Analogy between concepts

Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Pr...
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Gespeichert in:

Autor*in:	Barbot, N. [verfasserIn] Miclet, L. [verfasserIn] Prade, H. [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2019

Schlagwörter:	Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis

Übergeordnetes Werk:	Enthalten in: Artificial intelligence - Amsterdam : Elsevier, 1970, 275, Seite 487-539
Übergeordnetes Werk:	volume:275 ; pages:487-539

DOI / URN:	10.1016/j.artint.2019.06.008

Katalog-ID:	ELV002748673

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520			\|a Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework.
650		4	\|a Analogy
650		4	\|a Analogical reasoning
650		4	\|a Analogical proportion
650		4	\|a Analogy in lattices
650		4	\|a Formal concept analysis
700	1		\|a Miclet, L. \|e verfasserin \|4 aut
700	1		\|a Prade, H. \|e verfasserin \|4 aut
773	0	8	\|i Enthalten in \|t Artificial intelligence \|d Amsterdam : Elsevier, 1970 \|g 275, Seite 487-539 \|h Online-Ressource \|w (DE-627)266884822 \|w (DE-600)1468341-6 \|w (DE-576)075961520 \|x 1872-7921 \|7 nnns
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936	b	k	\|a 54.72 \|j Künstliche Intelligenz
951			\|a AR
952			\|d 275 \|h 487-539

Indexfelder

author_variant	n b nb l m lm h p hp
matchkey_str	article:18727921:2019----::nlgbten
hierarchy_sort_str	2019
bklnumber	54.72
publishDate	2019
allfields	10.1016/j.artint.2019.06.008 doi (DE-627)ELV002748673 (ELSEVIER)S0004-3702(18)30186-3 DE-627 ger DE-627 rda eng 004 690 DE-600 54.72 bkl Barbot, N. verfasserin aut Analogy between concepts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework. Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis Miclet, L. verfasserin aut Prade, H. verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 275, Seite 487-539 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:275 pages:487-539 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 275 487-539
spelling	10.1016/j.artint.2019.06.008 doi (DE-627)ELV002748673 (ELSEVIER)S0004-3702(18)30186-3 DE-627 ger DE-627 rda eng 004 690 DE-600 54.72 bkl Barbot, N. verfasserin aut Analogy between concepts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework. Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis Miclet, L. verfasserin aut Prade, H. verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 275, Seite 487-539 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:275 pages:487-539 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 275 487-539
allfields_unstemmed	10.1016/j.artint.2019.06.008 doi (DE-627)ELV002748673 (ELSEVIER)S0004-3702(18)30186-3 DE-627 ger DE-627 rda eng 004 690 DE-600 54.72 bkl Barbot, N. verfasserin aut Analogy between concepts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework. Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis Miclet, L. verfasserin aut Prade, H. verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 275, Seite 487-539 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:275 pages:487-539 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 275 487-539
allfieldsGer	10.1016/j.artint.2019.06.008 doi (DE-627)ELV002748673 (ELSEVIER)S0004-3702(18)30186-3 DE-627 ger DE-627 rda eng 004 690 DE-600 54.72 bkl Barbot, N. verfasserin aut Analogy between concepts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework. Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis Miclet, L. verfasserin aut Prade, H. verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 275, Seite 487-539 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:275 pages:487-539 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 275 487-539
allfieldsSound	10.1016/j.artint.2019.06.008 doi (DE-627)ELV002748673 (ELSEVIER)S0004-3702(18)30186-3 DE-627 ger DE-627 rda eng 004 690 DE-600 54.72 bkl Barbot, N. verfasserin aut Analogy between concepts 2019 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework. Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis Miclet, L. verfasserin aut Prade, H. verfasserin aut Enthalten in Artificial intelligence Amsterdam : Elsevier, 1970 275, Seite 487-539 Online-Ressource (DE-627)266884822 (DE-600)1468341-6 (DE-576)075961520 1872-7921 nnns volume:275 pages:487-539 GBV_USEFLAG_U SYSFLAG_U GBV_ELV GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2008 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2088 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2118 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz AR 275 487-539
language	English
source	Enthalten in Artificial intelligence 275, Seite 487-539 volume:275 pages:487-539
sourceStr	Enthalten in Artificial intelligence 275, Seite 487-539 volume:275 pages:487-539
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis
dewey-raw	004
isfreeaccess_bool	false
container_title	Artificial intelligence
authorswithroles_txt_mv	Barbot, N. @@aut@@ Miclet, L. @@aut@@ Prade, H. @@aut@@
publishDateDaySort_date	2019-01-01T00:00:00Z
hierarchy_top_id	266884822
dewey-sort	14
id	ELV002748673
language_de	englisch
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author	Barbot, N.
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topic_title	004 690 DE-600 54.72 bkl Analogy between concepts Analogy Analogical reasoning Analogical proportion Analogy in lattices Formal concept analysis
topic	ddc 004 bkl 54.72 misc Analogy misc Analogical reasoning misc Analogical proportion misc Analogy in lattices misc Formal concept analysis
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title	Analogy between concepts
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title_full	Analogy between concepts
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title_auth	Analogy between concepts
abstract	Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework.
abstractGer	Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework.
abstract_unstemmed	Reasoning by analogy plays an important role in human thinking, in exploring parallels between situations. It enables us to explain by comparing, to draw plausible conclusions, or to create new devices or concepts by transposing old ones in new contexts. A basic form of analogy, called Analogical Proportion (AP), describes a particular relation between four objects of the same kind, e.g. “A calf is to a bull as a foal is to a stallion”. It is only recently that researchers have started to study APs in a formal way and to use their properties in different tasks of artificial intelligence (AI). This paper follows this line of research. Specifically, we are interested in giving the definition and some properties of an AP in lattices, a widely used structure in AI. We give general results before focusing on Concept Lattices, with the goal to investigate how analogical reasoning could be introduced in the framework of Formal Concept Analysis (FCA). This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. To finish, we relate them to another form of analogy, called Relational Proportion, which involves two universes of discourse, e.g. “Carlsen is to chess as Mozart is to music”, which leads to the more compact way of saying “Carlsen is the Mozart of chess”, which is not anymore a relation between four objects of the same kind, but can be interpreted as well in FCAs framework.
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title_short	Analogy between concepts
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up_date	2024-07-06T17:19:11.720Z
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This leads us to define an AP between formal concepts and to give algorithms to compute them, but also to point to special subcontexts, called analogical complexes. They are themselves organized as a lattice, and we show that they are closely related to APs between concepts, while not needing the complete construction of the lattice. 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