Uncertain Concept Graph for Social Web Summarization during Emergencies - CPS18
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Web has empowered emergency services to enhance operations by collecting real-time information about incidents from diverse data sources such as social media and Web. However, the high volume of unstructured data with varying degrees of veracity challenges the timely extraction and integration of relevant information to summarize the current situation. This research proposes a novel idea of building Uncertain Concept Graphs. It was presented at 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems, CPS Week 2018. Purohit, H., Nannapaneni, S., Dubey, A., Karuna, P., & Biswas, G. (2018). Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph. 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering. Cyber-Physical Systems (CPS Week).
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Uncertain Concept Graph for Social Web Summarization during Emergencies - CPS18
- 1. Structured Summarization of Social Web for Smart Emergency Services by Uncertain Concept Graph Contact: hpurohit@gmu.edu / @hemant_pt Hemant Purohit, Prakruthi Karuna Dept. of Information Sciences & Technology SCOPE-GCTC: 3rd Int’l Workshop on Science of Smart City Operations & Platforms Engineering Cyber-Physical Systems (CPS) Week 2018 Apr 10, 2018 Saideep Nannapaneni Dept. of Industrial, Systems & Manufacturing Engineering Abhishek Dubey, Gautam Biswas Dept. of Electrical Engineering & Computer Science
- 2. Outline ¨ Motivation n Disaster context n Needs of emergency responders n Social Media: big ‘citizen sensor’ data n Opportunity to structure social data into actionable summaries ¨ Position: Uncertain Concept Graph (UCG) for structured summarization n Novel knowledge representation n Contrast to Dynamic Bayesian Networks n Graph design: nodes and edges ¨ Summarizing Needs via UCG Inference n Source of uncertainty n Optimization problem formulation ¨ Future Work n Experimentation validation 2
- 3. Outline ¨ Motivation n Disaster context n Needs of emergency responders n Social Media: big ‘citizen sensor’ data n Opportunity to structure social data into actionable summaries ¨ Position: Uncertain Concept Graph (UCG) for structured summarization n Novel Knowledge Representation n Contrast to Dynamic Bayesian Networks n Graph design: Nodes and Edges ¨ Summarizing Needs via UCG Inference n Source of uncertainty n Optimization problem formulation ¨ Future Work n Experimentation validation 3
- 4. Disaster Context: High Dynamicity 4 ¨ Varying resource requirements ¨ Situational Decision- making
- 5. Disaster Context: Scale of (Dynamic) Management 5 ¨ Dynamic and Large-scale Resource Management
- 6. Disaster Context: Dynamic Emergency Service Needs Fundamental Information Requirement: WHO needs WHAT, WHERE & WHEN Image sources: http://www.kolotv.com/content/news/Learning-from-Hurricane-Harvey-442420813.html https://twitter.com/abc13houston/status/936188111293440000 Event Response Coordination 6
- 7. Disaster Context: Opportunity to Exploit 21st Century Information Exchange 7
- 8. Big Social & Web Data: ‘Citizen Sensor’ Data Unstructured, Unconstrained Language Data • Ambiguous & informal text • Diverse sources Bi-directional (BY people, TO people) Web 3.0 & Social media 8
- 9. Social Data Challenge: Need to Extract & Summarize Relevant Information from Streams ¨ Relevance challenge ¤ High-noise ¤ Low-signal 9
- 10. Outline ¨ Motivation n Disaster context n Needs of emergency responders n Social Media: big ‘citizen sensor’ data n Opportunity to structure social data into actionable summaries ¨ Position: Uncertain Concept Graph (UCG) for structured summarization n Novel Knowledge Representation n Contrast to Dynamic Bayesian Networks n Graph design: Nodes and Edges ¨ Summarizing Needs via UCG Inference n Source of uncertainty n Optimization problem formulation ¨ Future Work n Experimentation validation 10
- 11. Need for Novel Knowledge Representation: Structured Summarization 11 ¨ Goal: ¤ Summarize heterogeneous data stream about an evolving event in a structured form that can feed into decision support systems over time ¨ Required: ¤ Representation and integration of streaming information from heterogeneous sources
- 12. Need for novel Knowledge Representation: Structured Summarization ¨ Existing methods for summarizing social and Web streams: Event Detection, IR, Text Summarization [Zhao, Mitra, & Chen, AAAI-2007; Chakrabarti & Punera, ICWSM-2011; Aggarwal & Subbian, SDM-2012; Chua & Asur, ICWSM-2013; Tran, WWW-2013, Feng et al., ICDE-2015, etc.] ¤ Limitation: focused on easing human comprehension and not machine comprehension n e.g., summarizing top stories ¨ Existing Structured Models: Dynamic Bayesian Networks ¤ Limitation: requires a static system but here evolving situation 12
- 13. DISASTER Event Proposed: Identify & Structure Concepts in an Uncertain Graph - Needs, Actions, Locations CITIZEN Sensors RESPONSE Organizations x RELEVANT RESOURCE INFORMATION 13
- 14. Uncertain Concept Graph: Definition 14 ¨ A Probabilistic Graphical Modeling approach ¨ Nodes ¤ Events ¤ Regions ¤ Resources ¤ Information ¨ Edges ¤ Informing ¤ Inferred ¤ Allocated ¨ Edge weights ¤ Relative importance ¤ Derived from text mining analysis ¨ UCG structure can dynamically change with time
- 15. Uncertain Concept Graph: Example 15 An illustration of a disaster situation over time trajectory with unsteady nodes and edges
- 17. Uncertain Concept Graph: Edges 17 ¨ Types: ¤ Informing: Information Source Node to Incident Node and Incident Node to Region Node ¤ Inferred: Region Node to Allocated Node ¤ Allocated: Service Resource Node to Region Node ¨ Edge Weights: ¤ rely on the text mining models to weigh edges from Source to Incident Nodes ¤ propose a measure for edge relationship between a region node Rq and a resource type Sa based on the incident reports sourced from information sources Ik as: Informing edge Inferred edge
- 18. Outline ¨ Motivation n Disaster context n Needs of emergency responders n Social Media: big ‘citizen sensor’ data n Opportunity to structure social data into actionable summaries ¨ Position: Uncertain Concept Graph (UCG) for structured summarization n Novel Knowledge Representation n Contrast to Dynamic Bayesian Networks n Graph design: Nodes and Edges ¨ Summarizing Needs via UCG Inference n Source of uncertainty n Optimization problem formulation ¨ Future Work n Experimentation validation 18
- 19. Avoidable damage increases with the time lag between first incident reporting and corresponding service allocation at an exponential rate, and defined as: Summarizing Needs via UCG Inference: Resource allocation: Minimizing Damage at a Region 19 ¨ Divide the total damage at a region into two types ¤ avoidable damage(DA) n losses due to the unavailability of emergency resources n Focus for data analytics via UCG ¤ unavoidable damage(DU) n losses due to the disaster impact that cannot be controlled
- 20. Summarizing Needs via UCG Inference: Optimization for Minimizing Avoidable Damage and travel costs 20 ¨ The resources are required for dispatching to the regions wherever their services are the most needed at time Tm. This is achieved by solving the following optimization problem for the minimum utility as: Estimated damage due to the disaster Costs due to moving resources
- 21. Summarizing Needs via UCG Inference: Capturing the sources of uncertainty 21 ¨ Real-time resource decision-making needs to be robust ¨ Incorrect decisions (Wrong resources to wrong places) à Financial and human losses ¨ Robustness à capture all uncertainty sources that influence decisions ¤ Uncertainty in text mining analytics (Type of emergency, resources required and regions) ¤ Uncertainty in travel time and costs ¤ Uncertainty in the damage function (Need to be adjusted based on the region and surroundings such as vicinity to electric substations and lakes) Objective function becomes stochastic, we minimize its expectation value
- 22. Outline ¨ Motivation n Disaster context n Needs of emergency responders n Social Media: big ‘citizen sensor’ data n Opportunity to structure social data into actionable summaries ¨ Position: Uncertain Concept Graph (UCG) for structured summarization n Novel Knowledge Representation n Contrast to Dynamic Bayesian Networks n Graph design: Nodes and Edges ¨ Summarizing Needs via UCG Edge Inference n Source of uncertainty n Optimization problem formulation ¨ Future Work n Experimental validation 22
- 23. Future Work 23 ¨ Validating optimization solution ¤ Analysis to infer resource allocation sequentially at time interval Tm based on information from the last optimization at Tm-1 as well as all the new information that was obtained in the time interval {Tm−1,Tm}. ¤ The optimization analysis results into UCGTm state that represents the structured summarization of the dynamic disaster context at time Tm. ¨ Experimental validation of the model ¤ Data: 2017 hurricane season events
- 24. Conclusion ¨ Social ‘citizen sensor’ data streams have valuable information for Smart City Services (even beyond emergency services!) ¨ Uncertain Concept Graph (UCG) provides a promising structured representation to organize relevant information from social data streams. ¨ Optimization solution for correct inferences of UCG edges at any time can provide structured summaries, which could be fed into existing decision support systems. 24
- 25. TWITTER: @hemant_pt MAIL: hpurohit@gmu.edu PAPER LINK: http://ist.gmu.edu/~hpurohit/humanitarian- informatics-lab/papers/uncertain-concept-graph-cps18.pdf Acknowledgement: Respective image sources and grant sponsors: Questions? 25 Research partially supported by NSF IIS-1657379 & CNS-1640624, and Vanderbilt Initiative for Smart-City Operations Research (VISOR)