Relevant bibliographies by topics / Relation Graphs

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Relation Graphs'

Author: Grafiati
Published: 6 September 2023

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Journal articles on the topic "Relation Graphs"

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Gholap, P. S., and V. E. Nikumbh. "TOPOLOGICAL SPACES GENERATED BY GRAPH." Jnanabha 52, no. 01 (2022): 01–07. http://dx.doi.org/10.58250/jnanabha.2022.52101.

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n this paper we discuss topological spaces generated by simple graphs using adjacency relation and non adjacency relation on vertices. We establish important results showing relations between complete graph and discrete topological space. We also discuss the topological spaces related to complete graphs, isomorphic graphs and study their properties. Further we discuss the interior and closure operators and their properties. Our motivation is to give an fundamental step toward linkage between topology and graph so as to study different aspects of graphs in terms of topological properties
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Ke, Xiangyu, Arijit Khan, and Francesco Bonchi. "Multi-relation Graph Summarization." ACM Transactions on Knowledge Discovery from Data 16, no. 5 (October 31, 2022): 1–30. http://dx.doi.org/10.1145/3494561.

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Graph summarization is beneficial in a wide range of applications, such as visualization, interactive and exploratory analysis, approximate query processing, reducing the on-disk storage footprint, and graph processing in modern hardware. However, the bulk of the literature on graph summarization surprisingly overlooks the possibility of having edges of different types. In this article, we study the novel problem of producing summaries of multi-relation networks, i.e., graphs where multiple edges of different types may exist between any pair of nodes. Multi-relation graphs are an expressive model of real-world activities, in which a relation can be a topic in social networks, an interaction type in genetic networks, or a snapshot in temporal graphs. The first approach that we consider for multi-relation graph summarization is a two-step method based on summarizing each relation in isolation, and then aggregating the resulting summaries in some clever way to produce a final unique summary. In doing this, as a side contribution, we provide the first polynomial-time approximation algorithm based on the k -Median clustering for the classic problem of lossless single-relation graph summarization. Then, we demonstrate the shortcomings of these two-step methods, and propose holistic approaches, both approximate and heuristic algorithms, to compute a summary directly for multi-relation graphs. In particular, we prove that the approximation bound of k -Median clustering for the single relation solution can be maintained in a multi-relation graph with proper aggregation operation over adjacency matrices corresponding to its multiple relations. Experimental results and case studies (on co-authorship networks and brain networks) validate the effectiveness and efficiency of the proposed algorithms.
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Ratheesh, K. P. "On Soft Graphs and Chained Soft Graphs." International Journal of Fuzzy System Applications 7, no. 2 (April 2018): 85–102. http://dx.doi.org/10.4018/ijfsa.2018040105.

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Soft set theory has a rich potential for application in many scientific areas such as medical science, engineering and computer science. This theory can deal uncertainties in nature by parametrization process. In this article, the authors explore the concepts of soft relation on a soft set, soft equivalence relation on a soft set, soft graphs using soft relation, vertex chained soft graphs and edge chained soft graphs and investigate various types of operations on soft graphs such as union, join and complement. Also, it is established that every fuzzy graph is an edge chained soft graph.
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Guo, Yunfei, Fei Yin, Wei Feng, Xudong Yan, Tao Xue, Shuqi Mei, and Cheng-Lin Liu. "Social Relation Reasoning Based on Triangular Constraints." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 1 (June 26, 2023): 737–45. http://dx.doi.org/10.1609/aaai.v37i1.25151.

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Social networks are essentially in a graph structure where persons act as nodes and the edges connecting nodes denote social relations. The prediction of social relations, therefore, relies on the context in graphs to model the higher-order constraints among relations, which has not been exploited sufficiently by previous works, however. In this paper, we formulate the paradigm of the higher-order constraints in social relations into triangular relational closed-loop structures, i.e., triangular constraints, and further introduce the triangular reasoning graph attention network (TRGAT). Our TRGAT employs the attention mechanism to aggregate features with triangular constraints in the graph, thereby exploiting the higher-order context to reason social relations iteratively. Besides, to acquire better feature representations of persons, we introduce node contrastive learning into relation reasoning. Experimental results show that our method outperforms existing approaches significantly, with higher accuracy and better consistency in generating social relation graphs.
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Koch, Sebastian. "Unification of Graphs and Relations in Mizar." Formalized Mathematics 28, no. 2 (July 1, 2020): 173–86. http://dx.doi.org/10.2478/forma-2020-0015.

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Summary A (di)graph without parallel edges can simply be represented by a binary relation of the vertices and on the other hand, any binary relation can be expressed as such a graph. In this article, this correspondence is formalized in the Mizar system [2], based on the formalization of graphs in [6] and relations in [11], [12]. Notably, a new definition of createGraph will be given, taking only a non empty set V and a binary relation E ⊆ V × V to create a (di)graph without parallel edges, which will provide to be very useful in future articles.
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Yang, Wenjie, Jianlin Zhang, Jingju Cai, and Zhiyong Xu. "Relation Selective Graph Convolutional Network for Skeleton-Based Action Recognition." Symmetry 13, no. 12 (November 30, 2021): 2275. http://dx.doi.org/10.3390/sym13122275.

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Graph convolutional networks (GCNs) have made significant progress in the skeletal action recognition task. However, the graphs constructed by these methods are too densely connected, and the same graphs are used repeatedly among channels. Redundant connections will blur the useful interdependencies of joints, and the overly repetitive graphs among channels cannot handle changes in joint relations between different actions. In this work, we propose a novel relation selective graph convolutional network (RS-GCN). We also design a trainable relation selection mechanism. It encourages the model to choose solid edges to work and build a stable and sparse topology of joints. The channel-wise graph convolution and multiscale temporal convolution are proposed to strengthening the model’s representative power. Furthermore, we introduce an asymmetrical module named the spatial-temporal attention module for more stable context modeling. Combining those changes, our model achieves state-of-the-art performance on three public benchmarks, namely NTU-RGB+D, NTU-RGB+D 120, and Northwestern-UCLA.
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Flippen, Christopher, Allison H. Moore, and Essak Seddiq. "Quotients of the Gordian and H(2)-Gordian graphs." Journal of Knot Theory and Its Ramifications 30, no. 05 (April 2021): 2150037. http://dx.doi.org/10.1142/s0218216521500371.

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The Gordian graph and H(2)-Gordian graphs of knots are abstract graphs whose vertex sets represent isotopy classes of unoriented knots, and whose edge sets record whether pairs of knots are related by crossing changes or H(2)-moves, respectively. We investigate quotients of these graphs under equivalence relations defined by several knot invariants including the determinant, the span of the Jones polynomial, and an invariant related to tricolorability. We show, in all cases considered, that the quotient graphs are Gromov hyperbolic. We then prove a collection of results about the graph isomorphism type of the quotient graphs. In particular, we find that the H(2)-Gordian graph of links modulo the relation induced by the span of the Jones polynomial is isomorphic with the complete graph on infinitely many vertices.
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Sun, Ke, Shuo Yu, Ciyuan Peng, Yueru Wang, Osama Alfarraj, Amr Tolba, and Feng Xia. "Relational Structure-Aware Knowledge Graph Representation in Complex Space." Mathematics 10, no. 11 (June 4, 2022): 1930. http://dx.doi.org/10.3390/math10111930.

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Relations in knowledge graphs have rich relational structures and various binary relational patterns. Various relation modelling strategies are proposed for embedding knowledge graphs, but they fail to fully capture both features of relations, rich relational structures and various binary relational patterns. To address the problem of insufficient embedding due to the complexity of the relations, we propose a novel knowledge graph representation model in complex space, namely MARS, to exploit complex relations to embed knowledge graphs. MARS takes the mechanisms of complex numbers and message-passing and then embeds triplets into relation-specific complex hyperplanes. Thus, MARS can well preserve various relation patterns, as well as structural information in knowledge graphs. In addition, we find that the scores generated from the score function approximate a Gaussian distribution. The scores in the tail cannot effectively represent triplets. To address this particular issue and improve the precision of embeddings, we use the standard deviation to limit the dispersion of the score distribution, resulting in more accurate embeddings of triplets. Comprehensive experiments on multiple benchmarks demonstrate that our model significantly outperforms existing state-of-the-art models for link prediction and triple classification.
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Yurttas Gunes, Aysun, Sadik Delen, Musa Demirci, Ahmet Sinan Cevik, and Ismail Naci Cangul. "Fibonacci Graphs." Symmetry 12, no. 9 (August 19, 2020): 1383. http://dx.doi.org/10.3390/sym12091383.

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Apart from its applications in Chemistry, Biology, Physics, Social Sciences, Anthropology, etc., there are close relations between graph theory and other areas of Mathematics. Fibonacci numbers are of utmost interest due to their relation with the golden ratio and also due to many applications in different areas from Biology, Architecture, Anatomy to Finance. In this paper, we define Fibonacci graphs as graphs having degree sequence consisting of n consecutive Fibonacci numbers and use the invariant Ω to obtain some more information on these graphs. We give the necessary and sufficient conditions for the realizability of a set D of n successive Fibonacci numbers for every n and also list all possible realizations called Fibonacci graphs for 1≤n≤4.
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An, Dezhi, Xuejie Ma, Cao Jiang, Lei Liu, and Yanxu Wang. "Research and Application of Relation Extraction based on Triple Relation Graph Convolutional Networks." Journal of Physics: Conference Series 2166, no. 1 (January 1, 2022): 012060. http://dx.doi.org/10.1088/1742-6596/2166/1/012060.

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Abstract In recent years, data in non-Euclidean spaces is becoming more and more. Traditional methods cannot perform feature extraction on these data. Most of existing methods just extract contextual semantic features from relational instances. Their structural features in corpora are ignored. To solve this problem, the paper proposed a relation extraction method based on triple relation graph convolutional networks (TRGCN). Based on the extraction of semantic features of sentences using convolutional neural networks, this method used the concept of triple relation graphs to represent structural features. In other words, triple relation graphs were formed by considering triples formed by the relation between two entities in one sentence as nodes and triples with common entities and same relations as edges. Finally, multiple-layer graph convolutional networks were used for training. As shown by experimental results, the method proposed in this paper achieved an F1 value of 86.8% on the SemEval 2010 Tesk 8 data set, indicating that it is better than mainstream convolutional neural networks and recurrent neural networks.
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Dissertations / Theses on the topic "Relation Graphs"

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Mercier, Pierre-Adelin. "Graphages à type d'isomorphisme prescrit." Phd thesis, Ecole normale supérieure de lyon - ENS LYON, 2012. http://tel.archives-ouvertes.fr/tel-00751265.

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On considère R une relation d'équivalence borélienne standard de type I I1 sur un espace de probabilités (X, µ). On étudie une certaine propriété d'homogénéité pour un graphage fixé de la relation R : on suppose que les feuilles du graphage sont toutes isomorphes à un certain graphe transitif (connexe, infini, localement fini) Γ. Que peut-on dire sur la relation ? Dans ce cas, en considérant une action "à la Mackey", on montre qu'il existe (Z ,η) un revêtement standard probabilisé de (X, µ), une action libre (qui préserve η) sur Z du groupe G (localement compact, à base dénombrable d'ouverts) des automorphismes du graphe et un isomorphisme stable des groupoïdes mesurés associés. On fait le lien entre les propriétés du groupe G et celles de la relation de départ ; en particulier la propriété (T), (H) et la moyennabilité "passent" du graphe à la relation et réciproquement. On déduit aussi de la construction quelques couplages d'équivalence mesurée (ou plus généralement des "randembeddings") entre certains sous-groupes des automorphismes de Γ et tout groupe qui contient orbitalement la relation R. Dans un deuxième chapitre, on aborde le cas particulier de la propriété (T) relative pour les paires de groupes (ΓxZ^2, Z^2), où Γ est un sous-groupe non moyennable de SL(2,Z). Cette propriété a d'abord été prouvée par Marc Burger, puis "re-démontrée" plus "visuellement" quelques années plus tard dans le cas de SL(2,Z)xZ^2 par Y. Shalom, en utilisant des découpages du plan. On reprend cette technique dans le cas général du théorème de Burger afin d'obtenir par un algorithme des constantes de Kazhdan explicites pour toute paire (ΓxZ^2, Z^2).
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Byrne, Kate. "Populating the Semantic Web : combining text and relational databases as RDF graphs." Thesis, University of Edinburgh, 2009. http://hdl.handle.net/1842/3781.

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The Semantic Web promises a way of linking distributed information at a granular level by interconnecting compact data items instead of complete HTML pages. New data is gradually being added to the Semantic Web but there is a need to incorporate existing knowledge. This thesis explores ways to convert a coherent body of information from various structured and unstructured formats into the necessary graph form. The transformation work crosses several currently active disciplines, and there are further research questions that can be addressed once the graph has been built. Hybrid databases, such as the cultural heritage one used here, consist of structured relational tables associated with free text documents. Access to the data is hampered by complex schemas, confusing terminology and difficulties in searching the text effectively. This thesis describes how hybrid data can be unified by assembly into a graph. A major component task is the conversion of relational database content to RDF. This is an active research field, to which this work contributes by examining weaknesses in some existing methods and proposing alternatives. The next significant element of the work is an attempt to extract structure automatically from English text using natural language processing methods. The first claim made is that the semantic content of the text documents can be adequately captured as a set of binary relations forming a directed graph. It is shown that the data can then be grounded using existing domain thesauri, by building an upper ontology structure from these. A schema for cultural heritage data is proposed, intended to be generic for that domain and as compact as possible. Another hypothesis is that use of a graph will assist retrieval. The structure is uniform and very simple, and the graph can be queried even if the predicates (or edge labels) are unknown. Additional benefits of the graph structure are examined, such as using path length between nodes as a measure of relatedness (unavailable in a relational database where there is no equivalent concept of locality), and building information summaries by grouping the attributes of nodes that share predicates. These claims are tested by comparing queries across the original and the new data structures. The graph must be able to answer correctly queries that the original database dealt with, and should also demonstrate valid answers to queries that could not previously be answered or where the results were incomplete.
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Bouakaz, Adnan. "Real-time scheduling of dataflow graphs." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00945453.

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The ever-increasing functional and nonfunctional requirements in real-time safety-critical embedded systems call for new design flows that solve the specification, validation, and synthesis problems. Ensuring key properties, such as functional determinism and temporal predictability, has been the main objective of many embedded system design models. Dataflow models of computation (such as KPN, SDF, CSDF, etc.) are widely used to model stream-based embedded systems due to their inherent functional determinism. Since the introduction of the (C)SDF model, a considerable effort has been made to solve the static-periodic scheduling problem. Ensuring boundedness and liveness is the essence of the proposed algorithms in addition to optimizing some nonfunctional performance metrics (e.g. buffer minimization, throughput maximization, etc.). However, nowadays real-time embedded systems are so complex that real-time operating systems are used to manage hardware resources and host real-time tasks. Most of real-time operating systems rely on priority-driven scheduling algorithms (e.g. RM, EDF, etc.) instead of static schedules which are inflexible and difficult to maintain. This thesis addresses the real-time scheduling problem of dataflow graph specifications; i.e. transformation of the dataflow specification to a set of independent real-time tasks w.r.t. a given priority-driven scheduling policy such that the following properties are satisfied: (1) channels are bounded and overflow/underflow-free; (2) the task set is schedulable on a given uniprocessor (or multiprocessor) architecture. This problem requires the synthesis of scheduling parameters (e.g. periods, priorities, processor allocation, etc.) and channel capacities. Furthermore, the thesis considers two performance optimization problems: buffer minimization and throughput maximization.
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Eichler, Kathrin [Verfasser], and Josef van [Akademischer Betreuer] Genabith. "Generating and applying textual entailment graphs for relation extraction and email categorization / Kathrin Eichler ; Betreuer: Josef van Genabith." Saarbrücken : Saarländische Universitäts- und Landesbibliothek, 2018. http://d-nb.info/1162892242/34.

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Capshaw, Riley. "Relation Classification using Semantically-Enhanced Syntactic Dependency Paths : Combining Semantic and Syntactic Dependencies for Relation Classification using Long Short-Term Memory Networks." Thesis, Linköpings universitet, Interaktiva och kognitiva system, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-153877.

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Many approaches to solving tasks in the field of Natural Language Processing (NLP) use syntactic dependency trees (SDTs) as a feature to represent the latent nonlinear structure within sentences. Recently, work in parsing sentences to graph-based structures which encode semantic relationships between words—called semantic dependency graphs (SDGs)—has gained interest. This thesis seeks to explore the use of SDGs in place of and alongside SDTs within a relation classification system based on long short-term memory (LSTM) neural networks. Two methods for handling the information in these graphs are presented and compared between two SDG formalisms. Three new relation extraction system architectures have been created based on these methods and are compared to a recent state-of-the-art LSTM-based system, showing comparable results when semantic dependencies are used to enhance syntactic dependencies, but with significantly fewer training parameters.
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Benkoussas, Chahinez. "Approches non supervisées pour la recommandation de lectures et la mise en relation automatique de contenus au sein d'une bibliothèque numérique." Thesis, Aix-Marseille, 2016. http://www.theses.fr/2016AIXM4379/document.

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Cette thèse s’inscrit dans le domaine de la recherche d’information (RI) et la recommandation de lecture. Elle a pour objets :— La création de nouvelles approches de recherche de documents utilisant des techniques de combinaison de résultats, d’agrégation de données sociales et de reformulation de requêtes ;— La création d’une approche de recommandation utilisant des méthodes de RI et les graphes entre les documents. Deux collections de documents ont été utilisées. Une collection qui provient de l’évaluation CLEF (tâche Social Book Search - SBS) et la deuxième issue du domaine des sciences humaines et sociales (OpenEdition, principalement Revues.org). La modélisation des documents de chaque collection repose sur deux types de relations :— Dans la première collection (CLEF SBS), les documents sont reliés avec des similarités calculées par Amazon qui se basent sur plusieurs facteurs (achats des utilisateurs, commentaires, votes, produits achetés ensemble, etc.) ;— Dans la deuxième collection (OpenEdition), les documents sont reliés avec des relations de citations (à partir des références bibliographiques).Le manuscrit est structuré en deux parties. La première partie «état de l’art» regroupe une introduction générale, un état de l’art sur la RI et sur les systèmes de recommandation. La deuxième partie «contributions» regroupe un chapitre sur la détection de comptes rendus de lecture au sein de la collection OpenEdition (Revues.org), un chapitre sur les méthodes de RI utilisées sur des requêtes complexes et un dernier chapitre qui traite l’approche de recommandation proposée qui se base sur les graphes
This thesis deals with the field of information retrieval and the recommendation of reading. It has for objects:— The creation of new approach of document retrieval and recommendation using techniques of combination of results, aggregation of social data and reformulation of queries;— The creation of an approach of recommendation using methods of information retrieval and graph theories.Two collections of documents were used. First one is a collection which is provided by CLEF (Social Book Search - SBS) and the second from the platforms of electronic sources in Humanities and Social Sciences OpenEdition.org (Revues.org). The modelling of the documents of every collection is based on two types of relations:— For the first collection (SBS), documents are connected with similarity calculated by Amazon which is based on several factors (purchases of the users, the comments, the votes, products bought together, etc.);— For the second collection (OpenEdition), documents are connected with relations of citations, extracted from bibliographical references.We show that the proposed approaches bring in most of the cases gain in the performances of research and recommendation. The manuscript is structured in two parts. The first part "state of the art" includes a general introduction, a state of the art of informationretrieval and recommender systems. The second part "contributions" includes a chapter on the detection of reviews of books in Revues.org; a chapter on the methods of IR used on complex queries written in natural language and last chapter which handles the proposed approach of recommendation which is based on graph
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Ravelo, Jesus N. "Relations, graphs and programs." Thesis, University of Oxford, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.299107.

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Atto, Muhsin. "Implementing Order Relations on Graphs." Thesis, Uppsala University, Department of Information Technology, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-101170.

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The design of automatic verification methods for programs manipulating dynamic data structures is a challenging problem. We consider programs that operate on a data structures with one next-pointer, such as singly linked lists and circular lists. We refer to such data structures as heaps. We represent a heap as a graph, where vertices in the graph represent cells in the heap. To test that a program is safe, we should check that the out put of this program is well formed and is sorted. The aim of this project is to design a number of algorithms to test the different orderings between graphs which arise when including heaps. More precisely, given two graphs, G1 and G2, we check that G1 can be included in G2, using different type of orderings.

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Lucas, Claire. "Trois essais sur les relations entre les invariants structuraux des graphes et le spectre du Laplacien sans signe." Phd thesis, Ecole Polytechnique X, 2013. http://pastel.archives-ouvertes.fr/pastel-00956183.

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Le spectre du Laplacien sans signe a fait l'objet de beaucoup d'attention dans la communauté scientifique ces dernières années. La principale raison est l'intuition, basée sur une étude des petits graphes et sur des propriétés valides pour des graphes de toutes tailles, que plus de graphes sont déterminés par le spectre de cette matrice que par celui de la matrice d'adjacence et du Laplacien. Les travaux présentés dans cette thèse ont apporté des éléments nouveaux sur les informations contenues dans le spectre cette matrice. D'une part, on y présente des relations entre les invariants de structure et une valeur propre du Laplacien sans signe. D'autre part, on présente des familles de graphes extrêmes pour deux de ses valeurs propres, avec et sans contraintes additionnelles sur la forme de graphe. Il se trouve que ceux-ci sont très similaires à ceux obtenus dans les mêmes conditions avec les valeurs propres de la matrice d'adjacence. Cela aboutit à la définition de familles de graphes pour lesquelles, le spectre du Laplacien sans signe ou une de ses valeurs propres, le nombre de sommets et un invariant de structure suffisent à déterminer le graphe. Ces résultats, par leur similitude avec ceux de la littérature viennent confirmer l'idée que le Laplacien sans signe détermine probablement aussi bien les graphes que la matrice d'adjacence.
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Haugeard, Jean-Emmanuel. "Extraction et reconnaissance de primitives dans les façades de Paris à l'aide d'appariement de graphes." Thesis, Cergy-Pontoise, 2010. http://www.theses.fr/2010CERG0497.

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Cette dernière décennie, la modélisation des villes 3D est devenue l'un des enjeux de la recherche multimédia et un axe important en reconnaissance d'objets. Dans cette thèse nous nous sommes intéressés à localiser différentes primitives, plus particulièrement les fenêtres, dans les façades de Paris. Dans un premier temps, nous présentons une analyse des façades et des différentes propriétés des fenêtres. Nous en déduisons et proposons ensuite un algorithme capable d'extraire automatiquement des hypothèses de fenêtres. Dans une deuxième partie, nous abordons l'extraction et la reconnaissance des primitives à l'aide d'appariement de graphes de contours. En effet une image de contours est lisible par l'oeil humain qui effectue un groupement perceptuel et distingue les entités présentes dans la scène. C'est ce mécanisme que nous avons cherché à reproduire. L'image est représentée sous la forme d'un graphe d'adjacence de segments de contours, valué par des informations d'orientation et de proximité des segments de contours. Pour la mise en correspondance inexacte des graphes, nous proposons plusieurs variantes d'une nouvelle similarité basée sur des ensembles de chemins tracés sur les graphes, capables d'effectuer les groupements des contours et robustes aux changements d'échelle. La similarité entre chemins prend en compte la similarité des ensembles de segments de contours et la similarité des régions définies par ces chemins. La sélection des images d'une base contenant un objet particulier s'effectue à l'aide d'un classifieur SVM ou kppv. La localisation des objets dans l'image utilise un système de vote à partir des chemins sélectionnés par l'algorithme d'appariement
This last decade, modeling of 3D city became one of the challenges of multimedia search and an important focus in object recognition. In this thesis we are interested to locate various primitive, especially the windows, in the facades of Paris. At first, we present an analysis of the facades and windows properties. Then we propose an algorithm able to extract automatically window candidates. In a second part, we discuss about extraction and recognition primitives using graph matching of contours. Indeed an image of contours is readable by the human eye, which uses perceptual grouping and makes distinction between entities present in the scene. It is this mechanism that we have tried to replicate. The image is represented as a graph of adjacency of segments of contours, valued by information orientation and proximity to edge segments. For the inexact matching of graphs, we propose several variants of a new similarity based on sets of paths, able to group several contours and robust to scale changes. The similarity between paths takes into account the similarity of sets of segments of contours and the similarity of the regions defined by these paths. The selection of images from a database containing a particular object is done using a KNN or SVM classifier
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Books on the topic "Relation Graphs"

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Schmidt, Gunther, and Thomas Ströhlein. Relations and Graphs. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77968-8.

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Bader, David A., 1969- editor of compilation, Meyerhenke, Henning, 1978- editor of compilation, Sanders, Peter, editor of compilation, and Wagner, Dorothea, 1957- editor of compilation, eds. Graph partitioning and graph clustering: 10th DIMACS Implementation Challenge Workshop, February 13-14, 2012, Georgia Institute of Technology, Atlanta, GA. Providence, Rhode Island: American Mathematical Society, 2013.

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Schmidt, Gunther. Relations and graphs: Discrete mathematics for computer scientists. Berlin: New York, 1993.

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Schmidt, Gunther. Relations and graphs: Discrete mathematics for computer scientists. Berlin: Springer, 1993.

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Varlamov, Oleg. Mivar databases and rules. ru: INFRA-M Academic Publishing LLC., 2021. http://dx.doi.org/10.12737/1508665.

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The multidimensional open epistemological active network MOGAN is the basis for the transition to a qualitatively new level of creating logical artificial intelligence. Mivar databases and rules became the foundation for the creation of MOGAN. The results of the analysis and generalization of data representation structures of various data models are presented: from relational to "Entity — Relationship" (ER-model). On the basis of this generalization, a new model of data and rules is created: the mivar information space "Thing-Property-Relation". The logic-computational processing of data in this new model of data and rules is shown, which has linear computational complexity relative to the number of rules. MOGAN is a development of Rule - Based Systems and allows you to quickly and easily design algorithms and work with logical reasoning in the "If..., Then..." format. An example of creating a mivar expert system for solving problems in the model area "Geometry"is given. Mivar databases and rules can be used to model cause-and-effect relationships in different subject areas and to create knowledge bases of new-generation applied artificial intelligence systems and real-time mivar expert systems with the transition to"Big Knowledge". The textbook in the field of training "Computer Science and Computer Engineering" is intended for students, bachelors, undergraduates, postgraduates studying artificial intelligence methods used in information processing and management systems, as well as for users and specialists who create mivar knowledge models, expert systems, automated control systems and decision support systems. Keywords: cybernetics, artificial intelligence, mivar, mivar networks, databases, data models, expert system, intelligent systems, multidimensional open epistemological active network, MOGAN, MIPRA, KESMI, Wi!Mi, Razumator, knowledge bases, knowledge graphs, knowledge networks, Big knowledge, products, logical inference, decision support systems, decision-making systems, autonomous robots, recommendation systems, universal knowledge tools, expert system designers, logical artificial intelligence.
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University of Exeter. Centre for Innovation in Mathematics Teaching, ed. Relations by experiment. Exeter: Centre for Innovation in Mathematics Teaching, 1989.

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Productions, JN, ed. Oresama teacher: Eternal relation. San Francisco, Calif: Viz Media, 2012.

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Janowitz, M. F. Ordinal and relational clustering. Singapore: World Scientific, 2010.

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Rebecca, Otis, ed. Friends and relations: Using literature with social themes, grades 3-5. Greenfield, MA: Northeast Foundation for Children, 2000.

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Fraïssé, Roland. La reconstruction d'une relation dans l'hypothèse forte: Isomorphie des restrictions à chaque partie stricte de la base. Montréal, Québec, Camada: Presses de l'Université Laval, 1990.

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Book chapters on the topic "Relation Graphs"

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Schmidt, Gunther, and Thomas Ströhlein. "Graphs: Associated Relation, Incidence, Adjacency." In Relations and Graphs, 81–104. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/978-3-642-77968-8_5.

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Blanchard, Philippe, and Dimitri Volchenkov. "Worth Another Binary Relation: Graphs." In Springer Series in Synergetics, 19–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-19592-1_2.

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Dwivedi, Sri Nath, Harish Karnick, and Renu Jain. "Relation Classification: How Well Do Neural Network Approaches Work?" In Knowledge Graphs and Semantic Web, 102–12. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65384-2_8.

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Carvalho, Danilo S., André Freitas, and João C. P. da Silva. "Graphia: Extracting Contextual Relation Graphs from Text." In Advanced Information Systems Engineering, 236–41. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-41242-4_31.

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Mohamed, Sameh K., Emir Muñoz, Vít Nováček, and Pierre-Yves Vandenbussche. "Identifying Equivalent Relation Paths in Knowledge Graphs." In Lecture Notes in Computer Science, 299–314. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-59888-8_26.

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Rios-Alvarado, Ana B., Jose L. Martinez-Rodriguez, Tania Y. Guerrero-Melendez, Adolfo J. Rodriguez-Rodriguez, and David T. Vargas-Requena. "Axiomatic Relation Extraction from Text in the Domain of Tourism." In Knowledge Graphs and Semantic Web, 14–28. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65384-2_2.

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Pollandt, Silke. "Relation Graphs: A Structure for Representing Relations in Contextual Logic of Relations." In Conceptual Structures: Integration and Interfaces, 34–47. Berlin, Heidelberg: Springer Berlin Heidelberg, 2002. http://dx.doi.org/10.1007/3-540-45483-7_4.

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Nojima, Yusuke, Yasuhito Asano, and Masatoshi Yoshikawa. "Greedy Approximation Algorithms for Generalized Maximum Flow Problem towards Relation Extraction in Information Networks." In Computational Geometry and Graphs, 132–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45281-9_13.

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Deng, Jia, Nan Ding, Yangqing Jia, Andrea Frome, Kevin Murphy, Samy Bengio, Yuan Li, Hartmut Neven, and Hartwig Adam. "Large-Scale Object Classification Using Label Relation Graphs." In Computer Vision – ECCV 2014, 48–64. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-10590-1_4.

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Sun, Zequn, Jiacheng Huang, Wei Hu, Muhao Chen, Lingbing Guo, and Yuzhong Qu. "TransEdge: Translating Relation-Contextualized Embeddings for Knowledge Graphs." In Lecture Notes in Computer Science, 612–29. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30793-6_35.

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Conference papers on the topic "Relation Graphs"

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Huang, Zhichao, Xutao Li, Yunming Ye, and Michael K. Ng. "MR-GCN: Multi-Relational Graph Convolutional Networks based on Generalized Tensor Product." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/175.

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Graph Convolutional Networks (GCNs) have been extensively studied in recent years. Most of existing GCN approaches are designed for the homogenous graphs with a single type of relation. However, heterogeneous graphs of multiple types of relations are also ubiquitous and there is a lack of methodologies to tackle such graphs. Some previous studies address the issue by performing conventional GCN on each single relation and then blending their results. However, as the convolutional kernels neglect the correlations across relations, the strategy is sub-optimal. In this paper, we propose the Multi-Relational Graph Convolutional Network (MR-GCN) framework by developing a novel convolution operator on multi-relational graphs. In particular, our multi-dimension convolution operator extends the graph spectral analysis into the eigen-decomposition of a Laplacian tensor. And the eigen-decomposition is formulated with a generalized tensor product, which can correspond to any unitary transform instead of limited merely to Fourier transform. We conduct comprehensive experiments on four real-world multi-relational graphs to solve the semi-supervised node classification task, and the results show the superiority of MR-GCN against the state-of-the-art competitors.
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Sousa, Rita T., Sara Silva, and Catia Pesquita. "Explainable Representations for Relation Prediction in Knowledge Graphs." In 20th International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/kr.2023/62.

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Knowledge graphs represent real-world entities and their relations in a semantically-rich structure supported by ontologies. Exploring this data with machine learning methods often relies on knowledge graph embeddings, which produce latent representations of entities that preserve structural and local graph neighbourhood properties, but sacrifice explainability. However, in tasks such as link or relation prediction, understanding which specific features better explain a relation is crucial to support complex or critical applications. We propose SEEK, a novel approach for explainable representations to support relation prediction in knowledge graphs. It is based on identifying relevant shared semantic aspects (i.e., subgraphs) between entities and learning representations for each subgraph, producing a multi-faceted and explainable representation. We evaluate SEEK on two real-world highly complex relation prediction tasks: protein-protein interaction prediction and gene-disease association prediction. Our extensive analysis using established benchmarks demonstrates that SEEK achieves comparable or even superior performance to standard learning representation methods while identifying both sufficient and necessary explanations based on shared semantic aspects.
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Zhang, Fuxiang, Xin Wang, Zhao Li, and Jianxin Li. "TransRHS: A Representation Learning Method for Knowledge Graphs with Relation Hierarchical Structure." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/413.

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Representation learning of knowledge graphs aims to project both entities and relations as vectors in a continuous low-dimensional space. Relation Hierarchical Structure (RHS), which is constructed by a generalization relationship named subRelationOf between relations, can improve the overall performance of knowledge representation learning. However, most of the existing methods ignore this critical information, and a straightforward way of considering RHS may have a negative effect on the embeddings and thus reduce the model performance. In this paper, we propose a novel method named TransRHS, which is able to incorporate RHS seamlessly into the embeddings. More specifically, TransRHS encodes each relation as a vector together with a relation-specific sphere in the same space. Our TransRHS employs the relative positions among the vectors and spheres to model the subRelationOf, which embodies the inherent generalization relationships among relations. We evaluate our model on two typical tasks, i.e., link prediction and triple classification. The experimental results show that our TransRHS model significantly outperforms all baselines on both tasks, which verifies that the RHS information is significant to representation learning of knowledge graphs, and TransRHS can effectively and efficiently fuse RHS into knowledge graph embeddings.
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Ye, Rui, Xin Li, Yujie Fang, Hongyu Zang, and Mingzhong Wang. "A Vectorized Relational Graph Convolutional Network for Multi-Relational Network Alignment." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/574.

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Alignment of multiple multi-relational networks, such as knowledge graphs, is vital for AI applications. Different from the conventional alignment models, we apply the graph convolutional network (GCN) to achieve more robust network embedding for the alignment task. In comparison with existing GCNs which cannot fully utilize multi-relation information, we propose a vectorized relational graph convolutional network (VR-GCN) to learn the embeddings of both graph entities and relations simultaneously for multi-relational networks. The role discrimination and translation property of knowledge graphs are adopted in the convolutional process. Thereafter, AVR-GCN, the alignment framework based on VR-GCN, is developed for multi-relational network alignment tasks. Anchors are used to supervise the objective function which aims at minimizing the distances between anchors, and to generate new cross-network triplets to build a bridge between different knowledge graphs at the level of triplet to improve the performance of alignment. Experiments on real-world datasets show that the proposed solutions outperform the state-of-the-art methods in terms of network embedding, entity alignment, and relation alignment.
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Wang, Huijuan, Siming Dai, Weiyue Su, Hui Zhong, Zeyang Fang, Zhengjie Huang, Shikun Feng, Zeyu Chen, Yu Sun, and Dianhai Yu. "Simple and Effective Relation-based Embedding Propagation for Knowledge Representation Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/382.

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Relational graph neural networks have garnered particular attention to encode graph context in knowledge graphs (KGs). Although they achieved competitive performance on small KGs, how to efficiently and effectively utilize graph context for large KGs remains an open problem. To this end, we propose the Relation-based Embedding Propagation (REP) method. It is a post-processing technique to adapt pre-trained KG embeddings with graph context. As relations in KGs are directional, we model the incoming head context and the outgoing tail context separately. Accordingly, we design relational context functions with no external parameters. Besides, we use averaging to aggregate context information, making REP more computation-efficient. We theoretically prove that such designs can avoid information distortion during propagation. Extensive experiments also demonstrate that REP has significant scalability while improving or maintaining prediction quality. Particularly, it averagely brings about 10% relative improvement to triplet-based embedding methods on OGBL-WikiKG2 and takes 5%-83% time to achieve comparable results as the state-of-the-art GC-OTE.
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Wu, Yuting, Xiao Liu, Yansong Feng, Zheng Wang, Rui Yan, and Dongyan Zhao. "Relation-Aware Entity Alignment for Heterogeneous Knowledge Graphs." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/733.

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Entity alignment is the task of linking entities with the same real-world identity from different knowledge graphs (KGs), which has been recently dominated by embedding-based methods. Such approaches work by learning KG representations so that entity alignment can be performed by measuring the similarities between entity embeddings. While promising, prior works in the field often fail to properly capture complex relation information that commonly exists in multi-relational KGs, leaving much room for improvement. In this paper, we propose a novel Relation-aware Dual-Graph Convolutional Network (RDGCN) to incorporate relation information via attentive interactions between the knowledge graph and its dual relation counterpart, and further capture neighboring structures to learn better entity representations. Experiments on three real-world cross-lingual datasets show that our approach delivers better and more robust results over the state-of-the-art alignment methods by learning better KG representations.
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Pan, Jia-Hui, Jibin Gao, and Wei-Shi Zheng. "Action Assessment by Joint Relation Graphs." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00643.

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Banerjee, Arindam, Sugato Basu, and Srujana Merugu. "Multi-way Clustering on Relation Graphs." In Proceedings of the 2007 SIAM International Conference on Data Mining. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2007. http://dx.doi.org/10.1137/1.9781611972771.14.

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Yang, Qiang, Qiannan Zhang, Chuxu Zhang, and Xiangliang Zhang. "Interpretable Relation Learning on Heterogeneous Graphs." In WSDM '22: The Fifteenth ACM International Conference on Web Search and Data Mining. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3488560.3498508.

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Mandya, Angrosh, Danushka Bollegala, and Frans Coenen. "Graph Convolution over Multiple Dependency Sub-graphs for Relation Extraction." In Proceedings of the 28th International Conference on Computational Linguistics. Stroudsburg, PA, USA: International Committee on Computational Linguistics, 2020. http://dx.doi.org/10.18653/v1/2020.coling-main.565.

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Reports on the topic "Relation Graphs"

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Baader, Franz, Ralf Molitor, and Stefan Tobies. On the Relation between Conceptual Graphs and Description Logics. Aachen University of Technology, 1998. http://dx.doi.org/10.25368/2022.87.

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Barooah, Prabir, and Joao P. Hespanha. Estimation from Relative Measurements: Electrical Analogy and Large Graphs. Fort Belvoir, VA: Defense Technical Information Center, September 2007. http://dx.doi.org/10.21236/ada473862.

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Kaplan, I., G. Abdulla, S. Brugger, and S. Kohn. Implementing Graph Pattern Queries on a Relational Database. Office of Scientific and Technical Information (OSTI), December 2007. http://dx.doi.org/10.2172/924192.

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Hidders, Jan, Bei Li, and George Fletcher, eds. Property Graph Schema contributions to WG3. Linked Data Benchmark Council, June 2020. http://dx.doi.org/10.54285/ldbc.ofjf3566.

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Five papers and three accompanying presentations contributed to the ISO/IEC JTC 1/SC 32 WG3 (Database Languages) standards committee relating to property graph schema, including meta-properties and keys
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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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Conrad, Shane B. Loyalty, Leadership, and the Profession: A Grass Roots Approach to Civil-Military Relations. Fort Belvoir, VA: Defense Technical Information Center, March 2011. http://dx.doi.org/10.21236/ada553154.

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Barrera-Osorio, Felipe, Paul Gertler, Nozomi Nakajima, and Harry A. Patrinos. Promoting Parental Involvement in Schools: Evidence from Two Randomized Experiments. Research on Improving Systems of Education (RISE), January 2021. http://dx.doi.org/10.35489/bsg-rise-wp_2021/060.

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Parental involvement programs aim to strengthen school-home relations with the goal of improving children’s educational outcomes. We examine the effects of a parental involvement program in Mexico, which provides parent associations with grants and information. We separately estimate the effect of the grants from the effect of the information using data from two randomized controlled trials conducted by the government during the rollout of the program. Grants to parent associations did not improve educational outcomes. Information to parent associations reduced disciplinary actions in schools, mainly by increasing parental involvement in schools and changing parenting behavior at home. The divergent results from grants and information are partly explained by significant changes in perceptions of trust between parents and teachers. Our results suggest that parental involvement interventions may not achieve their intended goal if institutional rules are unclear about the expectations of parents and teachers as parents increase their involvement in schools.
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Rodríguez, María Ximena, Andrés Díaz, Jimmy Zapata, Alex Smith, and Paula Mesa. Avances en el manejo y control de Fusarium oxysporum en el cultivo de uchuva (physalis peruviana). Corporación colombiana de investigación agropecuaria - AGROSAVIA, 2021. http://dx.doi.org/10.21930/agrosavia.cartilla.2012.1.

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La uchuva (Physalis peruviana) es una planta perenne, herbácea, que crece silvestre o semisilvestre. Se propaga normalmente por semilla; sus flores son solitarias y hermafroditas, fácilmente polinizadas por insectos, el viento o autopolinización. El fruto es una baya globosa u ovoide y jugosa, con un diámetro entre 1.25 y 2.15 cm y un peso entre 4 a 10 gramos, con una estructura interna similar a la de un tomate. La baya varía de color amarillo a ocre o amarillo naranja cuando madura; su piel es delgada y lustrosa y está recubierta con un cáliz o capacho (Figura 1). El cultivo de la uchuva presenta buen comportamiento en las regiones que se ubican entre 1800 y 2800 metros sobre el nivel del mar, con alta luminosidad, temperaturas promedio entre 13 y 18 grados centígrados, precipitación anual de entre 1000 y 2000 milímetros y humedad relativa de 70 a 80 por ciento, preferiblemente en suelos con una estructura granular y una textura areno – arcillosa, que contengan altos contenidos de materia orgánica y un pH entre 5.5 y 6.8.
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Berlinski, Samuel, Matías Busso, Taryn Dinkelman, and Claudia Martínez. Research Insights: Can Low-Cost Communication Technologies Bridge Information Gaps between Schools and Parents? Inter-American Development Bank, October 2021. http://dx.doi.org/10.18235/0003737.

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We document large gaps between parents knowledge and school reports of students attendance and grades. Sending frequent text messages with information on attendance, grades and school behavior shrinks those gaps. Parents of at-risk students adjust their understanding of their children's performance to the greatest degree. High-frequency text messages had positive impacts on grades and attendance. Math GPA increased 0.08 of a standard deviation; the probability of earning a passing grade in math increased by 2.7 percentage points (relative to a mean of 90 percent). The intervention also reduced school absenteeism by 1 percentage point and increased the share of students who met attendance requirements for grade promotion by 4.5 percentage points.
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Schwarte, Kirk A., and James R. Russell. Grazing Management Effects on the Sward and Physical Characteristics Relative to Streams in Cool-Season Grass Pastures. Ames (Iowa): Iowa State University, January 2009. http://dx.doi.org/10.31274/ans_air-180814-71.

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