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Knowledge and Information Systems
Erschienen in: Knowledge and Information Systems 2/2019

07.09.2018 | Regular Paper

Similarity reasoning in formal concept analysis: from one- to many-valued contexts

verfasst von: Anna Formica

Erschienen in: Knowledge and Information Systems | Ausgabe 2/2019

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Abstract

In this paper, concept similarity in formal concept analysis (FCA) with many-valued contexts is addressed. In particular, this work focuses on FCA many-valued contexts where attribute values are intervals (FCA with interordinal scaling), here referred to as IFCA. IFCA is based on interval type-2 fuzzy sets, which provide a simplification of the more general type-2 fuzzy sets. In this work, a method for evaluating concept similarity in IFCA is proposed, which is a problem that has not been adequately investigated in the literature, although the increasing interest in the combination of FCA with fuzzy sets. Note that the topic addressed in this paper is presented by providing simple examples in order to reach a broad audience of readers.
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Nächster Artikel Recurrent collective classification

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Literatur
1.
Akmal S, Batres R (2013) A methodology for developing manufacturing process ontologies. J Jpn Ind Manag Assoc 64:303–316
2.
Akmal S, Shih L, Batres R (2014) Ontology-based similarity for product information retrieval. Comput Ind 65:91–107CrossRef
3.
Alam M, Buzmakov A, Napoli A, Sailanbayev A (2015) Revisiting pattern structures for structured attribute sets. In: Proceedings of international conference on concept lattices and their applications, Clermont-Ferrand, France, 13–16 October 2015, CEUR workshop proceedings, pp 241–252
4.
Alam M, Napoli A (2015) Interactive exploration over RDF data using formal concept analysis. In: IEEE international conference on data science and advanced analytics (DSAA), pp 1–10
5.
Bai L, Liu M (2008) A fuzzy-set based semantic similarity matching algorithm for web service. In: Proceedings of the IEEE international conference on services computing, vol 2. IEEE Computer Society
6.
Belohlávek R, Vychodil V (2005) What is a fuzzy concept lattice? In: Belohlávek R, Snásel V (eds) Proceedings of concept lattices and their applications (CLA), Olomouc, Czech Republic, 7–9 September 2005, pp 34–45
7.
Belohlávek R, Outrata J, Vychodil V (2008) Fast factorization by similarity of fuzzy concept lattices with hedges. Int J Found Comput Sci 19(2):255–269MathSciNetMATHCrossRef
8.
Berners-Lee T, Hendler J, Lassila O (2001) The semantic web. Sci Am 284(5):34–43CrossRef
9.
Bilgin A, Hagras H, Alghazzawi D, Malibari A, Alhaddad MJ (2015) Employing an enhanced interval approach to encode words into linear general type-2 fuzzy sets for computing with words applications. In: IEEE international conference on fuzzy systems (FUZZ-IEEE), Istanbul, Turkey
10.
11.
De Luca A, Termini S (1972) A definition of nonprobabilistic entropy in the setting of fuzzy sets theory. Inf Comput 20:301–312MathSciNetMATH
12.
Djouadi Y, Prade H (2009) Interval-valued fuzzy formal concept analysis. In: Rauch et al (eds) Foundations of intelligent systems, ISMIS 2009, LNAI, vol 5722, pp 592–601
13.
Dubois D, Prade H (2012) Fundamentals of fuzzy sets. Springer, New YorkMATH
14.
Ferr S, Cellier P (2016) Graph-FCA in practice. In: International conference on conceptual structures (ICCS), pp 107–121
15.
16.
Formica A (2008) Concept similarity in formal concept analysis: an information content approach. Knowl Based Syst 21(1):80–87MathSciNetCrossRef
17.
Formica A, Pourabbas E (2009) Content based similarity of geographic classes organized as partition hierarchies. Knowl Inf Syst 20(2):221–241CrossRef
18.
Formica A (2010) Concept similarity in fuzzy formal concept analysis for semantic web. Int J Uncertain Fuzziness Knowl Based Syst 18(2):153–167MathSciNetCrossRef
19.
Formica A (2012) Semantic web search based on rough sets and fuzzy formal concept analysis. Knowl Based Syst 26:40–47CrossRef
20.
Formica A (2013) Similarity reasoning for the semantic web based on fuzzy concept lattices: an informal approach. Inf Syst Front 15(3):511–520CrossRef
21.
Ganter B, Wille R (1999) Formal concept analysis: mathematical foundations. Springer, Berlin. ISBN 978-3-540-62771-5MATHCrossRef
22.
Ganter B, Kuznetsov SO (2001) Pattern structures and their projections. In: Delugach HS, Stumme G (eds) International conference on conceptual structures (ICCS). LNAI, vol 2120. Springer, pp 129–142
23.
Hao M, Mendel JM (2016) Encoding words into normal interval type-2 fuzzy sets: HM approach. IEEE Trans Fuzzy Syst 24(4):865–879CrossRef
24.
Hitzler P, Krötzsch M, Rudolph S (2009) Foundations of semantic web technologies. Chapman & Hall/CRC, LondonCrossRef
25.
Hitzler P (2011) What’s happening in semantic web ... and what FCA could have to do with it. In: 9th International conference on formal concept analysis (ICFCA) Nicosia, Cyprus, 2–6 May 2011. LNCS 6628, Springer, pp 18–23
26.
Jaccard P (1908) Nouvelles recherches sur la distribution florale. Bull Soc Vauddes Sci Nat 44:223
27.
Jay N, Nuemi G, Gadreau M, Quantin C (2013) A data mining approach for grouping and analyzing trajectories of care using claim data: the example of breast cancer. BMC Med Inform Decis Mak 13:130CrossRef
28.
Kaytoue M, Kuznetsov SO, Napoli A, Duplessis S (2011) Mining gene expression data with pattern structures in formal concept analysis. Inf Sci 181(10):1989–2001MathSciNetCrossRef
29.
Kirchberg M, Leonardi E, Tan YS, Link S, Ko RKL, Lee BS (2012) Formal concept discovery in semantic web data. In: Domenach F, Ignatov DI, Poelmans J (eds) International conference on formal concept analysis (ICFCA). Springer, Berlin, pp 164–179CrossRef
30.
Keler C (2007) Similarity measurement in context. In: Kokinov B (ed) CONTEXT’07. LNAI, vol 4635. Springer, Berlin, pp 277–290
31.
32.
Li C, Li J, He M (2016) Concept lattice compression in incomplete contexts based on K-medoids clustering. Int J Mach Learn Cybern 7(4):539–552MathSciNetCrossRef
33.
34.
Lin D (1998) An information-theoretic definition of similarity. In: Proceedings of the international conference on machine learning, Madison, Wisconsin, USA, Morgan Kaufmann, pp 296–304
35.
Liang Q, Mendel JM (2000) Interval type-2 fuzzy logic systems: theory and design. IEEE Trans Fuzzy Syst 8(5):535–550CrossRef
36.
Liu F, Mendel JM (2008) Encoding words into interval Type-2 fuzzy sets using an interval approach. IEEE Trans Fuzzy Syst 16(6):1503–1521CrossRef
37.
Maarek YS, Berry DM, Kaiser GE (1991) An information retrieval approach for automatically constructing software libraries. IEEE Trans Softw Eng 17(8):800–813CrossRef
38.
Mendel JM, John RI, Liu F (2006) Interval type-2 fuzzy logic systems made simple. IEEE Trans Fuzzy Syst 14(6):808–821CrossRef
39.
40.
Mendel JM (2007) Computing with words: Zadeh, Turing, Popper and Occam. IEEE Comput Intell Mag 2(4):10–17CrossRef
41.
Mendel JM, Wu D (2008) Perceptual reasoning for perceptual computing. IEEE Trans Fuzzy Syst 16(6):1550–1564CrossRef
42.
Mendel JM, Wu D (2010) Perceptual computing: aiding people in making subjective judgments. Wiley, New YorkCrossRef
43.
Mendel JM (2015) Type-2 fuzzy sets and systems: a retrospective. Inform Spektrum 38(6):523–532CrossRef
44.
Mendel JM (2016) A comparison of three approaches for estimating (synthesizing) an interval type-2 fuzzy set model of a linguistic term for computing with words. Granul Comput 1:59–69CrossRef
45.
Park S, Suresh NC, Jeong B (2008) Sequence-based clustering for Web usage mining: a new experimental framework and ANN-enhanced K-means algorithm. Data Knowl Eng 65(3):512–543CrossRef
46.
Poelmans J, Ignatov DI, Kuznetsov SO, Dedene G (2013) Formal concept analysis in knowledge processing: a survey on applications. Expert Syst Appl 40(16):6538–6560CrossRef
47.
Resnik P (1995) Using information content to evaluate semantic similarity in a taxonomy. In: Proceedings of the fourteenth international joint conference on artificial intelligence, (IJCAI), Montral Qubec, Canada, 20–25 August 1995, Morgan Kaufmann, pp 448–453
48.
Resnik P (1999) Semantic similarity in a taxonomy: an information-based measure and its application to problems of ambiguity in natural language. J Artif Intell Res 11:95–130MATHCrossRef
49.
Rodriguez A, Egenhofer M (2004) Comparing geospatial entity classes: an asymmetric and context-dependent similarity measure. Int J Geogr Inf Sci 18(3):229–256CrossRef
50.
51.
Safaeipour H, Zarandi MHF, Turksen IB (2013) Developing type-2 fuzzy FCA for similarity reasoning in the semantic web. Joint IFSA world congress and NAFIPS annual meeting (IFSA/NAFIPS). IEEE, pp 1477–1482
52.
Sertkaya B (2010) A survey on how description logic ontologies benefit from FCA. In: Proceedings of international conference on concept lattices and their applications (CLA), Seville, Spain, 19–21 October 2010, pp 2–21
53.
Singh PK, Aswani Kumar C, Li J (2016) Knowledge representation using interval-valued fuzzy formal concept lattice. Soft Comput 20:1485–1502MATHCrossRef
54.
Stumme G, Maedche A (2001) FCA-MERGE: bottom-up merging of ontologies. In: Proceedings of international joint conference on artificial intelligence (IJCAI), Seattle, USA, pp 225–234
55.
Tho QT, Hui SC, Cheuk A, Fong M, Cao TH (2006) Automatic fuzzy ontology generation for semantic web. IEEE Trans Knowl Data Eng 18(6):842–856CrossRef
56.
Wang JH, Hao J (2006) A new version of 2-tuple fuzzy linguistic representation model for computing with words. IEEE Trans Fuzzy Syst 14(3):435–445CrossRef
57.
58.
59.
Wu D, Mendel JM (2009) A comparative study of ranking methods, similarity measures and uncertainty measures for interval type-2 fuzzy sets. Inf Sci 179:1169–1192MathSciNetCrossRef
60.
Yao Y (2017) Interval sets and three-way concept analysis in incomplete contexts. Int J Mach Learn Cybern 8(1):3–20CrossRef
61.
62.
63.
Zadeh LA (1996) Fuzzy logic = computing with words. IEEE Trans Fuzzy Syst 4(2):103–111CrossRef
64.
Zhao Y, Li J, Liu W, Xu W (2017) Cognitive concept learning from incomplete information. Int J Mach Learn Cyber 8(1):159–170CrossRef
Metadaten
Titel
Similarity reasoning in formal concept analysis: from one- to many-valued contexts
verfasst von
Anna Formica
Publikationsdatum
07.09.2018
Verlag
Springer London
Erschienen in
Knowledge and Information Systems / Ausgabe 2/2019
Print ISSN: 0219-1377
Elektronische ISSN: 0219-3116
DOI
https://doi.org/10.1007/s10115-018-1252-4

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