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. [1]
The Vrije Universiteit Brussel
DOGMA, as a dialect of the fact-based modeling approach, has its root in database semantics and model theory. [2] It adheres to the fact-based information management methodology towards Conceptualization and 100% principle of ISO TR9007.
The DOGMA methodological principles include:
DOGMA [7] is an ontology approach and framework that is not restricted to a particular representation language. This approach has some distinguishing characteristics that make it different from traditional ontology approaches such as (i) its groundings in the linguistic representations of knowledge [8] and (ii) the methodological separation of the domain-versus-application conceptualization, which is called the ontology double articulation principle. [9] The idea is to enhance the potential for re-use and design scalability. Conceptualisations are materialised in terms of lexons. A lexon is a 5-tuple declaring either (in some context G):
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.
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.
Ontology double articulation refers to the methodological principle in ontology engineering, that an ontology should be built as separate domain axiomatizations and application axiomatization(s). According to this principle, an application axiomatization should be built in terms of a domain axiomatization. While a domain axiomatization focuses on the characterization of the intended meaning of a vocabulary at the domain level, application axiomatizations mainly focus on the usability of this vocabulary according to certain application/usability perspectives. An application axiomatization is intended to specify the legal models of the application(s) interest. This enables reuse of domain knowledge, use of application knowledge,and interoperability of applications. See.
Lexons could be approximately considered as a combination of an RDF/OWL triple and its inverse, or as a conceptual graph style relation (Sowa, 1984). The next section elaborates more on the notions of context.
The Resource Description Framework (RDF) is a family of World Wide Web Consortium (W3C) specifications originally designed as a metadata data model. It has come to be used as a general method for conceptual description or modeling of information that is implemented in web resources, using a variety of syntax notations and data serialization formats. It is also used in knowledge management applications.
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. Ontologies resemble class hierarchies in object-oriented programming but there are several critical differences. Class hierarchies are meant to represent structures used in source code that evolve fairly slowly whereas ontologies are meant to represent information on the Internet and are expected to be evolving almost constantly. Similarly, ontologies are typically far more flexible as they are meant to represent information on the Internet coming from all sorts of heterogeneous data sources. Class hierarchies on the other hand are meant to be fairly static and rely on far less diverse and more structured sources of data such as corporate databases.
Another distinguishing characteristic of DOGMA is the explicit duality (orthogonal to double articulation) in interpretation between the language level and conceptual level. The goal of this separation is primarily to disambiguate the lexical representation of terms in a lexon (on the language level) into concept definitions (on the conceptual level), which are word senses taken from lexical resources such as WordNet. [10] The meaning of the terms in a lexon is dependent on the context of elicitation. [11]
Double articulation, or duality of patterning is a concept used in linguistics and semiotics. It refers to the two-level structure inherent to a sign system, insofar as it is composed by two kinds of elements: 1) significant or meaningful, and 2) distinctive or meaningless.
WordNet is a lexical database for the English language. It groups English words into sets of synonyms called synsets, provides short definitions and usage examples, and records a number of relations among these synonym sets or their members. WordNet can thus be seen as a combination of dictionary and thesaurus. While it is accessible to human users via a web browser, its primary use is in automatic text analysis and artificial intelligence applications. The database and software tools have been released under a BSD style license and are freely available for download from the WordNet website. Both the lexicographic data and the compiler for producing the distributed database are available.
For example, consider a term “capital”. If this term was elicited from a typewriter manual, it has a different meaning (read: concept definition) than when elicited from a book on marketing. The intuition that a context provides here is: a context is an abstract identifier that refers to implicit or tacit assumptions in a domain, and that maps a term to its intended meaning (i.e. concept identifier) within these assumptions. [12]
A tacit assumption or implicit assumption is an assumption that includes the underlying agreements or statements made in the development of a logical argument, course of action, decision, or judgment that are not explicitly voiced nor necessarily understood by the decision maker or judge. Often, these assumptions are made based on personal life experiences, and are not consciously apparent in the decision making environment. These assumptions can be the source of apparent paradoxes, misunderstandings and resistance to change in human organizational behavior.
Ontologies naturally co-evolve with their communities of use. Therefore, in De Leenheer (2007) [13] he identified a set of primitive operators for changing ontologies. We make sure these change primitives are conditional, which means that their applicability depends on pre- and post-conditions. [14] Doing so, we guarantee that only valid structures can be built.
De Leenheer and de Moor (2005) distinguished four key characteristics of context:
Based on this, they identified three different types of context dependencies within one ontology (intra-ontological) and between different ontologies (inter-ontological): articulation, application, and specialisation. One particular example in the sense of conceptual graph theory [15] would be a specialisation dependency for which the dependency constraint is equivalent to the conditions for CG-specialisation [16]
Context dependencies provide a better understanding of the whereabouts of knowledge elements and their inter-dependencies, and consequently make negotiation and application less vulnerable to ambiguity, hence more practical.
A semantic network, or frame network is a knowledge base that represents semantic relations between concepts in a network. This is often used as a form of knowledge representation. It is a directed or undirected graph consisting of vertices, which represent concepts, and edges, which represent semantic relations between concepts, mapping or connecting semantic fields.
John Florian Sowa is an American computer scientist, an expert in artificial intelligence and computer design, and the inventor of conceptual graphs.
A modeling language is any artificial language that can be used to express 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.
Object-role modeling (ORM) is used to model the semantics of a universe of discourse. ORM is often used for data modeling and software engineering.
A metamodel or surrogate model is a model of a model, and metamodeling is the process of generating such metamodels. Thus metamodeling or meta-modeling is the analysis, construction and development of the frames, rules, constraints, models and theories applicable and useful for modeling a predefined class of problems. As its name implies, this concept applies the notions of meta- and modeling in software engineering and systems engineering. Metamodels are of many types and have diverse applications.
Semantic integration is the process of interrelating information from diverse sources, for example calendars and to do lists, email archives, presence information, documents of all sorts, contacts, search results, and advertising and marketing relevance derived from them. In this regard, semantics focuses on the organization of and action upon information by acting as an intermediary between heterogeneous data sources, which may conflict not only by structure but also context or value.
Enterprise modelling is the abstract representation, description and definition of the structure, processes, information and resources of an identifiable business, government body, or other large organization.
Machine interpretation of documents and services in Semantic Web environment is primarily enabled by (a) the capability to mark documents, document segments and services with semantic tags and (b) the ability to establish contextual relations between the tags with a domain model, which is formally represented as ontology. Human beings use natural languages to communicate an abstract view of the world. Natural language constructs are symbolic representations of human experience and are close to the conceptual model that Semantic Web technologies deal with. Thus, natural language constructs have been naturally used to represent the ontology elements. This makes it convenient to apply Semantic Web technologies in the domain of textual information. In contrast, multimedia documents are perceptual recording of human experience. An attempt to use a conceptual model to interpret the perceptual records gets severely impaired by the semantic gap that exists between the perceptual media features and the conceptual world. Notably, the concepts have their roots in perceptual experience of human beings and the apparent disconnect between the conceptual and the perceptual world is rather artificial. The key to semantic processing of multimedia data lies in harmonizing the seemingly isolated conceptual and the perceptual worlds. Representation of the Domain knowledge needs to be extended to enable perceptual modeling, over and above conceptual modeling that is supported. The perceptual model of a domain primarily comprises observable media properties of the concepts. Such perceptual models are useful for semantic interpretation of media documents, just as the conceptual models help in the semantic interpretation of textual documents.
The TOVE project, acronym of TOronto Virtual Enterprise project is a project to develop an ontological framework for enterprise integration (EI) based on and suited for enterprise modeling. In the beginning of the 1990s it was initiated by Mark S. Fox and others at the University of Toronto.
The three-schema approach, or three-schema concept, in software engineering is an approach to building information systems and systems information management that originated in the 1970s. It proposes three different views in systems development, with conceptual modelling being considered the key to achieving data integration.
Business Semantics Management (BSM) encompasses the technology, methodology, organization, and culture that brings business stakeholders together to collaboratively realize the reconciliation of their heterogeneous metadata; and consequently the application of the derived business semantics patterns to establish semantic alignment between the underlying data structures.
Ontology engineering in computer science, information science and systems engineering is a field which studies the methods and methodologies for building ontologies: formal representations of a set of concepts within a domain and the relationships between those concepts. 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.
Semantic decision tables (SDT) use modern ontology engineering (OE) technologies to enhance traditional decision tables. The term "semantic decision table" was coined by Yan Tang and Prof. Robert Meersman from VUB STARLab in 2006. An SDT is a decision table(s) 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.
Semantic Annotations for WSDL and XML Schema (SAWSDL) is a 2007 published technical recommendation of W3C in the context of Semantic Web framework:
Model Driven Interoperability (MDI) is a methodological framework, which provides a conceptual and technical support to make interoperable enterprises using ontologies and semantic annotations, following model driven development (MDD) principles.
OntoUML is a ontologically well-founded language for Ontology-driven Conceptual Modeling. OntoUML is built as a UML extension based on the Unified Foundational Ontology (UFO). The foundations of UFO and OntoUML can be traced back to Giancarlo Guizzardi's Ph.D. thesis "Ontological foundations for structural conceptual models". In his work, he proposed a novel foundational ontology for conceptual modeling (UFO) and employed it to evaluate and re-design a fragment of the UML 2.0 metamodel for the purposes of conceptual modeling and domain ontology engineering.
Bernhard Karl Thalheim is a German computer scientist and Professor of Information Systems Engineering at the University of Kiel who is known for conceptual modeling and its theoretical foundational contributions.
Henderik Alex (Erik) Proper is a Dutch computer scientist, Professor of Information Systems at the Radboud University Nijmegen, and IT consultant, known for work on conceptual modelling of information systems and enterprise engineering.