Temporal Web Dynamics: Implications from Search Perspective

Temporal Web Dynamics
Implications from Search
Perspective
Speaker: Nattiya Kanhabua
Advanced Methods for IR Course
L3S Research Center, University of Hannover
26 June 2014

Outline
• Temporal Web Dynamics
• Research Problems
– Temporal Information Extraction
– Temporal Query Analysis
– Time-aware Retrieval and Ranking
• Application to Temporal Search

Temporal Web Dynamics
• Web is changing over time in many aspects:
– Size: web pages are added/deleted all the time
– Content: web pages are edited/modified
– Query: users’ information needs changes
[Ke et al., CN 2006; Risvik et al., CN 2002]
[Dumais, SIAM-SDM 2012; WebDyn 2010]

2000
First billion-URL index
The world’s largest!
≈5000 PCs in clusters!2004
Index grows to
4.2 billion pages
1995 2012
2008
Google counts
1 trillion
unique URLs
Web and Index Sizes
2009
TBs or PBs of data/index
Tens of thousands of PCs
http://www.worldwidewebsize.com/
Impacts: crawling, indexing, and caching

Content Dynamics
• WayBack Machine
– Web archive search by the Internet Archive

1998
2006
Content Dynamics
2012Impacts: document representation and retrieval

Query Dynamics
• Search queries exhibit temporal patterns
– Spikes or seasonality
Impacts: search intent and query representation
http://www.google.com/insights/search

Temporal Query Examples
• A temporal query consists of:
– Query keywords
– Temporal expressions
• A document consists of:
– Terms, i.e., bag-of-words
– Publication time and temporal expressions
[Berberich et al., ECIR 2010]

Query/Document Matching
query
Temporal
Web
Determining
Search Intent
Term: {Germany, World, Cup}
Time: {06/2006, 07/2006}
D2006
Retrieved results
matching
Time-sensitive
queries
Semantic
Annotation
Annotated
documents Term: {w1, w2, …, wn}
Time: {PubTime(di), ContentTime(di)}

Temporal Information
Extraction

Two Time Aspects
Two time dimensions
1. Publication or modified time
2. Content or event time
content time
publication time

Problem Statements
• Difficult to find the trustworthy time for web documents
– Time gap between crawling and indexing
– Decentralization and relocation of web documents
– No standard metadata for time/date
Document Dating
Let’s me see…
This document is
probably
written in 850 A.C.
with 95% confidence.
I found a bible-like
document. But I have
no idea when it was
created?
“ For a given document with uncertain
timestamp, can the contents be used to
determine the timestamp with a sufficiently
high confidence? ”

Probabilistic Approach
Timestamp Word
1999 tsunami
1999 Japan
1999 tidal wave
2004 tsunami
2004 Thailand
2004 earthquake
Temporal Language Models
tsunami
Thailand
A non-timestamped
document
Similarity Scores
Score(1999) = 1
Score(2004) = 1 + 1 = 2 Most likely timestamp is 2004
Temporal Language
Models
• Based on the statistic usage
of words over time
• Compare each word of a
non-timestamped document
with a reference corpus
• Tentative timestamp -- a
time partition mostly
overlaps in word usage
[de Jong et al., AHC 2005; Kanhabua et al., ECDL 2008]
Freq
1
1
1
1
1
1

Extracting Content Time
• How to determine relevant temporal
expressions tagged in a document?
– Not all temporal expressions associated to an event
are equally relevant
Reported by World Health Organization (WHO) on
29 July 2012 about an ongoing Ebola outbreak
in Uganda since the beginning of July 2012

Approach
• The task of identifying relevant time is
regarded as a classification problem
– Two classes: (1) relevant and (2) irrelevant
• Definition: relevant if overlaps the starting,
ending or ongoing time of the event
• Machine learning: three classes of features
– Sentence-based features
– Document-based features
– Corpus-specific features
[Kanhabua et al., TAIA 2012; Strötgen et al., TempWeb 2012]

Features
• Sentence-based
– senLen, senPos, isContext, cntEntityInS, cntTExpInS,
cntTPointInS, cntTPeriodInS, entityPos, entityPosDist,
TExpPos, TExpPosDist, timeDist, entityTExpPosDist
• Document-based
– cntEntityInD, cntEntitySen, cntTExpInD, cntTPointInD,
cntTPeriodInD
• Domain-specific
– isNeg, isHistory
[Kanhabua et al., TAIA 2012]

Determining Search Intent
• Two types of temporal queries:
1. Explicit: time is provided, “US President 2012“
2. Implicit: time is not provided, "Germany FIFA
World Cup"
• Temporal intent can be implicitly inferred
• Previous studies on temporal queries:
– 1.5% of web queries are explicit
– ~7% of web queries are implicit
[Nunes et al., ECIR 2008; Metzler et al., SIGIR 2009]

Query Log Analysis
• Leverage real-world query logs
– Search query frequencies over time
• Apply time-series analysis
– Time-series decomposition for detecting seasonal
queries
[Metzler et al., SIGIR 2009; Shokouhi, SIGIR 2011]

Time-series Decomposition
Query: Easter

Matching: Re-visited
D2006
Ranked results
query
Temporal
Web
Determining
Search Intent
Term: {Germany, World, Cup}
Time: {06/2006, 07/2006}
D2006
Retrieved results
matching
Time-sensitive
Queries
Semantic
Annotation
Annotated
documents Term: {w1, w2, …, wn}
Time: {PubTime(di), ContentTime(di)}

Time-aware Retrieval and
Ranking

Searching the Past
• Searching documents created/edited over time
– E.g., web archives, news archives, blogs, or emails
– A journalist wants to write a timeline of a news article
– A Wikipedia contributor searches for historical
information about an entity of interests
Web
archives
news
archives
blogs emails
“temporal document
collections”
Retrieve documents
about Pope Benedict
XVI written before 2005
Term-based IR approaches
may give unsatisfied results

• Time must be explicitly modeled in order to
increase the effectiveness of ranking
– To order search results so that the most relevant
ones are ranked higher
• Time uncertainty should be taken into account
– Two temporal expressions can refer to the same time
period even though they are not equally written
– E.g. the query “Independence Day 2011”
• A retrieval model relying on term-matching only will fail to
retrieve documents mentioning “July 4, 2011”
Challenges

Query/Document Models
• A temporal query consists of:
– Query keywords
– Temporal expressions
• A document consists of:
– Terms, i.e., bag-of-words
– Publication time and temporal expressions

Time-aware Ranking Models
• Two main approaches
1. Mixture model [Kanhabua et al., ECDL 2010]
• Linearly combining textual- and temporal similarity
2. Probabilistic model [Berberich et al., ECIR 2010]
• Generating a query from the textual part and temporal part
of a document independently

Mixture Model
• Linearly combine textual- and temporal similarity
– α indicates the importance of similarity scores
• Both scores are normalized before combining
– Textual similarity can be determined using any term-
based retrieval model
• E.g., tf.idf or a unigram language model

Mixture Model
• Linearly combine textual- and temporal similarity
– α indicates the importance of similarity scores
• Both scores are normalized before combining
– Textual similarity can be determined using any term-
based retrieval model
• E.g., tf.idf or a unigram language model
How to determine temporal similarity?
[Kanhabua et al., ECDL 2010]

Temporal Similarity
• Assume that temporal expressions in the query are
generated independently from a two-step
generative model:
– P(tq|td) can be estimated based on publication time
using an exponential decay function [Kanhabua et al.,
ECDL 2010]
– Linear interpolation smoothing is applied to eliminates
zero probabilities
• I.e., an unseen temporal expression tq in d
Similarityscore
Time
d1 d2
<q>
Dist(d1,q)
Dist(d2,q)

Temporal Similarity
• Assume that temporal expressions in the query are
generated independently from a two-step
generative model:
– P(tq|td) can be estimated based on publication time
using an exponential decay function
– Linear interpolation smoothing is applied to eliminates
zero probabilities
• I.e., an unseen temporal expression tq in d

Problem Statements
• Queries of named entities (people, company, place)
– Highly dynamic in appearance, i.e., relationships between
terms changes over time
– E.g. changes of roles, name alterations, or semantic shift
Named Entity Evolution
Scenario 1
Query: “Pope Benedict XVI” and written before 2005
Documents about “Joseph Alois Ratzinger” are relevant
Scenario 2
Query: “Hillary R. Clinton” and written from 1997 to 2002
Documents about “New York Senator” and “First Lady of
the United States” are relevant

Examples of Name Changes
QUEST Demo: http://research.idi.ntnu.no/wislab/quest/

Current Approaches
• Temporal co-occurrence
• Temporal association rule mining
• Temporal knowledge extraction
– Ontology
– Wikipedia history
[Berberich et al., WebDB 2009; Kanhabua et al., JCDL 2010]
[Kaluarachchi et al., CIKM 2010; Tahmasebi et al., COLING 2012]

Temporal Co-occurrence
• Temporal co-occurrence
– Measure the degree of relatedness of two entities at
different times by comparing term contexts
– Require a recurrent computation at querying time,
which reduce efficiency and scalability
[Berberich et al., WebDB 2009]

Association Rule Mining
• Temporal association rule mining
– Discover semantically identical concepts (or named
entities) that are used in different time
– Two entities are semantically related if their
associated events occur multiple times in a collection
– Events are represented as sentences containing a
subject, a verb, objects, and nouns
[Kaluarachchi et al., CIKM 2010]

Temporal Knowledge Bases
• YAGO ontology
– Extract named entities from the YAGO ontology
– Track named entity evolution using the New York
Times Annotated Corpus
• Wikipedia history
– Define a time-based synonym as a term semantically
related to a named entity at a particular time period
– Extract synonyms of named entities from anchor texts
in article links using the whole history of Wikipedia
[Mazeika et al., CIKM 2011; Kanhabua et al., JCDL 2010]

Search with Name Changes
• Extract time-based synonyms from Wikipedia
– Synonyms are words with similar meanings
– In this context, synonyms refer name variants (name
changes, titles, or roles) of a named entity
• E.g., "Cardinal Joseph Ratzinger" is a synonym of
"Pope Benedict XVI" before 2005
• Two types of time-based synonyms
1. Time-independent
2. Time-dependent
[Kanhabua et al., JCDL 2010]

Recognize Named Entities

Find Synonyms
• Find a set of entity-synonym relationships at time tk
• For each ei ϵ Etk , extract anchor texts from article
links:
– Entity: President_of_the_United_States
– Synonym: George W. Bush
– Time: 11/2004
President_of_th
e_United_States
George
W. Bush
George
W. Bush
Presiden
t George
W. Bush
Presiden
t Bush
(43)

Initial Results
• Time periods are not accurate
Note: the time of synonyms are timestamps of Wikipedia articles (8 years)

• Analyze NYT Corpus to discover accurate time
– 20-year time span (1987-2007)
• Use the burst detection algorithm
– Time periods of synonyms = burst intervals
Enhancement using NYT

• Analyze NYT Corpus to discover accurate time
– 20-year time span (1987-2007)
• Use the burst detection algorithm
– Time periods of synonyms = burst intervals
Enhancement using NYT
Initial results

Query Expansion
1. A user enters an entity as a query
[Kanhabua et al., ECML PKDD 2010]

Query Expansion
2. The system retrieves synonyms wrt. the query

Query Expansion
2. The system retrieves synonyms wrt. the query
3. The user select synonyms to expand the query

References
• [Berberich et al., ECIR 2010] Klaus Berberich, Srikanta J. Bedathur, Omar Alonso, Gerhard Weikum:
A Language Modeling Approach for Temporal Information Needs. ECIR 2010: 13-25
• [Radinsky et al., WWW 2012] Kira Radinsky, Krysta Svore, Susan T. Dumais, Jaime Teevan, Alex
Bocharov, Eric Horvitz: Modeling and predicting behavioral dynamics on the web. WWW 2012: 599-
608.
• [Dumais, SIAM-SDM 2012] Susan T. Dumais: Temporal Dynamics and Information Retrieval. SIAM-
SDM 2012
• [de Jong et al., AHC 2005] Franciska de Jong, Henning Rode, Djoerd Hiemstra: Temporal language
models for the disclosure of historical text. AHC 2005: 161-168
• [Kaluarachchi et al., CIKM 2010] Amal Chaminda Kaluarachchi, Aparna S. Varde, Srikanta J.
Bedathur, Gerhard Weikum, Jing Peng, Anna Feldman: Incorporating terminology evolution for query
translation in text retrieval with association rules. CIKM 2010: 1789-1792
• [Kanhabua et al., JCDL 2010] Nattiya Kanhabua, Kjetil Nørvåg: Exploiting time-based synonyms in
searching document archives. JCDL 2010: 79-88
• [Kanhabua et al., ECDL 2010] Nattiya Kanhabua, Kjetil Nørvåg: Determining Time of Queries for Re-
ranking Search Results. ECDL 2010: 261-272
• [Kanhabua et al., TAIA 2012] Nattiya Kanhabua, Sara Romano, Avaré Stewart: Identifying Relevant
Temporal Expressions for Real-World Events. Time-aware Information Access Workshop 2012
• [Ke et al., CN 2006] Yiping Ke, Lin Deng, Wilfred Ng, Dik Lun Lee: Web dynamics and their
ramifications for the development of Web search engines. Computer Networks 50(10): 1430-1447
(2006)

References (cont’)
• [Metzler et al., SIGIR 2009] Donald Metzler, Rosie Jones, Fuchun Peng, Ruiqiang Zhang:
Improving search relevance for implicitly temporal queries. SIGIR 2009: 700-701
• [Nunes et al., ECIR 2008] Sérgio Nunes, Cristina Ribeiro, Gabriel David: Use of Temporal
Expressions in Web Search. ECIR 2008: 580-584
• [Risvik et al., CN 2002] Knut Magne Risvik, Rolf Michelsen: Search engines and Web dynamics.
Computer Networks 39(3): 289-302 (2002)
• [Shokouhi, SIGIR 2011] Milad Shokouhi: Detecting Seasonal Queries by Time-Series Analysis.
SIGIR 2011: 1171-1172
• [Strötgen et al., TempWeb 2012] Jannik Strötgen, Omar Alonso, Michael Gertz: Identification of
top relevant temporal expressions in documents. Temporal Web Workshop 2012.
• [Tahmasebi et al., COLING2012] Nina Tahmasebi, Gerhard Gossen, Nattiya Kanhabua, Helge
Holzmann, Thomas Risse: NEER: An Unsupervised Method for Named Entity Evolution
Recognition. COLING 2012
• [WebDyn 2010] Web Dynamics course: http://www.mpi-
inf.mpg.de/departments/d5/teaching/ss10/dyn/, Max-Planck Institute for Informatics, Saarbrücken,
Germany, 2010
• [Zhang et al., EMNLP 2010] Ruiqiang Zhang, Yuki Konda, Anlei Dong, Pranam Kolari, Yi Chang,
Zhaohui Zheng: Learning Recurrent Event Queries for Web Search. EMNLP 2010: 1129-1139

Further Reading
1. Tu Ngoc Nguyen, Nattiya Kanhabua: Leveraging Dynamic Query Subtopics for Time-Aware
Search Result Diversification. ECIR 2014: 222-234
2. Miles Efron: Query representation for cross-temporal information retrieval. SIGIR 2013: 383-392

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