A Context Based Approachto Acquisition and Utilization of Personal Knowledge
for WWW Browsing
Toru Takashiro

Hideaki Takeda

RACE, The University of Tokyo
Komaba 4-6-1, Meguro-ku,
Tokyo 153-8904, Japan

National Institute of Informatics
Hitotsubashi 2-1-2, Chiyoda-ku,
Tokyo 101-8430, Japan

takasiro@race.u-tokyo.ac.jp

takeda@nii.ac.jp

Abstract: Our personal knowledge plays a vital role in our daily intellectual work. This paper proposes amethod for acquiring and utilizing our personal knowledge in computer systems. We developed a system called
MindHeap that helps us to acquire personal knowledge by browsing WWW (World Wide Web) hypertexts. Our
approach for acquiring a user's personal knowledge is to use situational context which has important effects foracquiring and reminding knowledge. Situational context means information which characterizes her/his situationof information activities. MindHeap can help the user to organize knowledge on WWW pages by finding andrecording related words as situational context for topics specified by the user. The system extracts a list ofrelated words as situational context, and records it with the topics. Thus, topics and their contexts areaccumulated, then they can work as knowledge for the user, e.g., she/he can retrieve pages of which situation issimilar to the current page.

1. INTRODUCTION
In various situations in our everyday life, we rely
on knowledge. We have some common knowledge,
and more important part of knowledge in daily life
is peculiar knowledge for everyone. We call thistype of knowledge as “personal knowledge”. Thatholds our personal character peculiar to each of us.
If a keyword is given to some different people, theywould associate the keyword with different things.
For example, the word “agent” is given to us, we
may associate it with various things such as “adagent”, “distributed computer systems with agenttechnologies”, ”spy novels”. The difference among
these associations is derived from the difference
among our memory of experiences. A person
developing computer system is likely to remember“computer agent” for the keyword “agent”, and aperson who have read a lot of spy novels may
remember “spy agent” for it. In this paper, wedefine knowledge that is based on our experienceand peculiar to each of us as personal knowledge.
Many cognitive psychologists such as Bruner[1]and Norman[2] assert that there are two modes ofcognition. Bruner calls one as the paradigmaticmode, and the other as narrative mode. According toBruner, the paradigmatic mode is the mode ofexplanation. In this mode, we tend to explain things
logically and abstractly. On the other hand, thenarrative mode is the mode of understanding. We
can understand things with peculiar ways based on
our experiences that are different from each other.

Both modes are important for our intellectual work.
It is therefore needed to treat knowledge in bothmode of cognition to deal with knowledge on
computer systems. Our way of treating knowledge
in the paradigmatic mode resembles that ofcomputer systems, since computer systems havewell-defined knowledge bases and reason logically.
However, such computer systems cannot treat themode of narrative, and ignore personal dependent
properties of knowledge. These systems postulatethat knowledge is shared by all the people incommon. Therefore, users are often dissatisfied
with the systems because these systems cannot
work as their expectations. Consider the case inwhich we use search engines on WWW. When auser sends a query to search engines, they retrieve
web texts that match with the query from WWW.
The same results are given for another user whosends the same query to search engines. It is
undesirable behavior with respect to the narrativemode of cognition because expectations of different
users should be different even if they ask the samequery.
If it is possible to let computer systems know ourpersonal knowledge, we are able to overcome such
disappointment. Our final goal is acquiring a user's
personal knowledge in computer systems, andutilizing the personal knowledge for the user.

2. CONTEXT AND KNOWLEDGE
According to Rumelhart[3], there are the contextualeffects in remembering. These effects play animportant role in our remembering and recallingknowledge.
When we store our remembrances in memory,they are kept with information about the situation inwhich the remembrance is acquired. This situational
information is seemingly vain for our memory, but
it is conceivable to work as the key for recalling theremembrance.
We define this situational information in which
we acquire remembrance as situational context. Theword “context” can refer to many things. In thenarrow sense of the word, the context of a word orsentence consists of the words or sentences before
and after it. In addition, it is often used to mean thegeneral situation in which something exists oroccurs. In this paper, we concerned with the later
meaning.
The situational context makes our personal
knowledge peculiar to each of us. As we mentioned
in section 1, our personal knowledge derives fromoureachindividualexperiences.
Suchremembrances are accumulated in our memory, i.e.,they are associated to each other with situational
context. Since our knowledge consists of such
associated remembrances, it has a proper structure
to each of us.
In order to acquire personal knowledge incomputer systems, we need to know situational
contexts in which the knowledge is acquired, thento make use of these contexts as a cue to constructpersonal knowledge in the systems.

pages. But this acquisition is often done implicitly.
What s/he remembers explicitly is that s/he readsome words interesting to her/him. Even with suchconscious remembrance, s/he can retrieve what s/heread from various hints because of unconscious partof remembrance, i.e., situational context.
Our system works as “intelligent memo system”
by simulating the above process. The user justpoints out her/his attention by drugging some words
or phrases as “memo item”, then the system storesthe memo item with its context. The stored
information is used when s/he encounters similar
contexts.

3.1 Acquisition of Context
When the user point out their attended phrases,
the system acquires these phrases as the input. And
then the system attempts to extract the situational
context behind the inputted phrase from thedocument that is displayed on the browser at thetime. In MindHeap, situational context isrepresented as list of extracted words appendant to
the inputted phrase (see Figure 2).

Figure 2: Unit which composes of personal
knowledge.
The extracted words that are considered toconsist of situational context are linked with theinputted phrase, and each link has a value which
represents degree of relation between the extracted
word and the inputted phrase. This pair of inputtedphrase and extracted context words is the unit which
composes of the user’s personal knowledge.
To extract these context words, we need toconsider two following effects.
Figure 1: A snapshot of MindHeap

3. BEHAVIOR OF THE SYSTEM
We developed a prototype system called
MindHeap, that can assist human behavior toacquire, store and utilize personal knowledge onWWW browsing. A user on WWW browsing canacquire concepts or facts through text reading ona)

The information about situation on the time inwhich the user inputs phrase: As we havedescribed in section 2, when we remember
something explicitly, we also implicitly
remember the information about theenvironment with the user’s attention. Inbrowsing WWW, we can assume that thedocument which the user browses on the timeis an environment of the user’s attention.

Therefore, the system attempts to extract
situational context words from the document
showing on the time.
b) The influence of the user’s browsing history on
the extracted context words: If a user browses
same document, different browsing history
makes different browsing experiences for theuser. Therefore, we must consider historicalinfluence on the context.
The context is calculated as follows;Firstly, the system picks up nouns IN in the
inputted phrase I.

Secondly, it collects a set of words that would be
associated to the nouns above by calculating
occurrence in the given document. Suppose co(w,w’) is occurrence degree of word w and w’ in thegiven document D. Then the degree of association
to the inputted phrase is defined as follows;w∈co( w, w′)

Ch′w =Chw∑ Chu(w ∈W c)u∈W c

Due to memory limitation, the system can only
record values of Fw for a limited number of words.
Then we may encounter un-recorded Fw in
calculation of Chw. We assign 0 for Fw if Fw is notfound in the record.

3.2 Representation of Personal Knowledge
in the System

I N ={w:w∈noun,w∈I}

C w' = ∑Ch w = C ′w・( F ′w + 1)

(w’ ∈noun,w’∈D)

INPhrases which a user inputs to the system onbrowser and context words appendant to the
inputted phrases are recorded on the system’s
memory as units shown in Figure 2. The more theuser inputs phrases which she/he attempts to, themore units are appended to the memory. When a
new phrase with contexts is added, phrases whichhave same word in their contexts are tied up withthose words (see Figure 3).

In the current implementation, co(w, w’) is anumber of occurrence where word w and w’ areappeared in at most seven consecutive words in thedocument. We adopt top 30 words in Cw as context
words and call them Wc.
Thirdly, we obtain “non-historical” context C’w bynormalizing Cw.

C ′w =Cw∑ Cu(w ∈ W c)u∈W c

In order to consider browsing history, we modify
the context by emphasizing it with contexts of thepast five pages. If a word in the “non-historical”
context is often appeared in the past pages, the word
should be more important.
We just regard context of a page as occurrence
pattern of words in that page. Suppose Fw is anumber of occurrence of word w in the past page.
Then we can obtain its normalized version F’w
according to the set of words in the “non-historical”
context.

FwF ′w = ∑Fu( w ∈ W c)u∈W c“Historical” context Ch’w is defined as theexaggerated version of “non-historical” context by
the past pages in the following way.

Figure 3: An example of graph structure
As a result, the phrases that the user inputted tothe system are organized as a graph structure inwhich context words shared by some phrases
mediate between the phrases. For example, inFigure 3, Phrases such as “Design as intelligent
activity” and “Ontology for Design” are mediatedwith context words, “research” or “design”. It
means that concepts represented by these phrases
have associative relation for the user who input
these phrases with the contexts which arerepresented by context words
The graph structure is improved by the user’s
input, and it can work as personal knowledge for theuser.

3.3 Utilization of Personal Knowledge

With the structure of a user’s personal knowledgedescribed above, we try to navigate the user withsuggesting related pages to the user.
When the user points out her/his attended phrase,
MindHeap can suggest links to related pages to theuser. After context extraction process for the phrase,
the system scans the graph structure described inSection 3.2 for phrases which related with thenewly inputted phrase. If exists, the system displaysthese phrases with context words which mediate
these phrases and the newly inputted phrase (Figure4).

Figure 4: Suggestion of links to related pages
For example, in Figure 4, three phrases aresuggested to the user for the newly inputted phrase“design as intelligent activity”. In Figure 4, the usermarks “design as intelligent activity”. Then thesystem suggests their related topics, i.e., “supportintelligent activity”, “ontology for design” and“knowledge sharing”. The lower part of a box
shows the name of a topic that was marked by theuser, and the upper part shows context words thatmediate that topic and the original topic.
These suggested phrases work as hyperlinks. Theuser can link to the page in which she/he input thephrase by clicking suggested phrase. And byclicking the icon which appears at the upper left
corner of the dragged phrase, the user can send thequery to a search engine. The query includes not
only the inputted phrase but also extracted context
words, so the user can find new WWW pages which
are related in her/his browsing context.

4. RELATED WORK
There found many systems to acquire andintegrate personalized information for a user fromWWW.
WebWatcher[4] recommends hyperlinks basedon a user’s interests which the system asked to theuser at the beginning. Letizia[5] is also a
recommendation system. It watches a user’s
browsing behavior, presumes the user’s interest andrecommends web pages to us. Margin Notes[6]works as a proxy server for WWW, and it appends

recommendations to original WWW texts. They areintended for automated browsing with fewer user
actions, but our system aims to interactive
knowledge acquisition system where the system andthe user cooperate to improve knowledge.
Hirashima et al.[7] emphasized the importanceof context on hypertext browsing. They use mainly
a user’s browsing history as context, whereas oursystem uses situational context.

5. CONCLUSION AND FUTURE
WORKIn this paper, we discussed how personal
knowledge should be acquired in computer systems
with using situational context and proposed a
prototype system called MindHeap that cansimulate our acquiring and recalling process ofpersonal knowledge. With this system, the user caneasily capture her/his interesting topics and alsoeasily retrieve them, because the system collects
and organizes contexts of topics automatically. Of
course, our current implementation is weak. We
must consider possibility to manage context otherthan situational or historical context, and analyze
the results achieved.
We are planning to apply our system to multiuser system. In the current implementation, oursystem can only work for a user. To expand oursystem into multi-user system, we will share andexchange acquired personal knowledge withcontext.

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