Concept Index: Social Knowledge Construction from Documents
by Keiichi Nakata, Angi Voss, Marcus Juhnke and Thomas Kreifelts
With the rapid expansion of the Internet in the recent years,
there is a potential for the Internet to become a place in which
social activities take place. In the research framework ‘Social
Web’ at GMD Institute for Applied Information Technology, scientists
are exploring ways to facilitate social activities on the Internet
such as meeting people with similar interests, forming groups
and working together. In this research context, our group is interested
in how social knowledge, ie, a body of knowledge that belongs
to a group or community, can be captured and maintained.
The Concept Index is our first attempt initiated in April 1998.
It is based on the assumption that in the Internet-based situation,
members of a group would use documents as their main means of
information exchange, and these documents in turn contain cues
to what people know or are interested in, in words or phrases.
These keyphrases (keywords and phrases) describe concepts which
can be seen as the building blocks of knowledge. Once we can capture
the concepts they can be shared, inspected, and enhanced by members
of the group. The interactive and, more significantly, collaborative
process of identifying related or associated concepts is expected
to reveal the tacit knowledge held by the group as a whole. So
far we have developed a first demonstrator which is used to conduct
experiments on the validity of this approach.
A Concept Index is typically constructed in the following manner.
First, a document is registered with a collection of documents
that share the Index. In this document, users can highlight keyphrases
which are important or significant. These keyphrases are inserted
into the Index. In the Index, users can inspect a concept description
and may identify other words or phrases that would describe the
same concept, or identify concepts that are strongly related to
it. Types of such relations include super and sub concepts, and
simple associations.
Synonyms and concept relations can also be imported from electronic
thesauri, and through a text analysis technique known as text
mining, to enhance the Index. Whenever a user views a document,
all the occurrences of concepts in the Index are highlighted by
colour. Since any member in the group can contribute to the Index,
this means a user can see the keyphrases that others found important
or interesting. Moreover, links to other documents in the collection
that contain the same concept are automatically inserted, making
it possible for the user to navigate from one document to the
other through the concept space.
The task of identifying important and interesting concepts can
be performed in a more direct fashion, for instance by asking
the user using forms containing questions such as “List some keywords
that best describe your interest“. However, collecting information
of this nature in such a way is often cumbersome and cannot deal
efficiently when user’s interests change and grows through the
exposure to new information obtained through reading articles
etc.
The approach taken by the Concept Index addresses this problem
of knowledge acquisition and evolution. A user can simply mark
the phrases they find interesting while writing, reading, or glancing
through a document as you would do using a highlight marker on
paper, and these keyphrases are automatically recorded in the
Concept Index and updated as new documents arrive.
Once a Concept Index is generated, and enhanced by the members
of the group who share the Index, it can evolve into a community
language. Such a language consists of terms whose meanings are
agreed upon by the group, and the concepts to which they refer.
Since entries in the Index are always grounded in documents produced
or accepted by the group, the concepts are implicitly defined
by their usage.
Furthermore, a Concept Index may be used to memorise what people
found interesting, leading to what may be called collective memory,
ie, a collection of memories gathered and combined by a group
of people. Using Concept Indexes generated by different groups
to view the same document would result in different highlighting
of the document, and we can observe various perspectives reflected
by each Index.
Experiments are under way to evaluate the feasibility of this
approach. One of the promising applications of Concept Indexes
is query expansion and result filtering for Internet-based search.
We plan to integrate Concept Indexes into our agent-based collaborative
information seeking system in the future as a means for presentation
and navigation.
Information on the Web at:
http://orgwis.gmd.de/projects/Coins/
Please contact:
Keiichi Nakata - GMD
Tel: +49 2241 14 2317
E-mail: Keiichi.Nakata@gmd.de
