issues in knowledge representation and reasoning

Honkela: Issues in Knowledge Representation and Reasoning

Issues inKnowledge Representation

and Reasoning

Timo Honkela

Helsinki University of Technology (TKK)Adaptive Informatics Research Centre

[email protected]

Invited talk atN.C.S.R. “Demokritos”

Athens, Greece7th of June 2006


On human reasoning

● Traditionally knowledge representation and reasoning have been based on certain analytical means, such as first order predicate logic and related formalisms.

● Human beings reason in a probabilistic manner.● Reasoning is highly contextualised by relevant

prior knowledge and belief.● A division into a heuristic system and

an analytical system can be made.


Heuristic and analytical system, 1

● The heuristic system has evolved early, it is shared with animals, it is rapid and parallel, has high capacity and is pragmatic (cf e.g. J. Evans).

● The heuristic system is a vast “probability machine”.


Heuristic and analytical system, 2

● Analytical system has evolved recently in an evolutionary sense and it enables logical reasoning.

● Therefore, considering the analytical level is not enough as reasoning and knowledge is based on the underlying experiential domain.


Methodological issues

● Division into analytical and heuristic systems can be also seen in knowledge engineering methodologies.

● Analytical: rulebased systems, semantic nets, frames, logic programming, ontologies, etc.

● Heuristic: statistical pattern recognition, neural networks, statistical machine learning, etc.


Traditional AI approach

Agents Language Model of the world World

= = =


Emergentist approach

Agents LanguageWorld

Model of the world


Problems of purelyanalytical approaches

● There is a need for methods that would be more successful as building blocks for knowledge engineering and natural language processing systems as the ones traditionally used

● Two kinds of problems of analytical / logicbased formalisms (including Semantic Web and ontologies): one quantitative and many qualitative


Quantitative problem

● The efforts required to collect explicit knowledge representation in many domains requires considerable amount of human work

● This conclusion can be made based on numerous examples of development of expert systems and natural language processing applications


Qualitative problems

● Even if the knowledge acquisition problem were solved with machine learning techniques, much more burning qualitative problems remain

● In traditional AI systems, the symbolic representations are not grounded: the semantics are, at best, very shallow (this is an intentional contradiction with the terminology commonly used )

● Real knowledge is grounded in experience and requires access to the pattern recognition processes that are probabilistic in nature


We tend to perceive the worldas a collection of objects,their qualities and relationships.

However, the perceptual inputis a continuous flow of patterns.

The process in which the patternsare interpreted as objects is farfrom straightforward.

The conceptualisations that we use are an emergent result ofcomplex interactions betweenpeople and the world. This includesbiological, psychological, cognitive,social, etc, aspects.


Qualitative problems, cont'd● Interpretation of words/symbols for human

beings is always subjective to some degree.● When suitably high agreement is reached, one

can name it as the state of intersubjectivity.● Intersubjectivity is, however, always a matter of

degree and thus real objectivity in a strict sense cannot be reached.

● Traditional AI representations do not have proper means for dealing with this issue at all.


Qualitative problems, cont'd

● Conceptualisation is formed in an iterativeprocess in which a large number of interactingelements influence each other with no centralcontrol.

● Efforts of harmonisation or standardisation can be successful only to some degree. The higher the degree, the higher the costs.

● The costs include both development costs andimplementation (learning) costs.


History and change/variation:

What a word refersto nowadays can bedifferent what it wasa year or ten years ago.

One should also notecertain primacy ofpragmatics oversemantics.


From Semantic Webtowards Pragmatic Web

● Collecting skeletonlike semantic descriptionwithout grounding and without considerationof the use of the knowledge is an effort withserious limitations.

● Similarly, development of information systems in general has reached a local minimum: the systems do not understand contents being processed (no grounding)

● I suggest that those efforts put into Semantic Web projects are put into Pragmatic Web development


Pragmatic Web: key elements

● Grounding, contextuality and multimodality● Modeling individual variation in interpretation


Contextuality


SOM of Words: Grimm Fairy Tales

(Honkela, Pulkki, Kohonen, 1995)

Emergentimplicitcategories

VERBS

NOUNS


Emergentimplicitcategories:areas of inanimate and animate nouns

(Honkela, Pulkki, Kohonen, 1995)


Multimodality


color edge

n-gram user provided features

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Individual variation


Illustration: map of words incontext; study of individual differences

● N = 11; researchers at Lund University Cognitive Science, 11th of June, 2003

● Experimental setting unprofessional;only for demonstration purposes

● People were asked to judge if a number of adjectives (9) were natural in the context of some nouns (8) using a scale from1 to 5; as a result we have a 3dimensionalmatrix (adjectives x nouns x persons)


42

1 1 1

12

1

11

11 1

11

1

1

1

2

22

1

12

BLUE

YELLOW

GREEN


2

121

12

yellow

green, blue

romantic, darkmystical

happy, angry

light

PERSONNO 11


yellow

green

romantic

happylight

PERSONNO 4

mystical

angry

dark

blue


yellow

green rom.

happylight

mystical

angry

dark

blue

PERSONNO 4

PERSONNO 4

PERSONNO 4

PERSONNO 1

yellow

dark

light

dark

dark


THANK YOU!

issues in knowledge representation and reasoning

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