STARLab, Semantics Technology and Applications Research Laboratory. It is an internally funded project, concerned with the more general aspects of extracting, storing, representing and browsing information.

The Semantics of Business Vocabulary and Business Rules (SBVR) is an adopted standard of the Object Management Group (OMG) intended to be the basis for formal and detailed natural language declarative description of a complex entity, such as a business. SBVR is intended to formalize complex compliance rules, such as operational rules for an enterprise, security policy, standard compliance, or regulatory compliance rules. Such formal vocabularies and rules can be interpreted and used by computer systems. SBVR is an integral part of the OMG's model-driven architecture (MDA).

<span class="mw-page-title-main">Ontology engineering</span> Field that studies the methods and methodologies for building ontologies

In computer science, information science and systems engineering, ontology engineering is a field which studies the methods and methodologies for building ontologies, which encompasses a representation, formal naming and definition of the categories, properties and relations between the concepts, data and entities of a given domain of interest. In a broader sense, this field also includes a knowledge construction of the domain using formal ontology representations such as OWL/RDF. A large-scale representation of abstract concepts such as actions, time, physical objects and beliefs would be an example of ontological engineering. Ontology engineering is one of the areas of applied ontology, and can be seen as an application of philosophical ontology. Core ideas and objectives of ontology engineering are also central in conceptual modeling.

Contemporary ontologies share many structural similarities, regardless of the ontology language in which they are expressed. Most ontologies describe individuals (instances), classes (concepts), attributes, and relations.

A semantic decision table uses modern ontology engineering technologies to enhance traditional a decision table. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab in 2006. A semantic decision table is a set of decision tables properly annotated with an ontology. It provides a means to capture and examine decision makers’ concepts, as well as a tool for refining their decision knowledge and facilitating knowledge sharing in a scalable manner.

In philosophy, a process ontology refers to a universal model of the structure of the world as an ordered wholeness. Such ontologies are fundamental ontologies, in contrast to the so-called applied ontologies. Fundamental ontologies do not claim to be accessible to any empirical proof in itself but to be a structural design pattern, out of which empirical phenomena can be explained and put together consistently. Throughout Western history, the dominating fundamental ontology is the so-called substance theory. However, fundamental process ontologies have become more important in recent times, because the progress in the discovery of the foundations of physics has spurred the development of a basic concept able to integrate such boundary notions as "energy," "object", and those of the physical dimensions of space and time.

Knowledge extraction is the creation of knowledge from structured and unstructured sources. The resulting knowledge needs to be in a machine-readable and machine-interpretable format and must represent knowledge in a manner that facilitates inferencing. Although it is methodically similar to information extraction (NLP) and ETL, the main criterion is that the extraction result goes beyond the creation of structured information or the transformation into a relational schema. It requires either the reuse of existing formal knowledge or the generation of a schema based on the source data.

References

  1. ↑ This figure has similarities with Figure 1 in Guarino and to slide 7 in the talk by van Harmelen Archived 2009-05-30 at the Wayback Machine . These sources are among the references to this article. The figure is imported from Citizendium.
  2. 1 2 Gruber, Thomas R. (June 1993). "A translation approach to portable ontology specifications" (PDF). Knowledge Acquisition . 5 (2): 199–220. CiteSeerX   10.1.1.101.7493 . doi:10.1006/knac.1993.1008. S2CID   15709015.
  3. 1 2 Smith, Barry (2003). "Chapter 11: Ontology" (PDF). In Luciano Floridi (ed.). Blackwell Guide to the Philosophy of Computing and Information. Blackwell. pp. 155–166. ISBN   978-0631229186.
  4. ↑ Roger F. Gibson (1999). "Ontological commitment". In Robert Audi (ed.). The Cambridge Dictionary of Philosophy (Paperback 2nd ed.). Cambridge University Press. p. 631. ISBN   978-0521637220. A shortened version of that definition is as follows:
    The ontological commitments of a theory are those things which occur in all the ontologies of that theory. To explain further, the ontology of a theory consists of the objects the theory makes use of. A dependence of a theory upon an object is indicated if the theory fails when the object is omitted. However, the ontology of a theory is not necessarily unique. A theory is ontologically committed to an object only if that object occurs in all the ontologies of that theory. A theory also can be ontologically committed to a class of objects if that class is populated (not necessarily by the same objects) in all its ontologies. [italics added]
  5. ↑ Luigi Ceccaroni; Myriam Ribiere (2002). "Modeling utility ontologies in agentcities with a collaborative approach" (PDF). Proceedings of the Workshop AAMAS. A quotation follows:
    “Researchers...come from different areas of study and have different perspectives on modeling, but significantly they pledged to adopt the same ontological commitment. That is, they agree to adopt common, predefined ontologies...to express general categories, even if they do not completely agree on the modeling behind the ontological representations. Where ontological commitment is lacking, it is difficult to converse clearly about a domain and to benefit from knowledge representations developed by others... Ontological commitment is thus an integral aspect of ontological engineering.” [italics added]
  6. 1 2 Guarino, Nicola (1998). "Formal Ontology in Information Systems". In Nicola Guarino (ed.). Formal Ontology in Information Systems (Proceedings of FOIS '98, Trento, Italy). IOS Press. pp. 3 ff. ISBN   978-90-5199-399-8.
  7. 1 2 For example, see Luigi Ceccaroni; Myriam Ribiere (2002). "Modeling utility ontologies in agentcities with a collaborative approach" (PDF). Proceedings of the Workshop AAMAS.
  8. 1 2 Frank van Harmelen. "Ontology mapping: a way out of the medical tower of babel" (PDF). Archived from the original (PDF) on 2009-05-30. Retrieved 2013-08-02.
  9. ↑ In information science, one approach to finding a conceptualization (or avoiding it and using an automated comparison) is called 'ontology alignment' or 'ontology matching'. See for example, Jérôme. Euzenat; Pavel Shvaiko (2007). Ontology Matching. Springer. ISBN   978-3540496120.
  10. ↑ Nicola Guarino; Massimiliano Carrara; Pierdaniele Giaretta (1994). "Formalizing ontological commitments" (PDF). AAAI. 94: 560–567.[ permanent dead link ]
  11. ↑ Maja Hadzic; Pornpit Wongthongtham; Elizabeth Chang; Tharam Dillon (2009). "Chapter 7: Design methodology for integrated systems - Part I (Ontology design)". Ontology-Based Multi-Agent Systems. Springer. pp. 111 ff. ISBN   978-3642019036.

Further reading

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