Temporal Event Map Construction For Event
Search
Qing LiDepartment 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 2009-2010 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 providetemporal 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. 198-207, 2005.
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 2006.
Jiangtao Qiu et al. Timeline Analysis of Web News Events. In Proceeding of the ADMA 2008.
Christopher C. Yang, Xiaodong Shi, and Chih-Ping Wei. Discovering Event Evolution Graphs From News Corpora. IEEE Trans. Sys. Man Cyber. Part A 2009.
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, 2009. 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
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 2006.
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.
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 (It, Ip, If) We consider these input as three kinds of requirements and
only take the input requirements into consideration.
11 22 44 55
Rtf
33 11 22 44 55
Rtpf
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 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 Ranking some component events are more important than others ranking function to rank all the sub-events
E1 E3
E4E2 E6
E5
0.82
0.52 0.8
0.97
0.66
0.6
0.88
0.89
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 Projection Based on Content Dependency Relation about the event “2011 Japan Earthquake”
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