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Terminological ontologies Ph.d. course on representation formalisms for ontologies, Copenhagen, 30.10.-1.11.02 Bodil Nistrup Madsen Department of Computational.

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Presentation on theme: "Terminological ontologies Ph.d. course on representation formalisms for ontologies, Copenhagen, 30.10.-1.11.02 Bodil Nistrup Madsen Department of Computational."— Presentation transcript:

1 Terminological ontologies Ph.d. course on representation formalisms for ontologies, Copenhagen, Bodil Nistrup Madsen Department of Computational Linguistics, Copenhagen Business School & The Danish Terminology Centre, DANTERM

2 Approaches to ontology design Jørgen Fischer Nilsson – Ontologies according to purpose DB application constraints cf. Enrico Franconi Text knowledge domain conceptual catalogue of meaningful concepts cf. OntoQuery

3 Approaches to ontology design Other purposes: Ontologies form the basis for the definition of a database structure systems for digital document handling e-commerce electronic health care records. The unambiguous determination and systematic description of concepts within the field of operation of IT systems is an important precondition for the successful development of these systems and for usable results.

4 Approaches to ontology design Jørgen Fischer Nilsson Slide 5 Conceptual approaches (language neutral): a) categories in a general top level ontology b) conceptual models of particular domains Linguistic approaches (language specific): semantic lexicons recording vocabulary and relationships between words Intermediate or combined approaches: terminolgoy models recording terms (words, collocations, phrases) specific to a domain

5 Approaches to ontology design Terminological ontology: systematic specification of concepts belonging to a specific subject field and the relations between the concepts Purpose: clarification and definition of concepts, and for translation purposes also establishment of equivalence relations between concepts in various languages

6 ontology philoso- phical ontology pragmatic ontology top level ontology universal ontology domain specific ontology general ontology task specific ontology task inde- pendant ontology language inde- pendant ontology formal ontology not formal onto- logy VIEW specific ontology LEVEL SUBJECT PURPOSE LANGUAGE FORMALIZING application specific ontology Guarino, Nicola (1998). Formal Ontology and Information Systems,. In: Formal Ontology in Information Systems, Proceedings of the First International Conference (FOIS'98), June 6-8, Trento, Italy, Ed. Nicola Guarino. Amsterdam: IOS Press. Bodil Nistrup Madsen, based on a.o.:

7 CAOS: Computer Aided Ontology Structuring Madsen, Bodil Nistrup; Hanne Erdman Thomsen & Carl Vikner: 'Computer Assisted Ontology Structuring'. In: Melby, Alan (ed.): Proceedings of TKE '02 - Terminology and Knowledge Engineering, INRIA, Frankrig, Madsen, Bodil Nistrup; Hanne Erdman Thomsen & Carl Vikner: 'Data Modelling and Conceptual Modelling in the Domain of Terminology'. In: Melby, Alan (ed.): Proceedings of TKE '02 - Terminology and Knowledge Engineering, INRIA, Frankrig, 2002.

8 1.1 impact printer (202) CHARACTER TRANSFER : impact NOISE: noisy COPY: multiple (204) CHARACTER TRANSFER : impact NOISE : noisy COPY : multiple STRIKING TECHNIQUE: front (205) CHARACTER TRANSFER : impact NOISE : noisy COPY : multiple STRIKING TECHNIQUE :hammer STRIKING TECHNIQUE 1.2 nonimpact printer (203) CHARACTER TRANSFER : nonimpact NOISE : quiet COPY: single dot matrix printer (206) CHARACTER TRANSFER : impact NOISE : noisy COPY : multiple USED ON: microcomputer 1 printer (201) CHARACTER TRANSFER top (200) Figure 5: Part of a concept system for printer types introduced on the basis of the dimension STRIKING TECHNIQUE (no expressions)

9 Impact printers transfer the image onto paper by some type of printing mechanism striking the paper, ribbon, and character together. One technique is front striking in which the printing mechanism that forms the character strikes a ribbon against the paper from the front to form an image. This is similar to the method used on typewriters. The second technique utilizes a hammer striking device. The ribbon and paper are struck against the character from the back by a hammer to form the image on the paper (Figure 5-8 on the next page). Shelly, Cashman, Waggoner: Essential Computer Concepts (1993), pp Madsen, Bodil Nistrup; Hanne Erdman Thomsen & Carl Vikner: ’The project ”Computer-Aided Ontology Structuring”(CAOS)’. In: World Knowledge and Natural Language Analysis. Copenhagen Studies of Language, vol.23, København: Samfundslitteratur, 1999, s.9-38.

10 1.1 impact printer (202) CHARACTER TRANSFER: impact NOISE: noisy COPY: multiple 1.2 nonimpact printer (203) CHARACTER TRANSFER: nonimpact NOISE: quiet COPY: single 1 printer (201) CHARACTER TRANSFER NOISE COPY subdivision criterion dimension feature specification feature value CHARACTER TRANSFER: impact, nonimpact NOISE: noisy, quiet COPY: multiple, single dimension specification delimiting feature

11 intension meaning: precise meaning: vague linguistic sign general linguistic sign term LSP expression LSP concept characteristic superordinate concept subordinate concept concept system concept relation extension entity property systematic position notation type of part of has describes relation type: Figure 1: Part of a concept system for central concepts underlying the database structure of DANTERM CBS

12 intension MEANING: precise MEANING: vague linguistic sign general linguistic sign term LSP expression LSP concept characteristic superordinate concept subordinate concept concept system concept relation extension entity property systematic position notation type of part of has describes relation type: Figure 1: Part of a concept system for central concepts

13 MEANING: precise MEANING: vague linguistic sign general linguistic sign term LSP expression LSP concept Figure 1: Part of a concept system for central concepts examples of feature specifications: Feature: MEANING Values: vague, precise the concepts general linguistic sign and term are distinguished by means of the feature MEANING: a general linguistic sign has a vague meaning, while a term has a precise meaning linguistic sign in general language linguistic sign in LSP

14 MEANING: precise MEANING: vague linguistic sign general linguistic sign term LSP expression LSP concept Figure 1: Part of a concept system for central concepts linguistic sign: combination of an expression and a concept intension characteristic extension entity property concept: defined by means of characteristics, which describe properties of classes of entitites intension: the set of characteristics used to determine the extension of a concept

15 LSP concept characteristic superordinate concept subordinate concept concept system concept relation systematic position notation Figure 1: Part of a concept system for central concepts LSP concepts of a domain may be organized in one or more concept systems a concept has a systematic position in one or more concept systems a concept may be assigned several systematic positions notations in the same concept system when talking about concept relations we refer to the relation between concepts in a given position in a given concept system

16 Data modelling versus conceptual modelling in order to produce a well-functioning database it is necessary to know the conceptual model for the domain underlying the data model (the database structure) ie. you have to be familiar with the central concepts of the domain in which the database is going to function knowledge about the concepts in a domain is rendered by concept characteristics and information about relations between concepts (semantic knowledge) one or more concept systems within a domain form a conceptual model of the domain

17 conceptual modelling (semantic information) information about concepts in the form of concept characteristics and concept relations (information about meaning) data modelling (also referred to as semantic modelling) information about the entity types in the form of attributes and relationships between the entity types (ie. no information about the meaning of the entity types, but only a specification of what kind of information will be given about the entitites represented by the entity types in question) Data modelling versus conceptual modelling

18 Feature specifications versus attributes feature specifications give information on the content of the concept (meaning) they form the basis for a definition of a concept example: the concept term is charactarized by means of feature specifications in the concept system attributes do not give information on the meaning of the entity type they only specify what kind of information will be given about the entities represented by the entity type in question example: the entity type Term in an E/R diagram for a terminology database

19 Mapping between entity types and concepts example: the concepts intension and extension will not be found in an E/R- diagram for a terminology database they are important in the concept model for the understanding of the central concepts within the terminology domain

20 Mapping between entity types and concepts example: a concept system for concepts may comprise the concepts superordinate concept and subordinate concept, but there are no corresponding entity types in the E/R diagram for a termbase

21 intension meaning: precise meaning: vague linguistic sign general linguistic sign term LSP expression LSP concept characteristic superordinate concept subordinate concept concept system concept relation extension entity property systematic position notation type of part of has describes relation type: Figure 1: Part of a concept system for central concepts not included in the data model for a termbase

22 entity relation type: type of part of has describes characteristic linguistic sign general linguistic sign term LSP expression LSP concept superordinate concept subordinate concept concept system concept relation intension extension property systematic position notation MEANING: vague MEANING: precise not in the data model for a termbase

23 Generic relations versus other semantic relations Jørgen Fischer Nilsson : all other relations than ISA relations are relations between entities not concepts Cf. classical terminology = ontological relations (part-whole, temporal, causal) Wüster, Eugen (1985). Einführung in die Allgemeine Terminologielehre und terminologische Lexikographie. The LSP Centre, The Copenhagen School of Economics. 214 pp. (lecture manuscript from )

24 Generic relations versus other semantic relations Wüster, Eugen (1985). Einführung in die Allgemeine Terminologielehre und terminologische Lexikographie. p. 12: Ontologische Beziehungen bestehen zwischen den Individuen, die unter die betreffenden Begriffe fallen p. 9: Logische Beziehungen bestehen im Grad und in der Art der Ähnlichkeit

25 Generic relations versus other semantic relations Madsen, Bodil Nistrup: Terminologi – Principper og Metoder, Gads Forlag 1999: p. 21: Generic relationship … the characteristics of a super-ordinate concept is a proper subset of the characteristics of its sub- ordinate concepts, which means that a sub- ordinate concept has all the characteristics of the super-ordinate concept and at least one further characteristic, which distinguishes it from its co- ordinate concepts.

26 employee secretarymanager top managerarea manager programmer domain specific versus application specific ontologies


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