scholarly journals Using Cognitive Entropy to Manage Uncertain Concepts in Formal Ontologies

2008 ◽  
pp. 315-329 ◽  
Author(s):  
Joaquín Borrego-Díaz ◽  
Antonia M. Chávez-González
Keyword(s):  
Formal Ontologies

Towards Formal Ontologies for Technology Risk Measurement in the Banking Industry

2005 ◽  
Author(s):  
Christian Cuske ◽  
Axel Korthaus ◽  
Stefan Seedorf ◽  
Peter Tomczyk
Keyword(s):  
Banking Industry ◽  
Risk Measurement ◽  
Formal Ontologies

scholarly journals Linguistic scales: current state-of-the-art

2019 ◽  
Vol 17 (3) ◽  
pp. 5-17
Author(s):  
Mariya K. Timofeeva
Keyword(s):  
Experimental Investigations ◽  
Dialogue Systems ◽  
Practical Application ◽  
General Picture ◽  
Current State ◽  
Formal Ontologies ◽  
Semantics And Pragmatics ◽  
Subject Areas ◽  
Basic Ideas ◽  
Concrete Language

The aim of this article consists in reviewing the basic areas of studying language scales in pragmatics; several prospects of their investigation are discussed. Presently, language scales are the object of intensive research in semantics and pragmatics, from linguistic, logical, psycholinguistic, and neuro-linguistic perspectives. We are interested mainly in pragmatics (although the area of semantics is also considered) and concentrate on linguistic rather than logical, psycholinguistic, or neuro-linguistic aspects. The article continues the series of publications intending to review and systematize pragmatic investigation in basic topical areas. An interest in studying linguistic scales in pragmatics has increased primarily due to the works of H. P. Grice, L. Horn, G. Gazdar, and S. Levinson. An important class of general pragmatic principles of communication was introduced by H. P. Grice and then was elaborated on greater detail in neo-gricean pragmatics. This class of principles specifies quantity characteristics of communication, and can be defined in terms of scales. Language scales give rise to a special class of implicatures called “scalar implicatures”. In many cases, it is necessary for a speaker to choose some position on a scale. Scalar implicature appears as a result of this choice. Each position potentially generates a certain set of implications. This pragmatic phenomenon is intensively studied in linguistics, logic, and experimental investigations. The literature in the area is ample; the article draws only a general picture of the area. The article proposes: 1) to elicit a system of potential language scales for a concrete language; 2) to consider individual / situational scales; 3) to consider dynamics of scales in speech (in accordance with basic ideas of dynamic semantics). The proposed areas of practical application are the following: stylistic analysis and studying an author’s style, modelling of reasoning and communication (particularly in dialogue systems), constructing formal ontologies of different subject areas.

scholarly journals Turning Informal Thesauri into Formal Ontologies: A Feasibility Study on Biomedical Knowledge Re-Use

2003 ◽  
Vol 4 (1) ◽  
pp. 94-97 ◽  
Author(s):  
Udo Hahn
Keyword(s):  
Domain Knowledge ◽  
Large Scale ◽  
Description Logics ◽  
Biomedical Domain ◽  
Biomedical Knowledge ◽  
Knowledge Conversion ◽  
Formal Knowledge ◽  
Knowledge Repositories ◽  
Formal Ontologies ◽  
Rigorous Description

This paper reports a large-scale knowledge conversion and curation experiment. Biomedical domain knowledge from a semantically weak and shallow terminological resource, the UMLS, is transformed into a rigorous description logics format. This way, the broad coverage of the UMLS is combined with inference mechanisms for consistency and cycle checking. They are the key to proper cleansing of the knowledge directly imported from the UMLS, as well as subsequent updating, maintenance and refinement of large knowledge repositories. The emerging biomedical knowledge base currently comprises more than 240 000 conceptual entities and hence constitutes one of the largest formal knowledge repositories ever built.

On the Integration of Clinical Archetypes with Ontologies and Rules

2013 ◽  
pp. 82-147 ◽  
Author(s):  
Leonardo Lezcano
Keyword(s):  
Ad Hoc ◽  
Teaching Hospitals ◽  
Formal Representation ◽  
Automatic Translation ◽  
Ontology Language ◽  
Formal Ontologies ◽  
Wide Range ◽  
Ontology Languages ◽  
Clinical Rules ◽  
Alerting Systems

This chapter presents an approach to translate definitions expressed in openEHR Archetype Definition Language (ADL) to a formal representation using ontology languages. The approach is implemented in the ArchOnt framework, which is also described. The integration of those formal representations with clinical rules is then studied, providing an approach to reuse reasoning on concrete instances of clinical data. Sharing the knowledge expressed in the form of rules is coherent with the philosophy of open sharing underlying clinical archetypes, and it also extends reuse to propositions of declarative knowledge as those encoded for example in clinical guidelines. Thus, this chapter describes the techniques to map archetypes to formal ontologies and how rules can be attached to the resulting representation. In addition, the translation allows specifying logical bindings to equivalent clinical concepts from other knowledge sources. Such bindings encourage reuse as well as ontology reasoning and navigability across different ontologies. Another significant contribution of the chapter is the application of the presented approach as part of two research projects in collaboration with teaching hospitals in Madrid. Examples taken from those cases, such as the development of alerting systems aimed at improving patient safety, are explained. Besides the direct applications described, the automatic translation of archetypes to an ontology language fosters a wide range of semantic and reasoning activities to be designed and implemented on top of a common representation instead of taking an ad-hoc approach.

Ontologies and E-Learning

2005 ◽  
pp. 345-356
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

Ontologies and E-Learning

2011 ◽  
pp. 322-331 ◽  
Author(s):  
Matteo Cristani
Keyword(s):  
Artificial Intelligence ◽  
Knowledge Management ◽  
Natural Language Processing ◽  
Knowledge Sharing ◽  
Language Processing ◽  
Information Integration ◽  
Knowledge Engineering ◽  
Formal Ontologies ◽  
E Learning ◽  
Intelligent Information

What is an ontology? Why is this relevant to a learning environment? It is quite well-established in recent investigations on information systems that formal ontologies area crucial problem to deal with, and in fact, received a lot of attention in several different communities, such as knowledge management, knowledge engineering, natural language processing, intelligent information integration, and so on (Fensel, 2000).Ontologies have been developed in artificial intelligence to facilitate knowledge sharing and reuse. The viewpoint we adopt here is taken from the general considerations on the use of philosophical issues in artificial intelligence: “the systematic, formal, axiomatic development of the logic of all forms and modes of being” (Cocchiarella,1991). Another commonly accepted definition is that an ontology is an explicit specification of a shared conceptualization that holds in a particular context.

A Semantic Matching Strategy for Very Large Knowledge Bases Integration

2020 ◽  
Vol 15 (2) ◽  
pp. 1-29
Author(s):  
Antonio M. Rinaldi ◽  
Cristiano Russo ◽  
Kurosh Madani
Keyword(s):  
State Of The Art ◽  
Knowledge Bases ◽  
Data Reuse ◽  
Semantic Matching ◽  
Formal Ontologies ◽  
Upper Level ◽  
Analyse Data ◽  
Matching Strategy ◽  
Definition Of ◽  
Matching Techniques

Over the last few decades, data has assumed a central role, becoming one of the most valuable items in society. The exponential increase of several dimensions of data, e.g. volume, velocity, variety, veracity, and value, has led the definition of novel methodologies and techniques to represent, manage, and analyse data. In this context, many efforts have been devoted in data reuse and integration processes based on the semantic web approach. According to this vision, people are encouraged to share their data using standard common formats to allow more accurate interconnection and integration processes. In this article, the authors propose an ontology matching framework using novel combinations of semantic matching techniques to find accurate mappings between formal ontologies schemas. Moreover, an upper-level ontology is used as a semantic bridge. An implementation of the proposed framework is able to retrieve, match, and align ontologies. The framework has been evaluated with the state-of-the-art ontologies in the domain of cultural heritage and its performances have been measured by means of standard measures.

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