We have developed a novel method, Grounded Intersubjective Concept Analysis (GICA), for the analysis and visualization of individual differences in language use and conceptualization. The GICA method first employs a conceptual survey or a text mining step to elicit to elicit from varied groups of individuals the particular ways in which terms and associated concepts are used among the individuals. The subsequent analysis and visualization reveals potential underlying groupings of subjects, objects and contexts. In order to demonstrate the use of the GICA method, we present the results of two case studies. In the first case, a GICA analysis of health-related concepts is conducted. In the second one, the State of the Union addresses by US presidents are analyzed. The GICA method can be used, for instance, to support education of heterogeneous audiences, public planning processes and participatory design, conflict resolution, environmental problem solving, interprofessional and interdisciplinary communication, product development processes, mergers of organizations, and building enhanced knowledge representations in semantic web.

1. Subjects on Objects in Contexts:
Using GICA method to quantify
epistemological subjectivity
Timo Honkela1, Juha Raitio1, Krista Lagus1,
Ilari T. Nieminen1, Nina Honkela2, Mika Pantzar3
1
Aalto University
(former Helsinki University of Technology)
Department of Information and Computer Science
(former Neural Networks Research Center, Adaptive Informatics Research Center)
2
University of Helsinki
3
National Consumer Research Center
Finland

2. Subjects on Objects in Contexts:
Using GICA method to quantify
epistemological subjectivity
Timo Honkela Juha Raitio Krista Lagus
Ilari T. Nieminen Nina Honkela Mika Pantzar

3. Traditional representation of meaning:
Generalized (non-contextual, non-subjective)
Gaines: “Designing Visual Languages for Description Logics”
http://pages.cpsc.ucalgary.ca/~gaines/reports/KBS/VLL/

4. Meaning is contextual
red wine
red skin
red shirt
Gärdenfors: Conceptual Spaces
Hardin Color for Philosophers

5. Meaning is contextual
WHITE
SNOW -
WHITE?

6. Meaning is contextual
● “Small”, “big”
● “White house”
● “Get”
● “Every” - “Every Swede is tall/blond”
● etc. etc.

7. Learning meaning from context
● Self-Organizing Semantic Maps
(Ritter & Kohonen 1989)
● Latent Semantic Analysis
● Latent Dirichlet Allocation
● WordICA
● etc. etc.
Honkela, Pulkki & Kohonen 1995

8. Learning meaning from context:
Maps of words in Grimm fairy tales
Honkela, Pulkki & Kohonen 1995

9. Meaning is subjective

10. Meaning is subjective
● Good
● Fair
● Useful
A proper theory of
● Scientific meaning has to take
● Democratic this into account.
● Sustainable (opposite to the view
given by V. Cherkassky
● etc. about an hour ago)

11. Modeling variation of
meaning in a community of agents
Honkela: ICANN 1993
Steels, Kaplan, Vogt, et al.:
Language games
(Lindh-Knuutila, Lagus & Honkela, SAB'06)
Related to e.g. Steels and Vogt on language games

12. Intersubjective Concept Spaces
concept symbol concept symbol
(shared)
space space space space
context
C1 S1 C2 S2
observations
signal
Sender d Receiver (Honkela, Könönen,
(agent 1) (agent 2) Lindh-Knuutila &
Paukkeri 2008)

13. Intersubjective Concept Spaces
(Honkela, Könönen, Lindh-Knuutila & Paukkeri 2008)
Ci: N­dimensional ξ: si ∈ Si → C
metric concept An individual
space mapping function
from symbols to
concepts Observing f1 and after symbol
S: symbol space,
selection process, agent 1
The vocabulary of an
φi: Si → D communicates a symbol s*
agent that consists of
An individual to agent 2 as signal d. When agent
discrete symbols
mapping from agent 2 observes d, it maps it to some
i's vocabulary to the s2 ∈ S2 by using the function φ ­11.
λ : Ci × Cj → R, i ≠ j signal space D and Then it maps the symbol to some
A distance between an inverse mapping point in its concept space by using
two points in the φ­1 i from the signal ξ2. If this point is close to its
concept spaces of space to the symbol observation f2 in the sense of λ,
different agents space the communication process has
succeeded.

14. Gary B. Fogel
11th of June, 2012
WCCI 2012

15. GICA:
Grounded
Intersubjective
Concept
Analysis
Description of the method

16. Subjectifying: adding subjective
views into object-context matrices
Outcome: Subject-Object-Context (SOC) Tensors

17. More on subjectification
● A central question in GICA is how to obtain the
data on subjectivity for expanding an object-
context matrix into the tensor that accounts
additionally for subjectivity.
● The basic idea is that for each element in the
object-context matrix one needs several
subjective evaluations.
● Specifically, the GICA data collection measures
for each subject si the relevance xijk of
an object oj in a context ck

18. Potential sources for subjectification
● Conceptual surveys:
● individual assessment of contextual
appropriateness
● Text mining:
● statistics of word/phrase-context patterns
● Empirical psychology:
● reaction times, etc.
● Brain research

19. Flattening: unfolding 3-way tensor
for traditional 2-way analysis

20. GICA:
Grounded
Intersubjective
Concept
Analysis
Examples of use

21. Case 1: Wellbeing concepts
● A conceptual survey was conducted among the
participants of the EIT ICT Labs activity “Wellbeing
Innovation Camp” that took place between 26th and
29th of October 2010 in Vierumäki, Finland.
● The participants were asked to fill in a data matrix
that consisted of the objects as rows and the
contexts as columns.
● Each individual’s task was to determine how
strongly an object is associated with a context, using
Likert scale from 1 to 5

22. Data collection
CONTEXTS:
OBJECTS:
Relaxation
Happiness
Fitness
Wellbeing
SUBJECTS:
Event participants

23. MDS: Objects x Subjects
Fitness

24. NeRV: Objects x Subjects
Fitness
NeRV:
J. Venna, J. Peltonen, K. Nybo, H. Aidos, and S. Kaski. Information Retrieval Perspective to Nonlinear
Dimensionality Reduction for Data Visualization. Journal of Machine Learning Research, 11:451-490, 2010.

25. SOM: Objects x Subjects

26. SOM: Distribution of contexts

27. SOM: Contexts

28. Case 2: State of the Union
Addresses
● In this case, text mining is used for populating
the Subject-Object-Context tensor
● This took place by calculating the frequencies
on how often a subject uses an object word in
the context of a context word
● Context window of 30 words

29. SOM: Subjects (presidents)

30. SOM: Objects x Subjects

31. Analysis of the word 'health'

32. Related research and
future plans

33. Our related research on subjectivity:
User-specific difficulty assessment
Paukkeri, Ollikainen &
Honkela, Information
Processing & Management,
2012

34. Interoperability
● Current situation:
Formalization and harmonization of knowledge
representations (e.g. using XML)
● Future possibility:
Meaning negotiation between systems
based on SOC tensors and further
developments
Context data is important!

35. Enhanced communication, democratic and
participatory processes

36. Collaboration opportunities
● Theoretical work
● Interdisciplinary: brain research, psychology,
sociology, organization research, etc.
● Methodological
– Formulation in different theoretical frameworks
– Analogical development with crisp>fuzzy:
“objective”>subjective
● Experimental
● Case studies
● Research visits, tutorials, workshops
● GICA workshop/summer school in 2013 in Finland