Post on 12-Sep-2021
Honkela: Issues in Knowledge Representation and Reasoning
Issues inKnowledge Representation
and Reasoning
Timo Honkela
Helsinki University of Technology (TKK)Adaptive Informatics Research Centre
timo.honkela@tkk.fi
Invited talk atN.C.S.R. “Demokritos”
Athens, Greece7th of June 2006
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
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”.
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
Traditional AI approach
Agents Language Model of the world World
= = =
Honkela: Issues in Knowledge Representation and Reasoning
Emergentist approach
Agents LanguageWorld
Model of the world
Honkela: Issues in Knowledge Representation and Reasoning
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
Honkela: Issues in Knowledge Representation and Reasoning
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
Honkela: Issues in Knowledge Representation and Reasoning
Honkela: Issues in Knowledge Representation and Reasoning
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
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
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.
Honkela: Issues in Knowledge Representation and Reasoning
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
Honkela: Issues in Knowledge Representation and Reasoning
Pragmatic Web: key elements
● Grounding, contextuality and multimodality● Modeling individual variation in interpretation
Honkela: Issues in Knowledge Representation and Reasoning
Contextuality
Honkela: Issues in Knowledge Representation and Reasoning
SOM of Words: Grimm Fairy Tales
(Honkela, Pulkki, Kohonen, 1995)
Emergentimplicitcategories
VERBS
NOUNS
Honkela: Issues in Knowledge Representation and Reasoning
Emergentimplicitcategories:areas of inanimate and animate nouns
(Honkela, Pulkki, Kohonen, 1995)
Honkela: Issues in Knowledge Representation and Reasoning
Multimodality
Honkela: Issues in Knowledge Representation and Reasoning
color edge
n-gram user provided features
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Honkela: Issues in Knowledge Representation and Reasoning
Individual variation
Honkela: Issues in Knowledge Representation and Reasoning
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)
Honkela: Issues in Knowledge Representation and Reasoning
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Honkela: Issues in Knowledge Representation and Reasoning
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Honkela: Issues in Knowledge Representation and Reasoning
THANK YOU!