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Some Notes on Extracting Linguistic Summaries Built with Epistemic Modalities and Natural Language Connectives of Equivalence

Katarzyniak R., Lorkiewicz W., Więcek D.

Computational Methods in Science and Technology

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2017

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Vol. 23, No. 1

19--41

EN

In this paper we deal with an original technically oriented model for cognitive semantics. As the expected area of application we focus on the process of extraction of modal linguistic summaries from data managed by autonomous components of ambient systems and intelligent environments. As such, the cognitive semantics is defined for a particular case of modal natural language statements with epistemic modalities. The statements of interest are built with natural language operators, representing epistemic modalities (related to the main cognitive states of knowledge certainty: full certainty, strong belief and epistemic possibility), and natural language connectives of equivalence. Furthermore, an approach to their effective processing by autonomous computational systems is designed. An internal architecture of the autonomous computational component is designed with respect to modular model for natural language processing with separate modules for epistemic and semantic memory storage and processing. An original theoretical concept underlying the model of semantic memory is a holon defined as a collection of complementary linguistic protoforms. Finally, we provide several illustrative computational examples of linguistic summaries’ extraction, based on artificial and real data.

2

Parallel fuzzy clustering for linguistic summaries

Smolińska M. K., Sosnowski Z. A.

Zeszyty Naukowe Politechniki Białostockiej. Informatyka

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2009

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Z. 4

139-150

PL

Z podsumowaniem lingwistycznym, jak i z predykatem rozmytym związana jest wartość prawdy. Możemy więc podsumowań lingwistycznych używać jako predykatów rozmytych. Podsumowanie postaci większość obiektów w populacji P jest podobna do obiektu oi wykorzystać możemy do znajdowania typowych wartości w populacji P, które to wykorzystuje rozmyty algorytm grupujący. Wadą tego algorytmu jest jego duża złożoność obliczeniowa. W celu przetwarzania dużej liczby danych zaimplementowaliśmy ten algorytm równolegle, korzystając ze standardu MPI do komunikacji między procesami działającymi na różnych procesorach. W tej pracy przedstawiamy algorytm równoległy i wyniki eksperymentów.

EN

The linguistic summaries have the associated truth value so they can be used as predicates. We use summaries of the form ”most objects in population P are similar to oi” to find typical values in population P. Then typical values are used in fuzzy clustering algorithm. Disadvantage of this algorithm is its complexity. For the purpose of processing the huge number of data, we decided to use parallel computing mechanism to implement this algorithm, and run it on the cluster machine. We use MPI (Message Passing Interface) to communicate between processes, which work on different processors. This paper presents this parallel algorithm and some results of experiments.

3

A Fuzzy Logic Based Approach to Linguistic Summaries of Databases

Kacprzyk J., Yager R. R., Zadrożny S.

International Journal of Applied Mathematics and Computer Science

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2000

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Vol. 10, no 4

813-834

EN

In this paper, we present basic ideas and perspectives related to the use of fuzzy logic for the derivation of linguistic summaries of data (databases). We concentrate on the issue of how to measure the goodness of a linguistic summary, and on how to embed data summarization within the fuzzy querying environment, for an effective and efficient implementation. In particular, we propose how to efficiently implement Kacprzyk and Yager's (2000) new quality indicators of linguistic summaries to derive summaries via Kacprzyk and Zadrozny's (1994; 1995a; 1995b; 1996) fuzzy querying add-on. Finally, we present an implementation for deriving linguistic summaries of a sales database at a computer retailer, and show how the linguistic summaries obtained can be useful for supporting decisions of the business owner.