Controlled Language with Terminology-Ontologies

Transkript

1 Controlled Language with Terminology-Ontologies How to automatically

2 Text production professional considerations text function precision unique background experience culture knowledge (un)awareness and presuppositions target-group considerations experts or laymen? understandability motivation and interest Seite 2

3 Text perception reader s task: hermeneutically interpret the text and adapt it to his own unique circumstances consideration of author traits experts or laymen? informativeness or grandstanding pleas and appeals professional considerations text function precision unique background experience culture knowledge (un)awareness and presuppositions Seite 3

4 Text perception the reader s task recapitulate the propositional structure recapitulate the argumentative structure add implicit information look up unfamiliar words relate information restructure the text update knowledge Seite 4

5 Text production vs. text perception unbalanced author/reader ratio unbalanced ratio of author s and reader s tasks cost-effectiveness for the reader is not taken into account reader may have only limited access to the text worst-case-scenario: reader loses interest Seite 5

6 The readers and their backgrounds group of readers group of readers Seite 6

7 Linguistic diversity of groups of readers common of computer science medical of AI of graph theory health care surgical Seite 8

8 Linguistic diversity of groups of readers common of computer science medical of AI of graph theory health care surgical Seite 9

9 Individual varieties Variety: Jacque Derrida.... différance homophon Variety: common French.... différence Seite 10

10 Linguistic diversity of groups Example Do we have a virus? crime illness malfunction human host cell vaccination computer virus microorganism software animal code plant Seite 11

11 Linguistic diversity of groups event sequences conditions protected entities protective measurements safety/ security environment safety measuring Seite 12

12 Explicitness Sign model The trilateral sign model Lemma (designation) Definition (context ) Variety (special ) Seite 13

13 Explicitness Semantic relations Various relations, e.g. synonymy homonymy polysemy translations false friends hierarchical relations part/whole super-/subordination sequential relation chronologies functional relations risk of confusion Seite 14

14 Explicitness Attribute hierarchies Mars planet distance earth distance max. earth distance min. length 401,1768 Mio. km 55,6512 Mio. km Seite 15

15 Implications for authors Authors can be assisted by tools in finding the designation they are supposed to use finding the designation a reader better understands anticipate misunderstandings readers might have include information they only implied Seite 16

16 Tool Support iglos web application relating of special signs individual and collective terminology lists visual terminology management with graphs attribute hierarchies status and commentation updating terminologies stable, versioned terminologies terminology evolution Seite 17

17 Tool support Word Plug In Seite 18

18 Tool support Word Plug In Creation of document terminologies based on project specific/ other existing terminologies availability of terminological explanations during text production and perception finding similar terms linking terminology to text passages Seite 19

19 Tool support RIF-reading plug in Plug in for a Requirements Engineering Tool processing of RIF-documents sentence for sentence iterative checks adressed problems vague descriptions lexical ambiguities implicit information Seite 20

20 Review of Requirements Example

21 Review of Requirements Example Originalanforderung Beispiel für eine Anforderungsüberarbeitung Überarbeitete Anforderungen 1. Die Zuordnung eines Verkehrszeichens zu einer Parkfläche muss bekannt sein. Die Zuordnung eines Verkehrszeichens zu einer Straße muss bekannt sein 2. Die Zuordnung eines Verkehrszeichens zu einem Radweg muss bekannt sein. 3. Die Zuordnung eines Verkehrszeichens zu einem Gehweg muss bekannt sein. 4. Die Zuordnung eines Verkehrszeichens zu einer Fahrbahn muss bekannt sein.

22 Sum

23 Thank you.