Document Retrieval through Time-dependent Events in the Web
M IKIHIKO M ORI Ý and S EIJI YAMADA ÝÝWe propose document retrieval in the Web by a visual interface arranging stories about events according
to time and user’s context. We present a concept of representation time-dependent events in the Web. We
give the definitions of the terms of story, event and topic. It is necessary to illustrate Web documents as
timeline to retrieve stories through a time. We also present the chronological visual interface to interact withusers. The interface shows stories in a timeline, which is made of Web documents. The visualization in theinterface shows clusters of stories that are similar mutually.

date, it is not satisfied. Some news sites present link
pages for collecting and tidying up on some events.
However, they provides just their inside pages.
We aim to develop a technique which retrieves
several series of relevant events in the Web pages,
clusters by searcher’s viewpoint and visualizes theevents and the series, and then to build a system implemented in the technique. In this paper, we propose our framework of technique.

1. Introduction
There are a large number of pages in the Web forreasons that most people are thinking that the Web
is a place of information service and communication. A kind of Web pages tells news in the realworld as online news sites, another teaches knowhow or knowledge of something. In recent years,
it is increasing that people begin to communicate
through the Web so that Web pages become morefunctional. They publish diaries or weblogs (blogs)
on the Web for talking about that they thought, felt
and found something.
Many researchers challenged to solve finding
necessary information in such pages. As a solution, many search engines exist for retrieving theinformation. However, these search engines tend
to present a lot of hit pages by users’ queries. As
one technique of more precise page ranking, Brinand Page proposed link-based estimation of ranking2) and presented Google which is now a largest
search engine. When a user wants to search throughblogs, Nanno et al. proposed a search method specialized in blogs5) .
Although Web users can obtain precise information, a series of pages to which is related mutually
can be hardly found out. For example, when a user
is interested in the affair of the avian influenza told
on the Web in 2003, the user may use a search engine by way of input a query like “bird flu 2003”,
but the user obtains much information by a lot ofpages from online news, blogs, diaries, public comments, etc. These pages is not clustered and notarranged in order of timeline. Though some searchengines show the time at which the page was up-

2. Related Works
Some search engines attend to the last update
time of the Web page or cluster similar pages ofsearch results as topics. Fresheye ☆1 shows the last
update times when it could get the times. Google ☆2also shows the last update times if it assumes thepages are frequent updated. Although these searchengines attend to document’s times, they do not attend to timeline and topics. On the other hand,
Clusty☆3 used the clustering engine of Vivisimo ☆4organizes search result pages. Each of the clusters
labeled short phrase. WiseNut ☆5 is also a clustering
search engine which has narrow down the results by
the words in the label as additional feature. These
search engines could enhance the results by topics
but do not look at document’s times.
Allan et al. proposed Topic Detection and Tracking (TDT)1) . TDT aims at developing algorithm fordetecting and tracking topic from newswire corpus
which had already structured in SGML format. Although it is similar to our work, we attend to timevalues in documents and do not use prepared corpus.
☆1

http://www.fresheye.com/
http://www.google.com/☆3
http://www.clusty.com/☆4
http://www.vivisimo.com/☆5
http://www.wisenut.com/☆2

Ý Kyoto UniversityÝÝ National Institute of Informatics

1Story
TopicDocument

Time

Event 1

Event 2

Event 3Fig. 1 The relation between events, stories and topics

There are works of time-dependent visualization
techniques for personal information. Lifestreams 4)
manages a time-ordered stream of documents thatare stored in daily life of each user. The user isable to filter from the stream and to create documents to it. Time-Machine Computing 7) proposes atime-centric approach that information is organizedand visualized user’s documents. It enables various visualization of time in addition to storing andfinding out their documents. Stuff I’ve Seen (SIS) 8)
searches personal contents by the value of annotating timeline with event landmark like digital photographs. It provides timeline-based visualizationof search results.
Kumar et al. uses visualizing timelines of metadata in digital libraries. Although they attend to
metadata in closed libraries, we step in contentson the Web in which people maintain each other’s
pages9) .
Blog Watcher5) searches blogs in terms of dateand words as a query. It also shows burst of a word,
which have a relation to a popular topic. It does not,however, show related documents by timeline while
blog writers surge in a topic.
Dying Link system 6) displays freshness of Web
pages by the appearance of a link. If a page was not
modified more old, the link to the page is effected
the look.
Loom3) visualizes discussions of Usenet groups
on a 2D map to show the social patterns. Each participant represent on an axis and posts of a participant are plotted dots along other axis as the time.

3. Time-dependent Event and Stories on Topics
We use the terms such as event, story and topic
which are similar to the usage of TDT. Their definitions are as follows.
We consider a document to tell some stories. Thestory says an event occurred or regarded to occur
in the world. The story sometimes describes someevents. Each story must have its written, first appeared or update time shown in the document. Thestory also includes descriptions about the event andrelated events. A topic is in the context of a readerof documents, which usually contains the stories ofa certain event. The topic frequently contains thestories of similar events or related events.
A time-dependent story among others has thetimes created or updated, or the risen time of anevent. In this paper, we employ just the story depended on the time and omit the others.
Fig. 1 shows an example of relationship of stories, events and topics. All stories are derived
from their individual events (shown as start pointof dashed line to each story-dot). Original documents including stories represent dashed ovals. Theleft dashed rectangle describes a topic about single
events. On the other hand, the right dashed rectangle describes another topic about two events which
may be related or similar events in a context. The
context is determined by each documents’ reader.
4. Searching Time-Dependent Stories
Readers of Web documents often have an inter-2

The Web

Query input
orInteraction

Crawler

Extractor

VisualInterface

SearcherIndexer

Index

Fig. 2 The architectural concept

Story
Similar Stories

The stories
related to a topic(dashed line)

TimeFig. 3 Visualizing timeline in the interface to search and to explore

est in the time of the documents. To know a topic
that they meet first, they need to see its context.
They must usually search for stories on the topic.
However, the following presentations are usually
not shown for the readers:¯ Freshness or recentness of the stories in a document describing the topic,¯ Related topics which do not include the eventsof the original topic,¯ The stories related to the topic in a document by
extracting each stories which describe different
topics,¯ Stories in chronological order only on the topic,¯ Relations among a lot of stories from different
documents,¯ To use reader’s context to search the stories.
Our Approach to present timeline helps these demands of using documents on the Web. Whensearch for stories on a topic, the timeline presentation shows the stories about the topic or stories oftopics related to it. We propose the following techniques for time-dependent search and visualization.
Crawling is the first action to utilize Web documents. Crawlers get Web documents to follow
each hyperlink in them. For time-dependency,
our crawler will be led to the pages that haveexpressions of the time like online news, blogs
and diaries. Here, a Web document is considered a Web page as a file.
Extracting of the time is necessary to present
timeline of stories. Every story is made related
to the times created or updated, or the risentime of event in the story. Every story is also
extracted at same time.
Indexing makes from stories Web pages crawled.
A story can properly be hit with words in the in-3

dex. The searcher has to be able to distinguisheach of the time exampled above.
Search uses above index by the times, words andother stories or topics.
Visual interface illustrates stories of search results and arranges stories by relations between
stories, events and topics. Users interact
through the interface to search and explore thestories on their topics.
Our concept of the architecture integrates thesetechniques as modules in Fig. 2. These modules
cooperate to present the stories on a topic. The
crawler gets Web pages via HTTP, retrieves hyperlinks and gets other pages over again. It also sends
the pages to the extractor of the times and words.
If the extractor recognizes the page including timedependent stories, the extractor sends a message tothe crawler back. When the crawler gets the message, it reinforces crawling around the page for thereason that the crawler is probably within online
news, blogs or diaries. If the crawler gets a timeindependent story, the extractor will estimate thetime of its content. The indexer gets the times, thewords, and the content of story from the extractor
and adds them to the index optimally.
When users demand to view the stories on a topic,
they input query about topic at first, then the interface shows the stories visually. These stories havebeen filtered by searcher beforehand. The users caninteract to accurate the topic more and more.

6. Conclusion
In this paper, we presented a concept of representation time-dependent events in the Web. We gave
the definitions of the terms of story, event and topic.
It is necessary to illustrate Web documents as timeline to retrieve stories through a time. We also presented the chronological visual interface to interact
with users. The interface showed stories in a timeline, and the visualization in the interface showedclusters of stories that were similar mutually.
We will realize this interface and the back end
modules. We will evaluate availability of this system experimentally.
References
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5. Chronological Visualization
The chronological visualization illustrates stories
that are clustered by similarity each other, arranged
along time axis as a topics (see also Fig. 3). The
similarity among stories calculates nearness of thetime and contents. In Fig. 3, dashed ovals group thesimilar stories. However, stories are not separated
clearly. Therefore, actual visualizing system will
not display the ovals.
Users are able to view stories on the interface
to click the story nodes. The interface enables toaccurate the topic in their mind. For traditional
method of searching, it prepares input form forword queries. The other method provides interactive interface on the visualizing part. Every story
on the visual interface are selectable. The searcher
make query internally and put into the interface
when the user selects them.

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