Reasoning About Relations
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Relations in Categories
Relations in Categories Stefan Milius A thesis submitted to the Faculty of Graduate Studies in partial fulfilment of the requirements for the degree of Master of Arts Graduate Program in Mathematics and Statistics York University Toronto, Ontario June 15, 2000 Abstract This thesis investigates relations over a category C relative to an (E; M)-factori- zation system of C. In order to establish the 2-category Rel(C) of relations over C in the first part we discuss sufficient conditions for the associativity of horizontal composition of relations, and we investigate special classes of morphisms in Rel(C). Attention is particularly devoted to the notion of mapping as defined by Lawvere. We give a significantly simplified proof for the main result of Pavlovi´c,namely that C Map(Rel(C)) if and only if E RegEpi(C). This part also contains a proof' that the category Map(Rel(C))⊆ is finitely complete, and we present the results obtained by Kelly, some of them generalized, i. e., without the restrictive assumption that M Mono(C). The next part deals with factorization⊆ systems in Rel(C). The fact that each set-relation has a canonical image factorization is generalized and shown to yield an (E¯; M¯ )-factorization system in Rel(C) in case M Mono(C). The setting without this condition is studied, as well. We propose a⊆ weaker notion of factorization system for a 2-category, where the commutativity in the universal property of an (E; M)-factorization system is replaced by coherent 2-cells. In the last part certain limits and colimits in Rel(C) are investigated. -
Simple Laws About Nonprominent Properties of Binary Relations
Simple Laws about Nonprominent Properties of Binary Relations Jochen Burghardt jochen.burghardt alumni.tu-berlin.de Nov 2018 Abstract We checked each binary relation on a 5-element set for a given set of properties, including usual ones like asymmetry and less known ones like Euclideanness. Using a poor man's Quine-McCluskey algorithm, we computed prime implicants of non-occurring property combinations, like \not irreflexive, but asymmetric". We considered the laws obtained this way, and manually proved them true for binary relations on arbitrary sets, thus contributing to the encyclopedic knowledge about less known properties. Keywords: Binary relation; Quine-McCluskey algorithm; Hypotheses generation arXiv:1806.05036v2 [math.LO] 20 Nov 2018 Contents 1 Introduction 4 2 Definitions 8 3 Reported law suggestions 10 4 Formal proofs of property laws 21 4.1 Co-reflexivity . 21 4.2 Reflexivity . 23 4.3 Irreflexivity . 24 4.4 Asymmetry . 24 4.5 Symmetry . 25 4.6 Quasi-transitivity . 26 4.7 Anti-transitivity . 28 4.8 Incomparability-transitivity . 28 4.9 Euclideanness . 33 4.10 Density . 38 4.11 Connex and semi-connex relations . 39 4.12 Seriality . 40 4.13 Uniqueness . 42 4.14 Semi-order property 1 . 43 4.15 Semi-order property 2 . 45 5 Examples 48 6 Implementation issues 62 6.1 Improved relation enumeration . 62 6.2 Quine-McCluskey implementation . 64 6.3 On finding \nice" laws . 66 7 References 69 List of Figures 1 Source code for transitivity check . .5 2 Source code to search for right Euclidean non-transitive relations . .5 3 Timing vs. universe cardinality . -
What Are Kinship Terminologies, and Why Do We Care?: a Computational Approach To
View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Kent Academic Repository What are Kinship Terminologies, and Why do we Care?: A Computational Approach to Analysing Symbolic Domains Dwight Read, UCLA Murray Leaf, University of Texas, Dallas Michael Fischer, University of Kent, Canterbury, Corresponding Author, [email protected] Abstract Kinship is a fundamental feature and basis of human societies. We describe a set of computat ional tools and services, the Kinship Algebra Modeler, and the logic that underlies these. Thes e were developed to improve how we understand both the fundamental facts of kinship, and h ow people use kinship as a resource in their lives. Mathematical formalism applied to cultural concepts is more than an exercise in model building, as it provides a way to represent and exp lore logical consistency and implications. The logic underlying kinship is explored here thro ugh the kin term computations made by users of a terminology when computing the kinship r elation one person has to another by referring to a third person for whom each has a kin term relationship. Kinship Algebra Modeler provides a set of tools, services and an architecture to explore kinship terminologies and their properties in an accessible manner. Keywords Kinship, Algebra, Semantic Domains, Kinship Terminology, Theory Building 1 1. Introduction The Kinship Algebra Modeller (KAM) is a suite of open source software tools and services u nder development to support the elicitation and analysis of kinship terminologies, building al gebraic models of the relations structuring terminologies, and instantiating these models in lar ger contexts to better understand how people pragmatically interpret and employ the logic of kinship relations as a resource in their individual and collective lives. -
Symmetric Relations and Cardinality-Bounded Multisets in Database Systems
Symmetric Relations and Cardinality-Bounded Multisets in Database Systems Kenneth A. Ross Julia Stoyanovich Columbia University¤ [email protected], [email protected] Abstract 1 Introduction A relation R is symmetric in its ¯rst two attributes if R(x ; x ; : : : ; x ) holds if and only if R(x ; x ; : : : ; x ) In a binary symmetric relationship, A is re- 1 2 n 2 1 n holds. We call R(x ; x ; : : : ; x ) the symmetric com- lated to B if and only if B is related to A. 2 1 n plement of R(x ; x ; : : : ; x ). Symmetric relations Symmetric relationships between k participat- 1 2 n come up naturally in several contexts when the real- ing entities can be represented as multisets world relationship being modeled is itself symmetric. of cardinality k. Cardinality-bounded mul- tisets are natural in several real-world appli- Example 1.1 In a law-enforcement database record- cations. Conventional representations in re- ing meetings between pairs of individuals under inves- lational databases su®er from several consis- tigation, the \meets" relationship is symmetric. 2 tency and performance problems. We argue that the database system itself should pro- Example 1.2 Consider a database of web pages. The vide native support for cardinality-bounded relationship \X is linked to Y " (by either a forward or multisets. We provide techniques to be im- backward link) between pairs of web pages is symmet- plemented by the database engine that avoid ric. This relationship is neither reflexive nor antire- the drawbacks, and allow a schema designer to flexive, i.e., \X is linked to X" is neither universally simply declare a table to be symmetric in cer- true nor universally false. -
On Effective Representations of Well Quasi-Orderings Simon Halfon
On Effective Representations of Well Quasi-Orderings Simon Halfon To cite this version: Simon Halfon. On Effective Representations of Well Quasi-Orderings. Other [cs.OH]. Université Paris-Saclay, 2018. English. NNT : 2018SACLN021. tel-01945232 HAL Id: tel-01945232 https://tel.archives-ouvertes.fr/tel-01945232 Submitted on 5 Dec 2018 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. On Eective Representations of Well asi-Orderings ese` de doctorat de l’Universite´ Paris-Saclay prepar´ ee´ a` l’Ecole´ Normale Superieure´ de Cachan au sein du Laboratoire Specication´ & Verication´ Present´ ee´ et soutenue a` Cachan, le 29 juin 2018, par Simon Halfon Composition du jury : Dietrich Kuske Rapporteur Professeur, Technische Universitat¨ Ilmenau Peter Habermehl Rapporteur Maˆıtre de Conferences,´ Universite´ Paris-Diderot Mirna Dzamonja Examinatrice Professeure, University of East Anglia Gilles Geeraerts Examinateur Associate Professor, Universite´ Libre de Bruxelles Sylvain Conchon President´ du Jury Professeur, Universite´ Paris-Sud Philippe Schnoebelen Directeur de these` Directeur de Recherche, CNRS Sylvain Schmitz Co-encadrant de these` Maˆıtre de Conferences,´ ENS Paris-Saclay ` ese de doctorat ED STIC, NNT 2018SACLN021 Acknowledgements I would like to thank the reviewers of this thesis for their careful proofreading and pre- cious comment. -
Math 475 Homework #3 March 1, 2010 Section 4.6
Student: Yu Cheng (Jade) Math 475 Homework #3 March 1, 2010 Section 4.6 Exercise 36-a Let ͒ be a set of ͢ elements. How many different relations on ͒ are there? Answer: On set ͒ with ͢ elements, we have the following facts. ) Number of two different element pairs ƳͦƷ Number of relations on two different elements ) ʚ͕, ͖ʛ ∈ ͌, ʚ͖, ͕ʛ ∈ ͌ 2 Ɛ ƳͦƷ Number of relations including the reflexive ones ) ʚ͕, ͕ʛ ∈ ͌ 2 Ɛ ƳͦƷ ƍ ͢ ġ Number of ways to select these relations to form a relation on ͒ 2ͦƐƳvƷͮ) ͦƐ)! ġ ͮ) ʚ ʛ v 2ͦƐƳvƷͮ) Ɣ 2ʚ)ͯͦʛ!Ɛͦ Ɣ 2) )ͯͥ ͮ) Ɣ 2) . b. How many of these relations are reflexive? Answer: We still have ) number of relations on element pairs to choose from, but we have to 2 Ɛ ƳͦƷ ƍ ͢ include the reflexive one, ʚ͕, ͕ʛ ∈ ͌. There are ͢ relations of this kind. Therefore there are ͦƐ)! ġ ġ ʚ ʛ 2ƳͦƐƳvƷͮ)Ʒͯ) Ɣ 2ͦƐƳvƷ Ɣ 2ʚ)ͯͦʛ!Ɛͦ Ɣ 2) )ͯͥ . c. How many of these relations are symmetric? Answer: To select only the symmetric relations on set ͒ with ͢ elements, we have the following facts. ) Number of symmetric relation pairs between two elements ƳͦƷ Number of relations including the reflexive ones ) ʚ͕, ͕ʛ ∈ ͌ ƳͦƷ ƍ ͢ ġ Number of ways to select these relations to form a relation on ͒ 2ƳvƷͮ) )! )ʚ)ͯͥʛ )ʚ)ͮͥʛ ġ ͮ) ͮ) 2ƳvƷͮ) Ɣ 2ʚ)ͯͦʛ!Ɛͦ Ɣ 2 ͦ Ɣ 2 ͦ . d. -
PROBLEM SET THREE: RELATIONS and FUNCTIONS Problem 1
PROBLEM SET THREE: RELATIONS AND FUNCTIONS Problem 1 a. Prove that the composition of two bijections is a bijection. b. Prove that the inverse of a bijection is a bijection. c. Let U be a set and R the binary relation on ℘(U) such that, for any two subsets of U, A and B, ARB iff there is a bijection from A to B. Prove that R is an equivalence relation. Problem 2 Let A be a fixed set. In this question “relation” means “binary relation on A.” Prove that: a. The intersection of two transitive relations is a transitive relation. b. The intersection of two symmetric relations is a symmetric relation, c. The intersection of two reflexive relations is a reflexive relation. d. The intersection of two equivalence relations is an equivalence relation. Problem 3 Background. For any binary relation R on a set A, the symmetric interior of R, written Sym(R), is defined to be the relation R ∩ R−1. For example, if R is the relation that holds between a pair of people when the first respects the other, then Sym(R) is the relation of mutual respect. Another example: if R is the entailment relation on propositions (the meanings expressed by utterances of declarative sentences), then the symmetric interior is truth- conditional equivalence. Prove that the symmetric interior of a preorder is an equivalence relation. Problem 4 Background. If v is a preorder, then Sym(v) is called the equivalence rela- tion induced by v and written ≡v, or just ≡ if it’s clear from the context which preorder is under discussion. -
Foundations of Relations and Kleene Algebra
Introduction Foundations of Relations and Kleene Algebra Aim: cover the basics about relations and Kleene algebras within the framework of universal algebra Peter Jipsen This is a tutorial Slides give precise definitions, lots of statements Decide which statements are true (can be improved) Chapman University which are false (and perhaps how they can be fixed) [Hint: a list of pages with false statements is at the end] September 4, 2006 Peter Jipsen (Chapman University) Relation algebras and Kleene algebra September 4, 2006 1 / 84 Peter Jipsen (Chapman University) Relation algebras and Kleene algebra September 4, 2006 2 / 84 Prerequisites Algebraic properties of set operation Knowledge of sets, union, intersection, complementation Some basic first-order logic Let U be a set, and P(U)= {X : X ⊆ U} the powerset of U Basic discrete math (e.g. function notation) P(U) is an algebra with operations union ∪, intersection ∩, These notes take an algebraic perspective complementation X − = U \ X Satisfies many identities: e.g. X ∪ Y = Y ∪ X for all X , Y ∈ P(U) Conventions: How can we describe the set of all identities that hold? Minimize distinction between concrete and abstract notation x, y, z, x1,... variables (implicitly universally quantified) Can we decide if a particular identity holds in all powerset algebras? X , Y , Z, X1,... set variables (implicitly universally quantified) These are questions about the equational theory of these algebras f , g, h, f1,... function variables We will consider similar questions about several other types of algebras, a, b, c, a1,... constants in particular relation algebras and Kleene algebras i, j, k, i1,.. -
SNAC: an Unbiased Metric Evaluating Topology Recognize Ability of Network Alignment
SNAC: An Unbiased Metric Evaluating Topology Recognize Ability of Network Alignment Hailong Li, Naiyue Chen* School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China [email protected], [email protected] ABSTRACT Network alignment is a problem of finding the node mapping between similar networks. It links the data from separate sources and is widely studied in bioinformation and social network fields. The critical difference between network alignment and exact graph matching is that the network alignment considers node mapping in non-isomorphic graphs with error tolerance. Researchers usually utilize AC (accuracy) to measure the performance of network alignments which comparing each output element with the benchmark directly. However, this metric neglects that some nodes are naturally indistinguishable even in single graphs (e.g., nodes have the same neighbors) and no need to distinguish across graphs. Such neglect leads to the underestimation of models. We propose an unbiased metric for network alignment that takes indistinguishable nodes into consideration to address this problem. Our detailed experiments with different scales on both synthetic and real-world datasets demonstrate that the proposed metric correctly reflects the deviation of result mapping from benchmark mapping as standard metric AC does. Comparing with the AC, the proposed metric effectively blocks the effect of indistinguishable nodes and retains stability under increasing indistinguishable nodes. Keywords: metric; network alignment; graph automorphism; symmetric nodes. * Corresponding author 1. INTRODUCTION Network, or graph1, can represent complex relationships of objects (e.g., article reference relationship, protein-protein interaction) because of its flexibility. However, flexibility sometimes exhibits an irregular side, leading to some problems related to graph challenges. -
Relations II
CS 441 Discrete Mathematics for CS Lecture 22 Relations II Milos Hauskrecht [email protected] 5329 Sennott Square CS 441 Discrete mathematics for CS M. Hauskrecht Cartesian product (review) •Let A={a1, a2, ..ak} and B={b1,b2,..bm}. • The Cartesian product A x B is defined by a set of pairs {(a1 b1), (a1, b2), … (a1, bm), …, (ak,bm)}. Example: Let A={a,b,c} and B={1 2 3}. What is AxB? AxB = {(a,1),(a,2),(a,3),(b,1),(b,2),(b,3)} CS 441 Discrete mathematics for CS M. Hauskrecht 1 Binary relation Definition: Let A and B be sets. A binary relation from A to B is a subset of a Cartesian product A x B. Example: Let A={a,b,c} and B={1,2,3}. • R={(a,1),(b,2),(c,2)} is an example of a relation from A to B. CS 441 Discrete mathematics for CS M. Hauskrecht Representing binary relations • We can graphically represent a binary relation R as follows: •if a R b then draw an arrow from a to b. a b Example: • Let A = {0, 1, 2}, B = {u,v} and R = { (0,u), (0,v), (1,v), (2,u) } •Note: R A x B. • Graph: 2 0 u v 1 CS 441 Discrete mathematics for CS M. Hauskrecht 2 Representing binary relations • We can represent a binary relation R by a table showing (marking) the ordered pairs of R. Example: • Let A = {0, 1, 2}, B = {u,v} and R = { (0,u), (0,v), (1,v), (2,u) } • Table: R | u v or R | u v 0 | x x 0 | 1 1 1 | x 1 | 0 1 2 | x 2 | 1 0 CS 441 Discrete mathematics for CS M. -
Binary Relations and Preference Modeling
Chapter 2 Binary Relations and Preference Modeling 2.1. Introduction This volume is dedicated to concepts, results, procedures and software aiming at helping people make a decision. It is then natural to investigate how the various courses of action that are involved in this decision compare in terms of preference. The aim of this chapter is to propose a brief survey of the main tools and results that can be useful to do so. The literature on preference modeling is vast. This can first be explained by the fact that the question of modeling preferences occurs in several disciplines, e.g. – in Economics, where one tries to model the preferences of a ‘rational consumer’ [e.g. DEB 59]; – in Psychology in which the study of preference judgments collected in experi- ments is quite common [KAH 79, KAH 81]; – in Political Sciences in which the question of defining a collective preference on the basis of the opinion of several voters is central [SEN 86]; – in Operational Research in which optimizing an objective function implies the definition of a direction of preference [ROY 85]; and – in Artificial Intelligence in which the creation of autonomous agents able to take decisions implies the modeling of their vision of what is desirable and what is less so [DOY 92]. Chapter written by Denis BOUYSSOU and Philippe VINCKE. 49 50 Decision Making Moreover, the question of preference modeling can be studied from a variety of perspectives [BEL 88], including: –a normative perspective, where one investigates preference models that are likely to lead to a ‘rational behavior’; –a descriptive perspective, in which adequate models to capture judgements ob- tained in experiments are sought; or –a prescriptive perspective, in which one tries to build a preference model that is able to lead to an adequate recommendation. -
LECTURE 1 Relations on a Set 1.1. Cartesian Product and Relations. A
LECTURE 1 Relations on a set PAVEL RU˚ZIˇ CKAˇ Abstract. We define the Cartesian products and the nth Cartesian powers of sets. An n-ary relation on a set is a subset of its nth Carte- sian power. We study the most common properties of binary relations as reflexivity, transitivity and various kinds of symmetries and anti- symmetries. Via these properties we define equivalences, partial orders and pre-orders. Finally we describe the connection between equivalences and partitions of a given set. 1.1. Cartesian product and relations. A Cartesian product M1 ×···× Mn of sets M1,...,Mn is the set of all n-tuples hm1,...,mni satisfying mi ∈ Mi, for all i = {1, 2,...,n}. The Cartesian product of n-copies of a single set M is called an nth-Cartesian power. We denote the nth-Cartesian power of M by M n. In particular, M 1 = M and M 0 is the one-element set {∅}. An n-ary relation on a set M is a subset of M n. Thus unary relations correspond to subsets of M, binary relations to subsets of M 2 = M × M, etc. 1.2. Binary relations. As defined above, a binary relation on a set M is a subset of the Cartesian power M 2 = M × M. Given such a relation, say R ⊂ M × M, we will usually use the notation a R b for ha, bi∈ R, a, b ∈ M. Let us list the some important properties of binary relations. By means of them we define the most common classes of binary relations, namely equivalences, partial orders and quasi-orders.