Automated Harvesting of Loss and Damage Information from Open Sources
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1 Automated Harvesting of Loss and Damage Information from Open Sources Mao Kezhi School of Electrical and Electronic Engineering Nanyang Technological University 04 August 2017 Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
2 Outline 1 Introduction 2 Automatic Knowledge Extraction from Open Sources 3 Outstanding Merits of AKEOS 4 Conclusion and Future Work Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
3 Introduction (i) Harvesting Loss and Damage Information from Open Sources Events such as terrorism attack, earthquake, flood, typhoon etc often happen in the region or other part of the world; News agencies, big and small, have tens of thousands of journalists around the world, who publish their first-hand information of events online at the first time; Online news serves as a major source of information nowadays. (ii) Automated Harvesting Each news article may only report part of the facts of an event; Reading a large number of articles to get a comprehensive understanding of an event is laborious and time consuming; An automated system is desired to automatically extract key information of an events from the open sources. Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
4 Architecture of AKEOS Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
5 Automatic Knowledge Extraction from Open Sources (i) Input to the System The name of an event: for example, 2011 Tohoku earthquake, 2015 Paris attack; An image, for example Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
6 (ii) Relevant Documents Search Scan websites of news agencies and governments etc; Crawl the text from the relevant web links. (iii) Knowledge Extraction Knowledge extraction outcome is the key information of an event; For example, 2011 Tohoku earthquake: Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
7 (iv) Knowledge Visualization Knowledge graph of 2011 Tohoku earthquake Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
8 (iv) Knowledge Visualization Knowledge graph of 2015 Nepal earthquake Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
9 (iv) Knowledge Visualization Knowledge graph of 2015 Bangkok attack Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
10 (iv) Knowledge Visualization Knowledge graph of 2016 Jakarta attack Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
11 (iv) Knowledge Visualization Knowledge graph of 2015 Paris attack Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
12 (v) Knowledge Base Generation Consolidate knowledge extracted the same type of events, for example, all earthquakes happened in the past; Store the consolidated knowledge in relational database; Perform query to the database to find, for example, earthquakes with magnitude higher than 7, earthquakes killing more than 1000, earthquakes in Japan between 2001 and 2017 etc; Explore relations between entities, for example, the relation between magnitude and tsunami etc; Discover hidden patterns. Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
13 Automatic Knowledge Extraction from Open Sources (v) Knowledge Base Generation Knowledge base of earthquakes Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
14 (vi) Bayesian Network Inference Engine Construction The knowledge base can used to construct Bayesian Network inference engine for predictive and diagnostic reasoning; This involves identification of causal relation between entities; This also involves conditional probability estimation based on the knowledge base. Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
15 (vi) Bayesian Network Inference Engine Construction Bayesian network of earthquake Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
16 Outstanding Merits of AKEOS (i) AKEOS is Task-independent Can be used to extract knowledge of any types of events; Automatically determine what key information to extract, without the need of human intervention. (ii) AKEOS has evolving learning capability Introduce a time dimension for developing events; Facts of an event update over time; Public attentions change overtime; Evolving learning makes the information extracted is up to date. (iii) AKEOS is an end-to-end system With query words as the input and inference engine as the end product Unstructured text converted to structure data for readily use of AI Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
17 Conclusion and future work Conclusion AKEOS is a knowledge extraction system for general purpose; AKEOS has good accuracy. Future Work AKEOS can be tailored for automated harvesting of loss and damage information; Improved performance is expected. Mao Kezhi (EEE-NTU) AKEOS 04 August / 17
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