PAPER

On the Value of Temporal Information in Information Retrieval
Omar Alonso1, Michael Gertz1 and Ricardo
Baeza-Yates2
1
Department of Computer Science
University of California at Davis, Davis, CA.
2
Yahoo! Research Barcelona, Spain
oralonso@udavis.edu, gertz@ucdavis.edu,
rbaeza@dcc.uchile.cl

Abstract
Time is an important dimension of any information space andcan be very useful in information retrieval. Current
information retrieval systems and applications do not takeadvantage of all the time information available in the content
of documents to provide better search results and user
experience. In this paper we show some of the areas that canbenefit from exploiting such temporal information.

1

Introduction

As the amount of generated information increases so rapidly in the digital world, the concept of time
as dimension along which information can be organized and explored becomes more and more
important. Time and time measurements can help in recreating a particular historical period or
describing the context of a document or document collection, which then can be helpful for relevancy
ranking purposes.
As an alternative to document ranking techniques like those based on popularity, time can bevaluable for placing search results in a timeline for document exploration purposes. Current
information retrieval systems and applications, however, do not take advantage of all the time
information available within documents to provide better search results and thus to improve the user
experience.
A quick look at any of the current search engines and information retrieval systems shows that thetemporal viewpoint is restricted to sorting the search result represented in a hit list by date only. The
date attribute is mainly the creation or last modified date of a Web page or document. In some cases,
this can be misleading, because the timestamp provided by a Web server or any other document
management system may not be accurate. Other search applications provide a range date search aspart of the advanced search options. Still, the search results are filtered based on the date attribute.
For search purposes, the time axis is mainly constructed using that type of document metadata.

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Even simple queries against Web search engines show that oftentimes organizing the documents in a
hit list along some timeline can be helpful. For example, a query for “soccer world cup” against
search engines now returns mostly pointers to documents that cover the recent event in Germany. But
every soccer fan knows that this event happens every four years. Clearly, it would be useful if a tool
on top of a traditional retrieval system is more aware of the temporal information embedded in the
documents and allows the user to have search results presented in different ways based on thetemporal information. For this, it is essential to extract temporal information from documents andassociate documents with points in time along well-defined timelines.

1.1 Motivating Examples
One can argue that for certain types of data sources or depending on a particular application, the roleof time is implicit in the query. If one is looking for information about “Q3 earnings” for a company,
it is assumed that the query means information that pertains to the current fiscal year.
This is typical for content that has a short time span such as news articles. News is essentially new
information or current events. If the news is old, it is generally assumed that it is not relevant. On theother hand if the query is “tsunami disaster”, and given that the event happened a few years ago, one
expects to retrieve the fact of the event, not necessarily the latest news (although that can be the case,too). Another example is a query about the Iraq war conflict, where the results are based on the latest
events with little content from the early 1990s war. In short, time does impact the quality of search
results. Ideally, we would like a search engine to be aware of the temporal information embedded in
documents and present the results in a time context.
In contrast to the Web where crawlers continuously run to collect new content and eliminate
references to broken links, Intranet search engines are starting to go into the opposite direction. For
example, based on the Sarbanes-Oxley legislation where public companies are required to keep
records of assets (email, for example) for periods of time, it is becoming important to store every
reference to a document - even if the document is no longer present. In case of an audit, forensic
retrieval (searching the past) can be performed and derive the existence of such a document from theindex. A query like “sell stock before earnings” in a particular timeframe determines its relevancy.
The important scenario here is “what did you know at what period of time?”
There are other vertical search applications like those in Health Care, where certain data sets such as
patient discharge summaries contain time information. In reconstructing a patient’s medical history,
the ability to find events and present them in a timeline is a key aspect for establishing the accuracyof the report.
From a user’s perspective, the relevancy of a query has a temporal aspect. The more data sources aninformation retrieval system acquires, the more important the temporal aspect can be in the retrieval
process. Instead of assuming that the user wants relevant search results implicitly sorted by date, itwould be interesting to investigate a system that is aware of time for relevancy and shows search
results in a temporal context. It can also be useful to filter the “trendy stuff” from the rest.
Every retrieval engine combines search and browsing features by categories (predefined or
generated). One can also extend the notion of browsing by adding temporal attributes. A clear
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advantage is that there is a predefined order for temporal items. For example, Wednesday is before
Thursday and the year 2002 describes the range of days from January 1st to December 31st. All of us
are used to using a calendar metaphor to manage and view main events in time. A combination of
calendar and browsing looks like an interesting alternative to searching and navigating large
collections from a temporal perspective.

2

What is Time?

Time has been the subject of study in many disciplines, particularly in philosophy, physics, logic, andArt. We start the study of time information in documents with a number of definitions to set up theright context. A look at an English dictionary shows the following definition for “time”: a) a nonspatial continuum in which events occur in apparently irreversible succession from the past throughthe present to the future; b) An interval separating two points on this continuum; a duration.
Since the notion of event appears quite often in combination with the notion of time, its dictionary
definition says: Event: a) Something that takes place; an occurrence; b) A significant occurrence or
happening; c) A social gathering or activity.
Time is defined operationally and involves the process of measuring the units chosen such as
millisecond, minute, day, and century. A timeline, also known as a chronology, is a linear
representation of events in the order in which they occurred. This sequence of events is usuallyarranged in chronological order and presented in a line display drawn left to right or top to bottom.
A calendar is a human designed system for representing physical time. A calendar defines the timevalues, called granularities, of interest to a user, usually over a specific segment of the timeline. One
calendar familiar to many is the Gregorian calendar.

2.1 Time and Timelines
As the basis for associating points in time with documents, it is customary to assume a discrete
representation of time based on the Gregorian calendar, with a single day being an atomic timeinterval called chronon. A base timeline, denoted Td, is an interval of consecutive day chronons. For
example, the sequence “March 12, 2002; March 13, 2002; March 14, 2002” is a contiguous
subsequence of chronons in Td. Contiguous sequences of chronons can be grouped into larger units
called granules, such as weeks, months, years, or decades. A grouping based on a granule results in amore coarse-grained timeline, such as Tw based on weeks, Tm based on months, or Ty based on years.
Examples of week chronons in Tw are “3rd week of 2005” or “last week of 2006”. Depending on thetype of underlying calendar, base timeline, and grouping of chronons, timelines of different time
granularity can be constructed.

2.2 Temporal Expressions
There is quite a lot of temporal information in any corpus of documents. Besides a simple document
timestamp, what types of temporal information are there and how do they relate to timelines? A
temporal entity describes a point in time, event, or time period at a conceptual level. The
identification of such entities involves a linguistic analysis of the document, where approaches based
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on named-entity extraction determine temporal expressions. A temporal expression is basically asequence of tokens that represent an instance of a temporal entity.
Similar to the approach by Schilder and Habel [10], here we identify the following three categories:
•

•

•

3

Explicit. These temporal expressions directly describe entries in some timeline, such as anexact date or year. For example, the token sequences “December 2004” or “September 12,
2005” in a document are explicit temporal expressions and can be mapped directly tochronons in a timeline.
Implicit. Depending on the underlying time ontology and capabilities of the named entity
extraction approach, even apparently imprecise temporal information, such as names ofholidays or events can be anchored in a timeline. For example, the token sequence “Columbus
Day 2006” in the text of a document can be mapped to the expression “October 12, 2006”, orthe sequence “Labor Day 2008” can be mapped to “September 1, 2008”.
Relative. These temporal expressions represent temporal entities that can only be anchored ina timeline in reference to another explicit or implicit, already anchored temporal expression(which, in the worst case, is the document timestamp). For example, the expression “today”
alone cannot be anchored in any timeline. However, it can be anchored if the document is
known to have a creation date. This date then can be used as a reference for that expression,
which then can be mapped to a chronon. There are many instances of relative temporal
expressions, such as the names of weekdays (e.g., “on Thursday”) or months (e.g., “in July”)
or references to such points in time like “next week” or “last Friday”.

Extracting Time from Documents

The extraction of temporal expressions from documents can be accomplished using an approachsimilar to named-entity extraction. We call such an approach a document annotation pipeline. The
first step is to extract time metadata from the document. This can be the creation or last modified dateof a file. In case of a Web page, we rely on the information provided by the Web server. The second
step is to run a part of speech tagger (POS tagger) on every document. A POS tagger returns the
document with parts of speech assigned to each word/token like noun, verb, etc. The tagger also tags
sentences delimiters that later are needed for temporal annotation. The third step is to run a temporal
expression tagger like GUTime on the POS-tagged version of the document [5]. This step extracts
temporal expressions based on the TimeML standard and produces an XML document. We rely on
the TimeML specification for temporal annotation, because it has emerged as the standard markup
language for events and temporal expressions in natural language [13]. Other approaches can befound in [11].

4Applications

In this section, we outline a few application areas that can benefit from using temporal information indifferent ways. The list is by no means complete and the purpose is to only highlight how time canprovide new insights. Earliest work on using temporal information are [1,6]. Another look at timefrom the linguist and natural language aspect can be found in [7,8].

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4.1 Ad-hoc retrieval
An information retrieval system helps users find documents that satisfy their needs. We all know thatusers are not very expressive in what they want and that the information they provide can be
ambiguous. The central idea in temporal information retrieval is to utilize the temporal expressions
that have been determined for each document in a given document collection in order to rank search
results based on the temporal information embedded in the documents. By using this approach, timeplays a central role in the overall quality of search results. The search application retrieves the result
based on the relevance of the documents with respect to the query using traditional metrics and the
distance of the query terms to temporal expressions in the documents. After all, tense happens at thesentence level so it is important to detect these “boundaries” with respect to the query.

4.2 Hit-list clustering
Hit-list clustering has emerged as an alternative mechanism to present similar documents without
requiring the user to go through hundreds of items. Clustering of result documents can lead to better
user interfaces and, therefore, to an improved user experience, in particular in the context of
information exploration. There are several ways in which the temporal expressions in the set of
retrieved documents can be used to group the documents using temporal aspects. Given such a hit
list, the first step is to construct a time outline for the documents in the hit list. The documents arethen clustered along this timeline, again based on their temporal characteristics. A single cluster canbe thought of as a bin that contains only documents with temporal expressions matching the cluster
label, which corresponds to some chronon from a timeline. The organization of clusters along atimeline as well as the lattice structure imposed among timelines then allows for the exploration of
document clusters at different levels of time granularity [2].

4.3 Exploratory Search
Exploratory search systems have emerged as a specialization of information exploration to support
serendipity, learning, and investigation of large data sets. In search situations where the task requiresbrowsing and exploration of a search result, we argue that temporal information can help significantly
to accomplish respective tasks. The presentation of relevant information along a timeline is an
important step to find, for example, the most recent document relevant to a query or the first point intime a document (based on the temporal information contained in the document) is relevant to thequery [3,9]. The use of time and timelines for clustering and browsing nicely fits exploratory searchsystems that go beyond simply returning some documents or answer in response to a query.

4.4 Presentation
Current interfaces to search engines typically present search results sorted by the relevance of
documents from a document collection to a search query. Temporal attributes in Web pages ordocuments such as date, however, are just viewed as some structured criteria to sort the result indescending order of relevance. Traditionally, time is represented by an arrow where events are place
along the timeline. Recently, new visualization tools have emerged with alternative variations. For
example, TimeWall allows a user to define cards with attributes over long horizons while being able
to focus in on a particular time of interest [14]. SIMILE allows the construction of a timeline that can
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contain one or more time bands, which can be panned infinitely by dragging with the mouse. A band
can be configured to synchronize with another band such that panning one band also scrolls the other[12]. Finally, Google has added the view:timeline mechanism that allows to see certain search
results presented in a timeline [4].

5Conclusions

Temporal information embedded in documents in the form of temporal expressions provides animport means to further enhance the functionality of current information retrieval applications.
Recognizing such temporal information and exploiting it for document retrieval and related
applications are important features that can significantly improve the current functionality of search
applications. We believe that when a user is engaged in tasks that require time-related investigationand sensemaking, traditional information retrieval and search engines fall short if they do not fully
exploit the various types of temporal information embedded in documents.

6

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[10] Frank Schilder and Christopher Habel. “From Temporal Expressions to Temporal Information:
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