Refining Concepts by Machine Learning

Menšík, Marek; Duží, Marie; Albert, Adam; Patschka, Vojtěch; Pajr, Miroslav

doi:10.13053/cys-23-3-3242

Cited by 8 publications

(21 citation statements)

References 7 publications

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“…Next, the set of the selected constructions serves as an input for machine learning techniques, in particular, the Inductive heuristics module (see [12]), to obtain plausible hypotheses that explicate the given simple concept (Fig. 3).…”

Section: Fig 2 Pre-processing and Formalisation Of Textual Resourcesmentioning

confidence: 99%

“…Explications of atomic concepts extracted from one textual document have been introduced in [12]. By applying a supervised machine-learning method to multiple textual resources, we obtain several different explications (see [10]).…”

Section: Introductionmentioning

confidence: 99%

“…Section 2 briefly summarises the main principles of Transparent Intensional Logic and introduces TIL constructions that serve as a concept defining formalism. In Section 3, we summarise previous results on the topic, namely the system of seeking relevant textual resources as presented in [12]. In Section 4, we introduce the theory of association rules, while in Section 5, the whole algorithm of their computing is described.…”

Section: Introductionmentioning

confidence: 99%

“…For details on TIL theory of concepts see[6, § 2.2] 8. In this section we briefly recapitulate the results as presented in[12].A. Albert et al / Search for Appropriate Textual Information Sources…”

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confidence: 97%

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Albert¹,

Duží²,

Menšík³

et al. 2020

Frontiers in Artificial Intelligence and Applications

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In this paper, we deal with the support in the search for appropriate textual sources. Users ask for an atomic concept that is explicated using machine learning methods applied to different textual sources. Next, we deal with the so-obtained explications to provide even more useful information. To this end, we apply the method of computing association rules. The method is one of the data-mining methods used for information retrieval. Our background theory is the system of Transparent Intensional Logic (TIL); all the concepts are formalised as TIL constructions.

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Section: Fig 2 Pre-processing and Formalisation Of Textual Resourcesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 97%

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Albert¹,

Duží²,

Menšík³

et al. 2020

Frontiers in Artificial Intelligence and Applications

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“…This part was introduced in [1], [2], [3]. The second part concerns machine learning and searching for relevant text sources which was introduces in [4], [5], [6], [7]. Now our system presuppose that all the inputs are transformed into TIL-Script.…”

Section: The Brief Introduction Of Tilus For Appropriate Textual Info...mentioning

confidence: 99%

Conceptual Framework for the Conversion of Text Document into TIL-Script

Číhalová

Menšík

2022

Frontiers in Artificial Intelligence and Applications

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The paper deals with the introduction of TILUS tool for the needs of appropriate textual information sources retrieval and natural language processing. TILUS tool presupposed up to now that all the data are formalized in TIL-Script, the computational variant of Transparent Intensional Logic (TIL). We outline the general proposal of utilizing the Stanford typed dependencies representation for semi-automate conversion of natural language into TIL-Script. In order to be able to correctly solve this problem, we also introduce our universal conceptualization which is able to cover the thematic variations of processed texts.

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Modelling dynamic behaviour of agents in a multiagent world: Logical analysis of Wh-questions and answers

Číhalová

Duží

2022

Logic Journal of the IGPL

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In a multiagent and multi-cultural world, the fine-grained analysis of agents’ dynamic behaviour, i.e. of their activities, is essential. Dynamic activities are actions that are characterized by an agent who executes the action and by other participants of the action. Wh-questions on the participants of the actions pose a difficult particular challenge because the variability of the types of possible answers to such questions is huge. To deal with the problem, we propose the analysis and classification of Wh-questions apt for agents’ communication in a multiagent system (MAS). Our proposal of such a system consists of agents who communicate with their fellow agents by messaging so that each autonomous agent, though resource-bounded, can make less or more rational decisions to meet its own and collective goals. In addition, by communicating with other fellow agents and their environment, agents can learn new concepts and enrich their ontology so that their behaviour is dynamic. We aim to make a general proposal of the system so that the ‘envelope’ of agents’ messages can be formalized in any MAS standard, be it The Foundation for Intelligent Physical Agents - Agent Communication Language (FIPA-ACL) or Knowledge Query and Manipulation Language (KQML). Yet, the content of messages is encoded in a formalized natural language. To this end, we apply Transparent Intensional Logic (TIL) with its procedural semantics which is particularly apt for a fine-grained analysis in which all the semantically salient features of natural language can be plausibly formalized. In this paper, we concentrate on analysing the content of query messages, particularly the content of those that encode Wh-questions and the answers to them. We also summarize TIL deduction system that makes it possible to answer such questions in an intelligent way. Linguists distinguish several subtypes of Wh-questions. Though the linguistic classification is helpful, it is not always suitable for agents’ communication and reasoning. We need the classification based on a logical analysis of Wh-questions so that the agents can infer possible answers to such questions rather than only looking for them by keywords. This paper aims to apply an appropriate classification of the logical types of Wh-questions and the analysis of such questions; we concentrate in particular on questions concerning the participants of activities. The application of these results to the analysis of processes and events based on verb valency frames is another novelty of the paper.

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Refining Concepts by Machine Learning

Cited by 8 publications

References 7 publications

Search for Appropriate Textual Information Sources

Search for Appropriate Textual Information Sources

Conceptual Framework for the Conversion of Text Document into TIL-Script

Modelling dynamic behaviour of agents in a multiagent world: Logical analysis of Wh-questions and answers

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