Computational Ontology for Knowledge Representation in Disaster Management

1. Computational Ontology for Knowledge Representation in Disaster Management Prof. and Dean Feng-Tyan LIN Collage of Planning and Design National Cheng Kung University ftlin@mail.ncku.edu.tw

2. Outline • Motivation • Basic concepts • Methodology • Results • Conclusion

3. Climate Change Adaptation and Disaster Management in Taiwan Results Conclusion Motivation Basics Methodology • The impact of climate change, particularly in disaster, is very serious. • Taiwan has been implementing disaster management for decades. • Taiwan is currently encouraging local governments to make their own climate change adaptation strategy based on the central government’s version.

4. Results Conclusion Motivation Basics Methodology Issues • We encountered confusing expressions and interpretations from different researchers, experts, journalists, government officials, and people in general. • NCKU = National Cheng Kung University • Tainan City (after 2010) = Tainan City + Tainan County (before 2010) • Planning ? Management?

5. Land Management ! Planning ! Planning ?

6. Motivation Basics Methodology Results Conclusion Research Purpose • Building an Ontology in Disaster Management (ODM) based on Taiwan’s experience • Identifying key mechanisms for knowledge representation and manipulation • Reaching commonly accepted definition concerning terminology in disaster management planning • Proposing a construction process, including design, implementation, adjustment, and application • and web based thesaurus/dictionary for spatial planners

7. Motivation Basics Methodology Results Conclusion Information explosion • Large amounts of information are circulated through the network quickly. While it’s easy to access, confusion and misunderstanding are simultaneouslyincreasing. • Knowledge and its implication are also dynamic in terms of transforming, evolving, reforming.

8. Motivation Basics Methodology Results Conclusion Ontology(in computer science) • Ontology has been employed by many researchers to formallyrepresent knowledge network, facilitate information sharing, and support interoperability among different systems. • Ontology can act as a knowledge base which is continuously evolving.

9. Ontological Network Motivation Basics Methodology Results Conclusion • Knowledge network consists of concepts, relational terms, and relations. • concepts: mainly terms, denoted by • relational terms: mainly verbs and prepositions, denoted by • is-a, include • part-of, aspect • Order • synonym, antonym, translated terms

10. Motivation Basics Methodology Results Conclusion • relations: links, denoted by , between concepts and relational terms • Two ends of relation are concepts and relational terms. • Excepting relation of ordering, concepts and relational terms are linked in turn. • A new generation of terminology dictionary

11. Motivation Basics Methodology Results Conclusion

12. Motivation Basics Methodology Results Conclusion

13. Ontology Motivation Basics Methodology Results Conclusion • Ontology can function as • a dictionary: a collection of terms and their commonly accepted definitions • a lexical framework: sharing information between different communities(Smith, 2003). • Key issue in Web 3.0 • Web 1.0 : content provider • Web 2.0 : platform provider • Web 3.0 : knowledge organizer

14. Construction Process of ODM Motivation Basics Methodology Results Conclusion • Sentence Collection • Parsing sentences into terms • Building prototypical ontology • Implementation

15. 1. Sentence Collection Motivation Basics Methodology Results Conclusion • A bunch of representative sentences was firstly collected from books, conference proceedings, and meeting minutes concerning climate change adaptation planning. • 2. Parsing sentences into terms • Key nouns and verbs, which described concepts and their relationships respectively, were extracted from collected sentences. • Segmenter With Termlist software(ICTI, Academia Sinica)

16. 3. Building prototypical ontology Motivation Basics Methodology Conclusion Results • A prototypical ontology was built based on the extracted concepts (nouns) and relationships (verbs), which were classified into synonym, hypernym, hyponym, part meronum, etc. • Several tools and programming language were employed, including yEd graph editor, Unified Modeling Language (UML), Protégé 4.2 software, Ontology Web Language (OWL), and the Webpage service tools • 4. Implementation • A web site system with the capability of editing and browsing the prototypical ontology will be developed. • Graphical editors will be employed since it provides ontology developers a visual and intuitive interface. • Concepts (nouns) and relations (verbs) will be represented as graphs by nodes and links respectively.

17. Conceptual process of ontology-based knowledge management system Motivation Basics Methodology Results Conclusion

18. Disaster management Climate Change Adaptation http://140.116.41.55/PlanningWordNets/Index.php

19. General Concept Level of CEPD Motivation Basics Methodology Results Conclusion

20. Instance Level of Pingtung County Motivation Basics Methodology Results Conclusion • Specific Terms Level

21. Instance Level of Chiayi City CCA Motivation Basics Methodology Results Conclusion

22. Comparison Motivation Basics Methodology Results Conclusion • It helps to show the lack in plan making. The climate change adaptation plan should have a continually developing process, in order to make the policy conform to the needs of the local city. The present structure of Chiayi City’s project still has a great lack of this. • Replicateand shiftingconcepts were found during this process, which are common in conversation and communication, can be captured by appropriate representations in general concepts level and specific terms level.

23. Summary Motivation Basics Methodology Results Conclusion • Ontology is a promising technique to represent knowledge structure in climate change adaptation planning for mutual understanding and comparison. • A process of constructing planning ontology was suggested and tested in two local governments. • Not only experienced planners but also beginners, students, journalists, experts in other knowledge domains, and the public will also be able to get benefit from the formal, clear, and rigid description of planning terms.

24. Further study Motivation Basics Methodology Results Conclusion • Using the first order logic to represent a huge knowledge information system. More complicate planning issues will be able to be handled, detected, and resolved by adopting the technology of expert systems and artificial intelligent • A web version of planning domain ontology will spread the benefit even further and faster. Communication between human-machine and machine-machine is also possible.

25. Thanks for your Attention