A knowledge-based system for prototypical reasoning

Abstract

In this work we present a knowledge-based system equipped with a hybrid, cognitively inspired architecture for the representation of conceptual information. The proposed system aims at extending the classical representational and reasoning capabilities of the ontology-based frameworks towards the realm of the prototype theory. It is based on a hybrid knowledge base, composed of a classical symbolic component (grounded on a formal ontology) with a typicality based one (grounded on the conceptual spaces framework). The resulting system attempts to reconcile the heterogeneous approach to the concepts in Cognitive Science with the dual process theories of reasoning and rationality. The system has been experimentally assessed in a conceptual categorisation task where common sense linguistic descriptions were given in input, and the corresponding target concepts had to be identified. The results show that the proposed solution substantially extends the representational and reasoning ‘conceptual’ capabilities of standard ontology-based systems.

Keywords:

• knowledge representation
• formal ontologies
• conceptual spaces
• common sense reasoning
• dual process theory
• prototypical reasoning

Acknowledgements

The authors kindly thank Leo Ghignone, for working to an earlier version of the system; Marcello Frixione, for discussions and advices on the theoretical aspects of this approach; the anonymous reviewers, whose valuable suggestions were helpful to improve the work; Manuela Sanguinetti, for her comments on a previous version of the article. We also thank the attendees of the ConChaMo 4 Workshop,²⁰ organised by the University of Helsinki, and the participants of the Spatial Colloquium Workshop organised by the Spatial Cognition Center of the University of Bremen²¹ for their comments and insights to initial versions of this work: in particular, we thank David Danks, Christian Freksa, Peter Gärdenfors, Ismo Koponen, and Paul Thagard. We especially thank Leonardo Lesmo, beloved friend and colleague no longer with us, who strongly encouraged the present line of research.

Funding

This work has been partly supported by the Ateneo-San Paolo project number TO_call03_2012_0046, The role of visual imagery in lexical processing (RVILP). The first author's work is also partially supported by the CNR F.A.C.I.L.E. project ICT.P08.003.001.

Notes

1. This is the case, for example, of exceptions to the inheritance mechanism.

2. For the Web Ontology Language, see http://www.w3.org/TR/owl-features/ and http://www.w3.org/TR/owl2-overview/, respectively.

3. In the present implementation we considered two possible types of informational components: the typical one (encoding prototypical knowledge) and the classical one (encoding information in terms of necessary and sufficient conditions). In particular, although in this case we mainly concentrate on representation and reasoning tenets coming from the prototype theory, the typical component can be considered general enough to encode many other forms of representational and reasoning mechanisms related to a wider spectrum of typicality theories such as, for example, the Exemplars theory (Murphy, 2002).

4. Therefore, the use of prototypical knowledge in cognitive tasks such as categorisation is not a fault of the human mind, as it could be the fact that people are prone to fallacies and reasoning errors (leaving aside the problem of establishing whether recurrent errors in reasoning could have a deeper ‘rationality’ within the general framework of cognition). For the same reason it is also a desired characteristics in the field of intelligent artificial systems.

5. Currently OWL and OWL 2 profiles are not expressive enough to perform the reasoning processes provided by the overall system. However, both language profiles are usable in their DL-safe characterisation to exploit taxonomical reasoning. Extending the expressivity of ontological formalisms and languages would be a long-term desideratum in order to enrich the ontological reasoning with more complex inference. To be more expressive and practically usable, a KR framework should provide an acceptable trade-off in terms of complexity. However, this is an open problem in Fuzzy and Non-Monotonic extensions of standard DLs.

6. Typical traits are selected based on statistically relevant information regarding a given concept, as posited by the Prototype Theory (Rosch, 1975). For example, the selection of the information regarding the typical colour of a rose (red) is given by the fact that roses are often red.

7. WordNet information is relevant in our system in that synset identifiers are used by both $\mathcal{S}1$ and $\mathcal{S}2$ as a lexical ground to access both the conceptual representations.

8. The output of $\mathcal{S}2$ cannot be wrong on a purely logical perspective, in that it is the result of a deductive process. The control strategy tries to implement a tradeoff between ontological inference and the output of $\mathcal{S}1$, which is more informative but also less reliable from a formal point of view. However, in next future we plan to explore different conciliation mechanisms to ground the overall control strategy.

9. The expected prototypical target category represents a gold standard, since it corresponds to the results provided within a psychological experimentation. In this experimentation 30 subjects were requested to provide the corresponding target concept for each description. The full list is available at the URL http://www.di.unito.it/radicion/datasets/cs_2014/stimuli.txt.

10. We also tried to extend our evaluation to the well-known semantic question-answering engine Wolfram-Alpha (https://www.wolframalpha.com). However, it was not possible to test the descriptions in that it explicitly disregards considering typicality-based queries. Namely, the only stimulus correctly categorised is that describing the target $\mathsf{c}\mathsf{a}\mathsf{t}$ as ‘The domestic feline.’.

11. The ontology is available at the URL http://www.di.unito.it/radicion/datasets/cs_2014/Naive_animal_ontology.owl

12. http://www.loa-cnr.it/ontologies/DOLCE-Lite.owl

13. This follows by observing that ${\mathsf{c}}_{\mathsf{0}}=\text{{whale}}$, $\mathsf{c}\mathsf{c}=\text{{whale-shark}}$; and $\text{{whale}}\subset \mathit{m}\mathit{a}\mathit{m}\mathit{m}\mathit{a}\mathit{l}$, while $\text{{whale-shark}}\subset \mathit{f}\mathit{i}\mathit{s}\mathit{h}$; and mammal and fish are disjoint.

14. http://www.cyc.com/platform/opencyc.

15. The full list of the second set of stimuli, containing the expected ‘prototypically correct’ category is available at the following URL: http://www.di.unito.it/radicion/datasets/cs_2014/stimuli.txt.

16. In our view the distinction classical vs. prototypical is ‘a-modal’ per se, for example both a typical and a classical conceptual information can be accessed and processed through different modalities (that is visual vs. auditory, etc.).

17. http://www.cogsci.rpi.edu/rsun/clarion.html

18. http://conceptnet5.media.mit.edu.

19. http://www.b2international.com/portal/snow-owl.

20. http://conceptualchange.it.helsinki.fi/workshops/conchamo4/.

21. http://www.sfbtr8.uni-bremen.de/en/home/.