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ISCRAM 2013: Context Ontology for Humanitarian Assistance in Crisis Response
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ISCRAM 2013: Context Ontology for Humanitarian Assistance in Crisis Response

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Author: Satria Hutomo Jihan, Aviv Segev

Author: Satria Hutomo Jihan, Aviv Segev
KAIST

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  • 1. Baldwin, B. and Carpenter, B. (2003) LingPipe, available from World Wide Web: http://alias-i.com/lingpipe/2. Zhu, J., Uren, V. and Motta, E. (2005) ESpotter: Adaptive named entity recognition for web browsing, Professional Knowledge Management, 3782, 518-529.3. Jenny Rose Finkel, TrondGrenager, and Christopher Manning. 2005. Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling. Proceedings of the 43nd Annual Meeting of the Association for Computational Linguistics (ACL 2005), pp. 363-370
  • http://wwhgd.org/content/human-security-taxonomy
  • 6. http://wwhgd.org/content/human-security-taxonomy

ISCRAM 2013: Context Ontology for Humanitarian Assistance in Crisis Response Presentation Transcript

  • 1. Context Ontology for Humanitarian Assistance in Crisis Response Satria Hutomo Jihan, Aviv Segev 1
  • 2. Outline 2 Background Solution Offered System Description Experiment & Result Conclusion and Future Direction
  • 3. My sister and her husband are amongst 1,000 British tourists that have been abandoned by their travel companies, and are currently in a car park somewhere in Cancun. These people have no running water, no toilets, no food, no clean clothes, and have no idea what is happening to them, and when they are going to be able to come home. What is being done to help them? Adam Norris, Chichester A sample report - HurricaneWilma Source: http://news.bbc.co.uk 28 October 2005 3
  • 4. Challenges in Crisis Response: 1. Numerous crisis data available online BLOGS NEWS Social Media Background Other data sources 4
  • 5. Hygiene Challenges in Crisis Response: 1. Numerous crisis data available online 2. Variety of the help request Background Other expressions of help request Food 5
  • 6. Challenges in Crisis Response: 1. Numerous crisis data available online 2. Variety of the help request 3. Limited time to decide how to response the crisis Decision Makers Background 6
  • 7. Solution Proposed • To build knowledge representation system for the rapid response • Provide the necessary info & recommendation actions for decision makers to respond 7
  • 8. RelatedWorks • Di Maio (2007) addresses open ontology methodology for open source emergency response system. • Li et al. (2008) propose a practical emergency response workflow and emergency response ontology architecture. • Truptil et al. (2008) develops meta-model of crisis situations by using ontological links to integrate and support interoperability of heterogeneous information systems • Fan and Zlatanova (2011) explore the semantic interoperability of the terms and spatial information to be used by different emergency response communities However, aforementioned previous works did not explore raw data as source data nor offer a solution between crisis needs and humanitarian assistance. 8
  • 9. Main Contributions • The construction of generic Crisis Identification & Response Ontology using expert resources • Extracting ontological crisis concepts from online textual data • Inference logic rules for inferring recommendations based on humanitarian standard. 9
  • 10. System Description
  • 11. Context Ontology For Crisis • Ontology is an explicit and formal specification of a conceptualization (Gruber 1993) • Context ontology which cover crisis concepts and its relations • Context ontology includes Crisis Identification & Crisis Response sub-ontology • Context ontology adapts: 1. actual & dynamic data as its local ontology and 2. Static and predetermined data as its global ontology 11
  • 12. Context Ontology Framework Documents NER Metadata Extraction Context Recognition Preprocessing stage Inferencing stage Concept mapping Logic rules Context Ontology Humanitarian Response Recommend ation 12
  • 13. Preprocessing stage • Named Entity Recognition (NER) NER refers to the extraction process of words and strings of text within documents that represent discrete concepts, such as names and place • Available NER tools LingPipe, Esplitter, Stanford NER • Example My sister and her husband are amongst 1,000 British tourists that have been abandoned by their travel companies, and are currently in a car park somewhere in <LOCATION>Cancun</LOCATION> 13
  • 14. Preprocessing stage • Context Recognition Process 1. Terms Extraction using C-Value/NC-Value algorithm C-Value/NC-Value algorithm is an efficient domain-independent multi-word term recognition method which combines linguistic and statistical knowledge 2. Context Recognition algorithm Context Recognition algorithm uses internet as a knowledge base Major Processes: - Collecting Data, - Selecting context for each text, - Ranking contexts and - Declaring current context 3. Metadata Extraction - Extracting timestamp metadata from web documents. 14
  • 15. Building Context Ontology • Expert Sources: 1. The Sphere Handbook One of the most widely known and internationally recognized sets of common principles and universal minimum standards in humanitarian response areas. 2. Dbpedia The community-driven data pool that structures data from Wikipedia 15
  • 16. Context Ontology • Building Ontological Structure 16
  • 17. Context Ontology • Building Ontological Structure 17
  • 18. The Ontology Structure Crisis Identification Ontology 18 LOCAL GLOBAL
  • 19. Crisis Response Ontology The Ontology Structure 19
  • 20. • Mapping extracted terms from documents into Crisis Identification Ontology di = extracted terms of a document D = {d1, d2, …, di} = a set of textual descriptors representing a documents C={c1,c2, …, cn} = a set of concepts with R as their associated relation xp = contexts of di which describe all possible scenarios. O= <C,R> = a simplified representation of an ontology Inferencing stage 20
  • 21. Mapping extracted terms from documents into Crisis Identification Ontology Examples: d1 = Running Water x1 = sound, Tap Water Tap Water is an instance of Class “Water Supply” Water Supply Inferencing stage 21
  • 22. Logic Rules The functions of logic rules a. to enable the system to derive recommendation statements from a set of premises/ facts b. to provide explanations of how recommendations are made, c. to bridge between the Crisis Identification Ontology and the Crisis Response Ontology. 22
  • 23. Logic Rules Examples: @prefix co:<http://www.semanticweb.org/ontologies/context-ontologies#> @prefix rdf <http://www.w3.org/1999/02/22-rdf-syntax-ns#> co:ev1000001 rdf:type co:Event. co:ev1000001 co:impact co:water co:ev1000001 co:impact co:food co:ev1000001 co:place co:cancun co:ev1000002 rdf:type co:Event co:ev1000002 co:impact co:water co:ev1000002 co:impact co:shelter co:ev1000002 co:place co:cancun co:ev1000003 rdf:type co:Event co:ev1000003 co:impact co:water co:ev1000003 co:place co:cozumel co:waterSupply rdf:type co:Response co:WaterSupply co:possibleAction co:SphereBook_page_98 Recommendation Action when Threshold > 1 Place : Cancun Recommendation: SphereBook page 98 23
  • 24. Logic Rules Examples: @prefix co:<http://www.semanticweb.org/ontologies/context-ontologies#> @prefix rdf <http://www.w3.org/1999/02/22-rdf-syntax-ns#> co:ev1000001 rdf:type co:Event. co:ev1000001 co:impact co:water co:ev1000001 co:impact co:food co:ev1000001 co:place co:cancun co:ev1000002 rdf:type co:Event co:ev1000002 co:impact co:water co:ev1000002 co:impact co:shelter co:ev1000002 co:place co:cancun co:ev1000003 rdf:type co:Event co:ev1000003 co:impact co:water co:ev1000003 co:place co:cozumel co:waterSupply rdf:type co:Response co:WaterSupply co:possibleAction co:SphereBook_page_98 Recommendation Action when Threshold > 1 Place : Cancun Recommendation: SphereBook page 98 Source: Spherebook page 93 24
  • 25. Logic Rules Examples: @prefix co:<http://www.semanticweb.org/ontologies/context-ontologies#> @prefix rdf <http://www.w3.org/1999/02/22-rdf-syntax-ns#> @prefix dbpedia-owl <http://dbpedia.org/ontology/> co:ev1000004 rdf:type co:Event. co:ev1000004 co:impact co:water co:ev1000004 co:impact co:food co:ev1000004 co:place co:cancun co:waterSupply rdf:type co:Response co:WaterSupply co:possibleAction co:SphereBook_page_98 co:SphereBook_page_98 co:basicWaterNeed 15 co:cancun dbpedia-owl:populationTotal 628306 Recommendation Action Place : Cancun Recommendation: WaterNeed 9,424,590 litres per day 25
  • 26. Recommendation actions for debris removal Source: Sphere handbook page 276 26
  • 27. Distribution Map of Humanitarian Needs 27
  • 28. Experiment & Result
  • 29. Experiments • Data sources Crisis Sample: Wilma Hurricane Crisis Period : 15 October- 26 October 2005 Datasets : 125 raw data from blogs 10 place names are selected for further analysis 29
  • 30. Experiments • Entities Cloth Debris Food Lighting Shelter Toilet Water 7 Type of Needs Cancun Holbox Cozumel Chiquila Miami Merida Solferino Mexico City New Orleans Fort Myers 10 Location names 30
  • 31. Experiment Results Recall Precision F-Score 0.897436 0.530303 0.666667 Context Ontology Recall precision F-Score 0.74359 0.483333 0.585859 KeywordsMatching 31
  • 32. Experiment Results 32
  • 33. Conclusion • Context Ontology may able to transform the textual crisis data into the sets of response recommendations for decision makers • By using context recognition process,Context Ontology may capture variety of help request types from actual crisis report. • Context Ontology can adapt update info about crisis • A set of logic rules is used to automatically generate the output, humanitarian response recommendations, and to provide the decision maker with the explanations of how the system infers the recommended actions. • Context ontology works better than keyword query matching. 33
  • 34. Future Directions • The concepts in Context Ontology can be expanded by including others expert data sources, such as Infinitum Humanitarian Systems Human Security Taxonomy, IASC Guidelines, etc. • Context Ontology may include logic rules for the hierarchical chain of command of the emergency response team • Context Ontology may be expanded into multilingual ontology by using dictionary to translate the concepts from one language to another language. 34
  • 35. Q&A 35
  • 36. 36 THANKS YOU becauseOf
  • 37. References 37
  • 38. 38 Experiment Results No Place 1st Priority 2nd Priority 3rd Priority 4th Priority 1 Cancun Water , Shelter Food Lighting Cloth, Toilet 2 Holbox Water, Ligthing Food, Shelter 3 Solferino Lighting Debris Removal, Water, Shelter 4 Cozumel Debris Removal, Lighting Food 5 Ciquila Shelter, Water 6 Fort Myers Water Clothing 7 Cozumel Debris Removal, Shelter 8 Miami Water Debris Removal, Food 9 Merida Shelter, Water 10 New Orleans Debris Removal, Shelter
  • 39. 39 Experiment Results