Acquisition And Structuring Of An Ontology Within Conceptual Graphs (original) (raw)

Structuration and acquisition of medical knowledge. Using UMLS in the conceptual graph formalism

Proceedings / the ... Annual Symposium on Computer Application [sic] in Medical Care. Symposium on Computer Applications in Medical Care

The ue of a taxonomy, such as the concept type lattice (CTL) of Conceptual Graphs. is a central structuring piece in a knowledge-based system. The knowledge it contains is constantly used by the system, and its structure provides a guide for the acquisition of other pieces of knowledge. We show how UMLS can be used as a knowledge resource to build a CTL and how the CTL can help the process of acquisition for other kinds of knowledge. We il. lustrte this method in the contezt of the MENELAS natural language understanding project.

Conceptual graphs as a universal knowledge representation

Computers & Mathematics with Applications, 1992

Conceptual graphs are a knowledge representation language designed as a synthesis of several different traditions. First are the semantic networks, which have been used in machine translation and computational linguistics for over thirty years. Second are the logic-based techniques of unification, lambda calculus, and Peirce's existential graphs. Third is the linguistic research based on Tosni~re's dependency graphs and various forms of case grammar and thematic relations. Fourth are the dataflow diagrams and Petri nets, which provide a computational me,~hani,m for relating conceptual graphs to external procedures and databases. The result is a highly expressive system of logic with a direct mapping to and from natural languages. The lambda calculus supports the definitions for a taxonomic system and provides a general mecha~m for restructuring knowledge bases. With the definitional mechanisms, conceptual graphs can be used as an intermediate stage between natural languages and the rules and frames of expert systems-an important feature for knowledge acquisition and for help and exphaatious. During the past five years, conceptual graphs have been applied to almost every aspect of AI, ranging from expert systems and natural langm~e to computer vision and neural networks. This paper surveys conceptual graphs, their development from each of these traditions, and the applications based on them.

Methodological Principles for Structuring an "Ontology

1995

The knowledge used in most AI applications does not rely on a formal model of the domain. Therefore, it has to be normalized to ensure that the formal exploitation of its representation conforms to its meaning in the domain. Considering the intensional non extensional nature of concepts, which re ects the essences of the objects they denote, this normalization relies on a commitment o n t ype denitions by necessary and su cient conditions at the knowledge level. Our claim is that the taxonomic structure that accounts for the intensional nature of the ontology can be nothing but a tree. From this starting point, we derive methodological principles to constrain and justify the structuring of ontological types. Based on this methodology, w e advocate understandability o f a n o n tology rather than a putative reusability.

Building ontological meaning in a lexico-conceptual knowledge base

Onomázein: Revista de Lingüística, …

Framed within the world of Artificial Intelligence, and more precisely within the project FunGramKB, i.e. a user-friendly environment for the semiautomatic construction of a multipurpose lexico-conceptual knowledge base for Natural Language Processing systems, the aim of this paper is two-fold. Firstly, we shall provide a necessarily non-exhaustive theoretical discussion of FunGramKB in which we will introduce the main elements that make up its Ontology (i.e. Thematic Frames, Meaning Postulates, different types of concepts, etc.). Secondly, we will describe the meticulous process carried out by knowledge engineers when populating this conceptually-driven Ontology. In doing so, we shall examine various examples belonging to the domain of ‘change’ or #TRANSFORMATION (in the COREL notation), in an attempt to show how conceptual knowledge can be modeled in for Artificial Intelligence purposes.

Issues in the Structuring and Acquisition of an Ontology for Medical Language Understanding

Methods of Information in Medicine, 1995

Medical natural language understanding basically aims at representing the contents of medical texts in a formal, conceptual representation. The understanding process itself increasingly relies on a body of domain knowledge, generally expressed in the same conceptual formalism. The design of such a conceptual representation is a key knowledge acquisition issue. When representing knowledge, the most important point is to ensure that the formal exploitation of the knowledge representation conforms to its meaning in the domain. In this paper, we examine some methodological and theoretical principles to enforce this conformity. These principles result from our experience in Menelas, a medical language understanding project. 1

Ontologically Correct Taxonomies by Construction

DKE, 2022

Taxonomies play a central role in conceptual domain modeling, having a direct impact in areas such as knowledge representation, ontology engineering, and software engineering, as well as knowledge organization in information sciences. Despite this, there is little guidance on how to build high-quality taxonomies, with notable exceptions being the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules on the formation of taxonomic structures. In this paper, we show how to leverage the formal rules underlying these techniques in order to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. In addition to considering the ontological micro-theory of endurant types underlying OntoClean and OntoUML, we also employ the MLT (Multi-Level Theory) microtheory of high-order types, which allows us to address multi-level taxonomies based on the powertype pattern. To validate our proposal, we formalize the model building operations as a graph grammar that incorporates both micro-theories. We apply automatic verification techniques over the grammar language to show that the graph grammar is sound, i.e., that all taxonomies produced by the grammar rules are correct, at least up to a certain size. We also show that the rules can generate all correct taxonomies up to a certain size (a completeness result).

A Linguistic Approach to Conceptual Modeling with Semantic Types and OntoUML

2010 14th IEEE International Enterprise Distributed Object Computing Conference Workshops, 2010

The process of conceptual modeling involves the acquisition of concepts (and of the signs that represent them) used in the Universe of Discourse (UoD) being modeled, and the creation of the model (as a concrete artifact) according to a modeling language grammar. The knowledge about the UoD is obtained from a variety of sources, all of which are mostly expressed in a natural language. It is correct to say that conceptual modeling is much similar to language translation i.e., identifying concepts that are represented by signs of a language, and then representing those same concepts in a different language. Also, the semantic quality of the resulting model (translation) is directly affected by the modeler's (translator's) understanding of the source material. As so, conceptual modeling activities can benefit from an analysis carried out from a linguistic point of view, as well as from the use of a modeling language which constructs allow for a representation that is semantically equivalent to the natural language original descriptions. This work proposes a linguistic approach to conceptual modeling based on the notion of semantic types, and on the use of OntoUML as a modeling language. The proposed approach is illustrated in an example.

Conceptual graphs and formal concept analysis

Conceptual structures: Fulfilling Peirce's dream, 1997

Analysis may be combined to obtain a formalization of Elementary Logic which is useful for knowledge representation and processing. For this, a translation of conceptual graphs to formal contexts and concept lattices is described through an example. Using a suitable mathematization of conceptual graphs, basics of a uni ed mathematical theory for Elementary Logic are proposed.

Building Correct Taxonomies with a Well-Founded Graph Grammar

Taxonomies play a central role in conceptual domain modeling having a direct impact in areas such as knowledge representation, ontology engineering, software engineering, as well as in knowledge organization in information sciences. Despite their key role, there is in the literature little guidance on how to build high-quality taxonomies, with notable exceptions such as the OntoClean methodology, and the ontology-driven conceptual modeling language OntoUML. These techniques take into account the ontological meta-properties of types to establish well-founded rules for forming taxonomic structures. In this paper, we show how to leverage on the formal rules underlying these techniques to build taxonomies which are correct by construction. We define a set of correctness-preserving operations to systematically introduce types and subtyping relations into taxonomic structures. To validate our proposal, we formalize these operations as a graph grammar. Moreover, to demonstrate our claim of correctness by construction , we use automatic verification techniques over the grammar language to show that: (i) all taxonomies produced by the grammar rules are correct; and (ii) the rules can generate all correct taxonomies.