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A Framework for Modelling Real-World Knowledge Capable of Obtaining Answers to Fuzzy and Flexible Searches

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Computational Intelligence (IJCCI 2013)

Part of the book series: Studies in Computational Intelligence ((SCI,volume 613))

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Abstract

The Internet has become a place where massive amounts of information and data are being generated every day. This information is most of the times stored in a non-structured way, but the times it is structured in databases it cannot be retrieved by using easy fuzzy queries: we need human intervention to determine how the non-fuzzy information stored needs to be combined and processed to answer a fuzzy query. We present a web interface for posing fuzzy and flexible queries and a framework. Our framework allows to represent non-fuzzy concepts, fuzzy concepts and relations between them, giving the programmer the capability to model any real-world knowledge. It is this representation in the framework’s language what it uses to (1) determine how to answer the query without any human intervention and (2) provide the search engine with the information it needs to present the user a friendly and easy to use query form. We expect this work contributes to the development of more human-oriented fuzzy search engines.

This work is partially supported by research projects DESAFIOS10 (TIN2009-14599-C03-00) funded by Ministerio Ciencia e Innovación of Spain, PROMETIDOS (P2009/TIC-1465) funded by Comunidad Autónoma de Madrid and Research Staff Training Program (BES-2008-008320) funded by the Spanish Ministry of Science and Innovation. It is partially supported too by the Universidad Politécnica de Madrid entities Departamento de Lenguajes, Sistemas Informáticos e Ingeniería de Software and Facultad de Informática.

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Notes

  1. 1.

    We say that it is a more declarative programming language because it removes the necessity to specify the flow control in most cases, but the programmer still needs to know if the interpreter or compiler implements depth or breadth-first search strategy and left-to-right or any other literal selection rule.

  2. 2.

    As usually, a n-ary function \(\hat{F}\) is called monotonic in the idefine-th argument (\(i\le n\)), if \(x \le x'\) implies \(\hat{F}(x_1,\ldots ,x_{i-1}, x, x_{i+1}, \ldots , x_n) \le \hat{F}(x_1, \ldots , x_{i-1}, x', x_{i+1}, \ldots ,x_n)\) and a function is called monotonic if it is monotonic in all arguments.

  3. 3.

    Note that the above definition of aggregation operators subsumes all kinds of minimum, maximum or mean operators.

  4. 4.

    The domain of an interpretation is the set of all atoms in the Herbrand Base (interpretations are total functions), although for readability reasons we present interpretations as sets of pairs \((A, ( \mathsf {p},~\mathsf {v}))\) where \(A \in \mathbf {HB}\) and \( ( \mathsf {p},~\mathsf {v})\in \mathbf {KT}{\setminus }\{\bot \}\) (we omit those atoms whose interpretation is the truth value \(\bot \)).

  5. 5.

    Please note that the types in our framework are not the same as the types used in Eqs. 8–14. Nevertheless, our types are subsets of this ones. We justify in the paragraph below this one why we need this fine-grained type control.

  6. 6.

    Be careful, we are not saying that the spanish food is 0.7 similar to the mediterranean one. You need to add another clause with that information if you wanna say that too.

  7. 7.

    The meaning of this “by default” is explained too in the paragraphs after this one.

  8. 8.

    Please note that we not remove the original condition, so we can combine conditions introduced by the semantics of a clause with the conditions introduced by one or more tails.

  9. 9.

    We use indistinctively ’,’ and \(\wedge \) because the first one is the Prolog symbol for conjunction.

  10. 10.

    [(valIn, valOut)] is basically a piecewise function definition, where each two contiguous points represent a piece.

  11. 11.

    This “only for one sequence of two contiguous points” means that we generate one clause of the form in Eq. 29 for each piece defined by two contiguous points.

  12. 12.

    We include two examples here so if one builds a program by taking all the examples in the contribution the rule in Eq. 35 does not fail to obtain answers because it has not enough information to infer results.

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Authors and Affiliations

  1. The Babel Research Group, Facultad de Informática, Universidad Politécnica de Madrid, Madrid, Spain

    Víctor Pablos-Ceruelo & Susana Munoz-Hernandez

Authors
  1. Víctor Pablos-Ceruelo
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  2. Susana Munoz-Hernandez
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Correspondence to Susana Munoz-Hernandez .

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Editors and Affiliations

  1. Images, Signals and Intelligence Systems Laboratory, University PARIS-EST Creteil (UPEC), Créteil, France

    Kurosh Madani

  2. Departamento de Engenharia Informatica Polo II - Pinhal de Marrocos, University of Coimbra, Coimbra, Portugal

    António Dourado

  3. aSEEB-ISR-IST Av. Rovisco Pais, 1, Technical University of Lisbon (IST), Lisbon, Portugal

    Agostinho Rosa

  4. Rua do Vale de Chaves, Estefanilha, Polytechnic Institute of Setúbal / INSTICC, Setúbal, Portugal

    Joaquim Filipe

  5. Polish Academy of Sciences, Systems Research Institute, Warsaw, Poland

    Janusz Kacprzyk

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Pablos-Ceruelo, V., Munoz-Hernandez, S. (2016). A Framework for Modelling Real-World Knowledge Capable of Obtaining Answers to Fuzzy and Flexible Searches. In: Madani, K., Dourado, A., Rosa, A., Filipe, J., Kacprzyk, J. (eds) Computational Intelligence. IJCCI 2013. Studies in Computational Intelligence, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-23392-5_16

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  • DOI: https://doi.org/10.1007/978-3-319-23392-5_16

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