1. Knowledge codification and abstraction.
Scientometric approaches
2-12-2010, KIMO
Eva Ortoll / Àlex Lopez

2. Context and Objetives
Based on the I-Space theoretical framework, we try to explain the
knowledge codification and abstraction of a specific research
discipline by scientometric techniques, the idea is to gain
knowledge in the use of these techniques to apply them to the
ATLAS project (in its documents, databases, etc) at CERN.
The objective is to obtain an objective way to measure knowledge
codification and abstraction at ATLAS experiment, completing and
comparing the results with those obtained with the interviews.

3. Research Questions
Rq1: How codified is the knowledge in a scientific
discipline?
Rq2: How abstract is the knowledge in a scientific
discipline?
Rq3: How we can explain knowledge creation and
diffusion in a scientific discipline?

4. Data gathering
We will work with the ISI database (in this sense we would
retrieved the complete register of an article in terms of:
authors, title, abstract, source, references, etc)
We have decided to choose three disciplines to apply the
techniques: Physics, Management and Information Science

5. Initial approach: Mapping Knowledge Domains
“Knowledge mapping is locating a knowledge item (a node in a
scalable network) within the categorization scheme developed
for a given knowledge domain” (Boisot).
Mapping knowledge domains (MKD) are used for making and
visualizating the structure and dynamics of disciplines or
networks (Borner, 2003; Boyack 2004), and different
techniques are used for it.
Because we work, initially, with a bibliographic dataset ,we’ll
focus those related with literature/science mapping.

6. Science mapping can be done at several levels of granularity:
disciplines (information science), research fields (science studies),
research subfields (scientometrics) and research topics (citation
analysis; scientific collaboration) (Besselaar, 2006).
A research front allow us to obtain a picture of the main
current research topics in a discipline, how the topics are
related with each other and how they evolve over the time.
Research fronts provide insights in how new scientific
knowledge is incorporated into existing research (Upham
2010, Small 2003, Kuhn 1970).

7. We propose to work with the smallest unit of analysis, the “research
topic” as an expression of a “knowledge item” within a scientific
discipline, even we are aware that a “research topic” is a simplistic
way to look at the knowledge item or knowledge assets. Perhaps, a
second step analysis of the information around a “research topic”
will allow us to define better what a KA is in the context of
scientific publications.

8. Approaches
Main idea
Bibliographic coupling
(Kessler, 1963)
links documents that
reference the same
set of cited
references
Co-citation analysis
(Marshakova, 1973,
Small, 1973):
links documents that It is assumed that co-citation gives the background of a
are cited together.
research front.
The use of co-citation only has been criticized to draw
a research front (lost of some topics, super citing
effect, predominance of theoretical and review
papers....)
Co-word or word-coocurrences (Callon,
1986).
Semantic similarity
of research fields.
It has been criticized as and indicative of a research
front because the “words” can adopt different meanings
depending on the context they are used, also with
questions related to polysemus words or words with
little semantic value (Leydesdorff 2004, 2010; Janssen
2008). Some new approaches (comibining clustering
and factoring) are used to solve polysemy and synomy
problems (Kawkkle 2009).
Combination of citationbased approaches with
text-based approaches
(Zitt, 2010; Janssens,
Co-occurences of
words in references,
or in titles and
references. Several
It is assumed the co-word highlighted better the new
concepts, and the citations the knowledge background.
The hybrid method allows to draw a more real
delineation of research fronts
It is assumed that bibliographic coupling gives the more
dynamic and “actual” structure of a research front.

9. From Boyack; K; Klavans, R. JASIST 2010

10. Approaches
Main idea
Field to field citations *
(Buter et al 2010)
Citations form one scientific
field to another are used to
measure
interdisciplinary
connections
Network Authors
(Newman 2004;
Lambiotte 2009)
* useful to analysis the degree of abstraction?
Useful to identify the creation of a new
discipline, but also to identify knowledge
that is generated in one field and is used in
another one*
With co-authors networks one can measure
the influence of one author, or group of
authors (named communities) in a research
front

11. How could we measure the degree of codification and abstraction?
Degree
CODIFICATION
ABSTRACTION* (II): by citations analysis
High
A well defined groups of topics over
the years. The relations within the
topics and subtopics is clear.
The research topics and their relations
are clearly identifiable
The research topics of a research front have been
“absorbed**” by others research fronts. Papers of a
research front are highly cited by others research
fronts.
Medium
A well defined group of topics over
the years that generated subtopics,
with a variations over the years
Some papers of a research front are cited by others
research fronts.
Low
A lot of topics exists without a clear
structure of relations within them
The papers of a research front are not cited by others
research fronts.
** (Merton concept of “obliteration by incorporation”).”, we can use i.e the half-live of citations, as longer as
it is, less abstraction: the concept has not been yet “absorbed” by others (Upham 2010) .

12. How could we measure the degree of codification and abstraction?
Degree
CODIFICATION
ABSTRACTION* (I): by topic relations
High
A well defined groups of topics over
the years. The relations within the
topics and subtopics are clear.
The research topics and their
relations are clearly identifiable
A well defined group of topics over the years. The
same topic appears in different thematic clusters. One
topic, not related with others at the beginning is
related with other subtopics over the years.
Can we say that some kind of centrality could work as an
indicator of abstraction?
Medium
A well defined group of topics over
the years that generated subtopics,
with a variations over the years
The topics between different thematic clusters have a
weak relations, even that, the relation between topics
of two thematic clusters exist.
Low
A lot of topics exists without a clear
structure of relations within them
There’s not relation within subtopics of different
thematic clusters
All the topics only appear in an isolated thematic
clusters.
*Further analysis on abstraction need to be done based on the relations and proximity between different
fields, also the use of specific topics in different or distant thematic journals.

13. Our proposal
We’ll use, as a first step, the co-word co-occurrence technique
using title and abstract text of ISI records. We do it because it
seems the most suitable one to translated latter to CERN
databases.
Even though, we could also use some more literature-based
techniques to delineate how knowledge is used and transmitted
within a research field.
In a second step, we’d want to explore the above mentioned
techniques to evaluated the degree of abstraction and codification
in a research field using bibliographic information