Temporal Event Map Construction For Event Search Qing Li Department of Computer Science City University of Hong Kong

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Algebra operations devised on TEM  Conclusions and Future Work

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Algebra operations devised on TEM  Conclusions and Future Work

Introduction  Many news articles report events on the WWW  For an event, it may consist of several component events, i.e., episodes There are relationship between component events Some component events are more important than others  For example, “Toyota vehicle recalls”

 What people want from news articles? Not a sole news article, but the events reported by some related news articles Dependent relationships between component events Which component events play important roles in the event evolution or development

 They are interested in the whole picture of an event evolution or development along a time line includes the dependent relationships between component events Event importance in the event evolution or development  Read news is an important way to know what happen Too many news Too many topics  Time consuming job to read news to find what user wants Understand what happens What is important component events What is the relations between component events Temporal event map Provide a convenient way to browse the event evolution

Introduction  Current search engine Keyword search List of web pages Can not provide a map of an event  Necessary and useful to provide temporal event map (TEM)

Previous Work  Qiaozhu Mei and Chengxiang Zhai. Discovering Evolutionary Theme Patterns from Text: An Exploration of Temporal Text Mining. In Proceeding of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, pp ,  Christopher C. Yang, Xiaodong Shi, and Chih-Ping Wei. Tracing the Event Evolution of Terror Attacks from On-Line News. In Proceeding of the ISI  Jiangtao Qiu et al. Timeline Analysis of Web News Events. In Proceeding of the ADMA  Christopher C. Yang, Xiaodong Shi, and Chih-Ping Wei. Discovering Event Evolution Graphs From News Corpora. IEEE Trans. Sys. Man Cyber. Part A  Jin, Peiquan et al. TISE: A Temporal Search Engine for Web Contents. Proceedings of the 2008 Second International Symposium on Intelligent Information Technology Application  A. Feng and J. Allan. Incident threading for news passages. In CIKM ’09: Proceeding of the 18th ACM conference on Information and knowledge management, pages 1307–1316, New York, NY, USA, ACM.

Limitations of Previous Work  Only take time sequence and content similarity between two component events into consideration  Using such two factors are not enough to identify dependent relationships  Do not provide a way to measure event importance so as to identify milestone events that are more interested by most users  Do not provide a convenient way to browse event evolution

Our Work  Formalize the problem of event search  Propose a framework to search events based on users’ queries  Characteristics and contributions of our work

Content dependent relationship - mutual information VS. content similarity Event reference relationship- some news articles of an event may refer to (mention) other events Adopt three kinds of event relationships which are temporal relationship, content dependent relationship and event reference relationship to identify a dependent relationship between two events

 Characteristics and contributions of our work Define some algebra operations to assist user browsing TEM The search results are organized by a temporal event map (TEM) which is a whole picture about an event’s evolution or development along a time line Propose a method to measure event importance degrees so as to rank events based on their importance degrees Experiment results show that our method outperforms baselines in discovering event dependent relationships and ranking events based on event importance

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Algebra operations devised on TEM  Conclusions and Future Work

Event Modeling  Event – reported by documents  Document - A story talking about an event including the happen time, places and content of the event Timestamp Places Content

Event Modeling

 Related document set of an event - a set of documents talking about the event  Life cycle  Begin time the earliest timestamp among all timestamps of related documents of the event  End time latest timestamp among all timestamps of related documents of the event  Place set

Event Modeling  Example: SARS epidemic The life cycle of this event is from November 2002 to May The places of the event includes China, Canada, Singapore and so on. There are many news from the world wide web which reported such an event. We can extract keywords from the set of documents to describe the event such as SARS, flu-like, fever, treatment and so on.

An Example of TEM

Our Work  Input User can search events by time, places and interested content  Output A temporal event map  Event evolution  Fuzzy relations of events  Important events  Some algebra operations

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Algebra operations devised on TEM  Conclusions and Future Work

Steps of Constructing Temporal Event Map For Temporal Event Search  Identify related document set of target event  Event Discovering  Content Dependent Relationship Analysis  Event Reference Relationship Analysis  Event Ranking  Temporal Event Map Construction

Identify related document set of target event  Identify related document set of target event The input information could be considered as the search requirements of the user and corresponds to a target event which satisfy all the requirements. The related document set of the target event can be obtained by a function.

Identify related document set of target event  Special cases of input Partially input - part of (I t, I p, I f ) We consider these input as three kinds of requirements and only take the input requirements into consideration R tf R tpf

Event discovering  For each target event a corresponding to an input I and its related document set Ra, we can detect several sub-vents from Ra.  We do not aim at event detection  Adopt the topic-model based method to detect the events  A function

Content Dependent Relationship Analysis  Event Representation – salient feature vector

Content Dependent Relationship Analysis  Previous works - content similarity  Some keywords in two events are dependent but not exactly matched  Calculating mutual information to measure the dependence between features, and then use an aggregation of all mutual information between features in events

Event Reference Relationship Analysis

Event Ranking  some component events are more important than others  ranking function to rank all the sub-events E1E3 E4 E2 E6 E

Temporal Event Map Construction

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Algebra Operations Devised on TEM  Conclusions and Future Work

Algebra Operations Devised on TEM  Projection Based on Content Dependency Relation  Projection Based on Reference Relation  Projection on Happening Places  Projection on A Time Period  Zoom In  Zoom out

An example of the whole TEM about the event “2011 Japan Earthquake”

An example of Projection Based on Content Dependency Relation about the event “2011 Japan Earthquake”

An example of Projection Based on Reference Relation on TEM about the event “2011 Japan Earthquake”

Projection on Happening Places on TEM about the event “2011 Japan Earthquake”

Projection on Time Period on TEM about the event “2011 Japan Earthquake”

Zoom In Operation

Sub-TEM of Zoom In “The earthquake and tsunami caused a number of nuclear accidents.”

Outline  Introduction  Problem Formulation  Temporal Event Map Construction For Event Search  Evaluation  Conclusions and Future Work

Conclusion and future work  Conclusion Formulate the temporal event search problem Propose a framework to search events according to users’ queries. Define three kinds of relationships and use them to identify event dependent relationships The search results is represented by a temporal event map (TEM) A method to measure event importance degree  Future work Try to handle different kinds of input and discuss their scalability Discover the important and burst periods of an event To achieve personalization News recommendation to let user know the event more clearly and completely

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