Готові списки джерел за темами / Graph-based representation and reasoning

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  2. Дисертації
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Добірка наукової літератури з теми "Graph-based representation and reasoning"

Автор: Grafiati
Опубліковано: 3 червня 2025
Оновлено: 31 липня 2025

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Статті в журналах з теми "Graph-based representation and reasoning"

1

Tian, Xin, and Yuan Meng. "Relgraph: A Multi-Relational Graph Neural Network Framework for Knowledge Graph Reasoning Based on Relation Graph." Applied Sciences 14, no. 7 (2024): 3122. http://dx.doi.org/10.3390/app14073122.

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Анотація:
Multi-relational graph neural networks (GNNs) have found widespread application in tasks involving enhancing knowledge representation and knowledge graph (KG) reasoning. However, existing multi-relational GNNs still face limitations in modeling the exchange of information between predicates. To address these challenges, we introduce Relgraph, a novel KG reasoning framework. This framework introduces relation graphs to explicitly model the interactions between different relations, enabling more comprehensive and accurate handling of representation learning and reasoning tasks on KGs. Furthermor
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Zhou, Xiaojie, Pengjun Zhai, and Yu Fang. "Learning Description-Based Representations for Temporal Knowledge Graph Reasoning via Attentive CNN." Journal of Physics: Conference Series 2025, no. 1 (2021): 012003. http://dx.doi.org/10.1088/1742-6596/2025/1/012003.

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Анотація:
Abstract Knowledge graphs have played a significant role in various applications and knowledge reasoning is one of the key tasks. However, the task gets more challenging when each fact is associated with a time annotation on temporal knowledge graph. Most of the existing temporal knowledge graph representation learning methods exploit structural information to learn the entity and relation representations. By these methods, those entities with similar structural information cannot be easily distinguished. Incorporating other information is an effective way to solve such problems. To address th
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Feng, Siling, Cong Zhou, Qian Liu, Xunyang Ji, and Mengxing Huang. "Temporal Knowledge Graph Reasoning Based on Entity Relationship Similarity Perception." Electronics 13, no. 12 (2024): 2417. http://dx.doi.org/10.3390/electronics13122417.

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Анотація:
Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them to effectively handle these temporal attributes. Therefore, these models have limitations in dealing with previously invisible entities that appear over time and the implicit associations of static attributes between entities. To address this issue, we propose a temporal knowledge graph reasoning
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4

Zhao, Xiaojuan, Aiping Li, Rong Jiang, Kai Chen, and Zhichao Peng. "Householder Transformation-Based Temporal Knowledge Graph Reasoning." Electronics 12, no. 9 (2023): 2001. http://dx.doi.org/10.3390/electronics12092001.

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Анотація:
Knowledge graphs’ reasoning is of great significance for the further development of artificial intelligence and information retrieval, especially for reasoning over temporal knowledge graphs. The rotation-based method has been shown to be effective at modeling entities and relations on a knowledge graph. However, due to the lack of temporal information representation capability, existing approaches can only model partial relational patterns and they cannot handle temporal combination reasoning. In this regard, we propose HTTR: Householder Transformation-based Temporal knowledge graph Reasoning
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5

Tang, Yaling, and Peng Yang. "Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification." Journal of Physics: Conference Series 2303, no. 1 (2022): 012030. http://dx.doi.org/10.1088/1742-6596/2303/1/012030.

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Abstract Tabular fact verification is a challenging task that requires obtaining relevant evidence from the table and utilizing them to verify a given claim. The main difficulty in tabular fact verification is that traditional language models cannot capture the underlying information carried in tabular data. To solve this problem, we propose GraERR, a Graph Enhanced Representation and Reasoning Model for Tabular Fact Verification. It consists of two modules: a data initial representation module based on the DeBERTa model and a graph-augmented representation and inference module. The former imp
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6

Lv, Shangwen, Daya Guo, Jingjing Xu, et al. "Graph-Based Reasoning over Heterogeneous External Knowledge for Commonsense Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (2020): 8449–56. http://dx.doi.org/10.1609/aaai.v34i05.6364.

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Анотація:
Commonsense question answering aims to answer questions which require background knowledge that is not explicitly expressed in the question. The key challenge is how to obtain evidence from external knowledge and make predictions based on the evidence. Recent studies either learn to generate evidence from human-annotated evidence which is expensive to collect, or extract evidence from either structured or unstructured knowledge bases which fails to take advantages of both sources simultaneously. In this work, we propose to automatically extract evidence from heterogeneous knowledge sources, an
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Chein, Michel, Marie-Laure Mugnier, and Madalina Croitoru. "Visual reasoning with graph-based mechanisms: the good, the better and the best." Knowledge Engineering Review 28, no. 3 (2013): 249–71. http://dx.doi.org/10.1017/s0269888913000234.

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AbstractThis paper presents a graph-based knowledge representation and reasoning language. This language benefits from an important syntactic operation, which is called a graph homomorphism. This operation is sound and complete with respect to logical deduction. Hence, it is possible to do logical reasoning without using the language of logic but only graphical, thus visual, notions. This paper presents the main knowledge constructs of this language, elementary graph-based reasoning mechanisms, as well as the graph homomorphism, which encompasses all these elementary transformations in one glo
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Huang, Zhao, and Liu Yuan. "Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies." Discrete Dynamics in Nature and Society 2021 (July 30, 2021): 1–18. http://dx.doi.org/10.1155/2021/2857611.

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Анотація:
People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexi
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Lee, Jae Yeol, and Kwangsoo Kim. "Geometric reasoning for knowledge-based parametric design using graph representation." Computer-Aided Design 28, no. 10 (1996): 831–41. http://dx.doi.org/10.1016/0010-4485(96)00016-4.

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Jiang, Pin, and Yahong Han. "Reasoning with Heterogeneous Graph Alignment for Video Question Answering." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 07 (2020): 11109–16. http://dx.doi.org/10.1609/aaai.v34i07.6767.

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Анотація:
The dominant video question answering methods are based on fine-grained representation or model-specific attention mechanism. They usually process video and question separately, then feed the representations of different modalities into following late fusion networks. Although these methods use information of one modality to boost the other, they neglect to integrate correlations of both inter- and intra-modality in an uniform module. We propose a deep heterogeneous graph alignment network over the video shots and question words. Furthermore, we explore the network architecture from four steps
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Дисертації з теми "Graph-based representation and reasoning"

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Valle, Kjetil. "Graph-Based Representations for Textual Case-Based Reasoning." Thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for datateknikk og informasjonsvitenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-13575.

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Анотація:
This thesis presents a graph-based approach to the problem of text representation. The work is motivated by the need for better representations for use in textual Case-Based Reasoning (CBR). In CBR new problems are solved by reasoning based on similar past problem cases. When the cases are represented in free text format, measuring the similarity between a new problem and previously solved problems become a challenging task. The case documents need to be re-represented before they can be compared/matched.Textual CBR (TCBR) addresses this issue. We investigate automatic re-representation of tex
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Suen, Edward Shaw-Lee Carleton University Dissertation Computer Science. "Tableau-based theorem proving for representation and reasoning." Ottawa, 1987.

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Larsson, Carl. "Case Representation Methodology for a Scalable Case-Based Reasoning." Thesis, Mälardalens högskola, Akademin för innovation, design och teknik, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:mdh:diva-41116.

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Анотація:
Case-Based Reasoning (CBR) is an Artificial Intelligence (AI) methodology and a growing field of research. CBR uses past experiences to help solve new problems the system faces. To do so CBR is comprised of a few core parts, such as case representation, case library, case retrieval, and case adaptation. This thesis will focus on the case representation aspect of CBR systems and presents a scalable case representation for big data environments. One aspect of focus on big data environments is also the focus of a MapReduce environment. MapReduce is a software framework enabling the use of a Map a
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4

Diab, Mohammed. "Knowledge representation and reasoning for perception-based manipulation planning." Doctoral thesis, Universitat Politècnica de Catalunya, 2021. http://hdl.handle.net/10803/671505.

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Анотація:
This thesis develops a series of modeling and reasoning tools for knowledge-oriented manipulation planning in semi/unstructured environments. The main idea is to use high-level knowledge-based reasoning to capture a rich semantic description of the scene, knowledge about the physical behavior of the objects, and inference mechanism to reason about the potential manipulation actions. Moreover, a multi-model sensory module is proposed to perceive the objects in the environment and build the ontological knowledge. The first part of the thesis is focused on the techniques to provide useful know
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5

Wolter, Diedrich. "Spatial representation and reasoning for robot mapping a shape-based approach." Berlin Heidelberg Springer, 2006. http://d-nb.info/989966941/34.

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Wolter, Diedrich. "Spatial representation and reasoning for robot mapping a shape-based approach /." Berlin : Springer, 2008. http://www.myilibrary.com?id=186085.

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Nguyen, Vinh Thi Kim. "Semantic Web Foundations for Representing, Reasoning, and Traversing Contextualized Knowledge Graphs." Wright State University / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=wright1516147861789615.

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Bate, Andrew. "Consequence-based reasoning for SRIQ ontologies." Thesis, University of Oxford, 2016. https://ora.ox.ac.uk/objects/uuid:6b35e7d0-199c-4db9-ac8a-7f78256e5fb8.

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Анотація:
Description logics (DLs) are knowledge representation formalisms with numerous applications and well-understood model-theoretic semantics and computational properties. SRIQ is a DL that provides the logical underpinning for the semantic web language OWL 2, which is the W3C standard for knowledge representation on the web. A central component of most DL applications is an efficient and scalable reasoner, which provides services such as consistency testing and classification. Despite major advances in DL reasoning algorithms over the last decade, however, ontologies are still encountered in prac
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Steinhauer, Heike Joe. "A Representation Scheme for Description and Reconstruction of Object Configurations Based on Qualitative Relations." Doctoral thesis, Linköpings universitet, CASL - Cognitive Autonomous Systems Laboratory, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-12446.

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Анотація:
One reason Qualitative Spatial Reasoning (QSR) is becoming increasingly important to Artificial Intelligence (AI) is the need for a smooth ‘human-like’ communication between autonomous agents and people. The selected, yet general, task motivating the work presented here is the scenario of an object configuration that has to be described by an observer on the ground using only relational object positions. The description provided should enable a second agent to create a map-like picture of the described configuration in order to recognize the configuration on a representation from the survey pe
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Stefanoni, Giorgio. "Evaluating conjunctive and graph queries over the EL profile of OWL 2." Thesis, University of Oxford, 2015. https://ora.ox.ac.uk/objects/uuid:232978e9-90a2-41cc-afd5-319518296894.

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OWL 2 EL is a popular ontology language that is based on the EL family of description logics and supports regular role inclusions,axioms that can capture compositional properties of roles such as role transitivity and reflexivity. In this thesis, we present several novel complexity results and algorithms for answering expressive queries over OWL 2 EL knowledge bases (KBs) with regular role inclusions. We first focus on the complexity of conjunctive query (CQ) answering in OWL 2 EL and show that the problem is PSpace-complete in combined complexity, the complexity measured in the total size of
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Книги з теми "Graph-based representation and reasoning"

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Braun, Tanya, Marcel Gehrke, Tom Hanika, and Nathalie Hernandez, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-86982-3.

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Braun, Tanya, Diana Cristea, and Robert Jäschke, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16663-1.

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Haemmerlé, Ollivier, Gem Stapleton, and Catherine Faron Zucker, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6.

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Chapman, Peter, Dominik Endres, and Nathalie Pernelle, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-91379-7.

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Endres, Dominik, Mehwish Alam, and Diana Şotropa, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23182-8.

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Hernandez, Nathalie, Robert Jäschke, and Madalina Croitoru, eds. Graph-Based Representation and Reasoning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08389-6.

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Ojeda-Aciego, Manuel, Kai Sauerwald, and Robert Jäschke, eds. Graph-Based Representation and Reasoning. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-40960-8.

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Cochez, Michael, Madalina Croitoru, Pierre Marquis, and Sebastian Rudolph, eds. Graph Structures for Knowledge Representation and Reasoning. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72308-8.

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Croitoru, Madalina, Pierre Marquis, Sebastian Rudolph, and Gem Stapleton, eds. Graph Structures for Knowledge Representation and Reasoning. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-28702-7.

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Croitoru, Madalina, Sebastian Rudolph, Nic Wilson, John Howse, and Olivier Corby, eds. Graph Structures for Knowledge Representation and Reasoning. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29449-5.

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Частини книг з теми "Graph-based representation and reasoning"

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Knauff, Markus. "Visualization, Reasoning, and Rationality." In Graph-Based Representation and Reasoning. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23182-8_1.

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Ferré, Sébastien, and Peggy Cellier. "Graph-FCA in Practice." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_9.

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Buche, Patrice, Jérôme Fortin, and Alain Gutierrez. "Default Reasoning Implementation in CoGui." In Graph-Based Representation and Reasoning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08389-6_11.

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Ben-Naim, Jonathan. "Argumentation-Based Paraconsistent Logics." In Graph-Based Representation and Reasoning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08389-6_2.

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Frăsinaru, Cristian, and Florentin Olariu. "Efficient Representation of Extensional Constraints." In Graph-Based Representation and Reasoning. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08389-6_16.

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Yegenoglu, Alper, Pietro Quaglio, Emiliano Torre, Sonja Grün, and Dominik Endres. "Exploring the Usefulness of Formal Concept Analysis for Robust Detection of Spatio-temporal Spike Patterns in Massively Parallel Spike Trains." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_1.

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Priss, Uta. "A Semiotic-Conceptual Analysis of Conceptual Learning." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_10.

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Andrews, Simon, Ben Brewster, and Tony Day. "Organised Crime and Social Media: Detecting and Corroborating Weak Signals of Human Trafficking Online." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_11.

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Dragoş, Sanda-Maria, Diana-Florina Haliţă, and Christian Săcărea. "Distilling Conceptual Structures from Weblog Data Using Polyadic FCA." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_12.

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Dibie, Juliette, Stéphane Dervaux, Estelle Doriot, Liliana Ibanescu, and Caroline Pénicaud. "$$[MS]^2O$$ – A Multi-scale and Multi-step Ontology for Transformation Processes: Application to Micro-Organisms." In Graph-Based Representation and Reasoning. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-40985-6_13.

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Тези доповідей конференцій з теми "Graph-based representation and reasoning"

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Tena Cucala, David J., and Bernardo Cuenca Grau. "Bridging Max Graph Neural Networks and Datalog with Negation." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/89.

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Анотація:
We consider a general class of data transformations based on Graph Neural Networks (GNNs), which can be used for a wide variety of tasks. An important question in this setting is to characterise the expressive power of these transformations in terms of a suitable logic-based language. From a practical perspective, the correspondence of a GNN with a logical theory can be exploited for explaining the model's predictions symbolically. In this paper, we introduce a broad family of GNN-based transformations which can be characterised using Datalog programs with negation-as-failure, which can be com
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Liu, Yingjie, Yingchi Mao, Fudong Chit, Bo Wu, Silong Ding, and Rongzhi Qi. "LRIRL: Improving Knowledge Graph Reasoning through Representation Learning-Based Rule Induction." In 2024 IEEE 36th International Conference on Tools with Artificial Intelligence (ICTAI). IEEE, 2024. https://doi.org/10.1109/ictai62512.2024.00096.

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Pluska, Alexander, Pascal Welke, Thomas Gärtner, and Sagar Malhotra. "Logical Distillation of Graph Neural Networks." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/86.

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Анотація:
We present a logic based interpretable model for learning on graphs and an algorithm to distill this model from a Graph Neural Network (GNN). Recent results have shown connections between the expressivity of GNNs and the two-variable fragment of first-order logic with counting quantifiers (C2). We introduce a decision-tree based model which leverages an extension of C2 to distill interpretable logical classifiers from GNNs. We test our approach on multiple GNN architectures. The distilled models are interpretable, succinct, and attain similar accuracy to the underlying GNN. Furthermore, when t
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Morris, Matthew, David J. Tena Cucala, Bernardo Cuenca Grau, and Ian Horrocks. "Relational Graph Convolutional Networks Do Not Learn Sound Rules." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/84.

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Анотація:
Graph neural networks (GNNs) are frequently used to predict missing facts in knowledge graphs (KGs). Motivated by the lack of explainability for the outputs of these models, recent work has aimed to explain their predictions using Datalog, a widely used logic-based formalism. However, such work has been restricted to certain subclasses of GNNs. In this paper, we consider one of the most popular GNN architectures for KGs, R-GCN, and we provide two methods to extract rules that explain its predictions and are sound, in the sense that each fact derived by the rules is also predicted by the GNN, f
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Ivliev, Alex, Lukas Gerlach, Simon Meusel, Jakob Steinberg, and Markus Krötzsch. "Nemo: Your Friendly and Versatile Rule Reasoning Toolkit." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/70.

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Анотація:
We present Nemo, a toolkit for rule-based reasoning and data processing that emphasises robustness and ease of use. Nemo’s core is a scalable and efficient main-memory reasoner that supports an expressive extension of Datalog with support for datatypes, existential rules, aggregates, and (stratified) negation. Built around this core is a versatile system of libraries and applications for interfacing with several data formats and programming languages, use as a progressive web application, and IDE integration. In this system description, we present this toolkit and discuss relevant application
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Ahmetaj, Shqiponja, Timo Camillo Merkl, and Reinhard Pichler. "Consistent Query Answering over SHACL Constraints." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/1.

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Анотація:
The Shapes Constraint Language (SHACL) was standardized by the World Wide Web as a constraint language to describe and validate RDF data graphs. SHACL uses the notion of shapes graph to describe a set of shape constraints paired with targets, that specify which nodes of the RDF graph should satisfy which shapes. An important question in practice is how to handle data graphs that do not validate the shapes graph. A solution is to tolerate the non-validation and find ways to obtain meaningful and correct answers to queries despite the non-validation. This is known as consistent query answering (
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Bourgaux, Camille, Ricardo Guimarães, Raoul Koudijs, Victor Lacerda, and Ana Ozaki. "Knowledge Base Embeddings: Semantics and Theoretical Properties." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/77.

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Research on knowledge graph embeddings has recently evolved into knowledge base embeddings, where the goal is not only to map facts into vector spaces but also constrain the models so that they take into account the relevant conceptual knowledge available. This paper examines recent methods that have been proposed to embed knowledge bases in description logic into vector spaces through the lens of their geometric-based semantics. We identify several relevant theoretical properties, which we draw from the literature and sometimes generalize or unify. We then investigate how concrete embedding m
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8

Alviano, Mario, and Luis Angel Rodriguez Reiners. "ASP Chef: Draw and Expand." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/68.

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ASP Chef is a versatile tool built upon the principles of Answer Set Programming (ASP), offering a unique approach to problem-solving through the concept of ASP recipes. In this paper, we explore two key components of ASP Chef: the Graph ingredient and one of its extension mechanisms for registering new ingredients. The Graph ingredient serves as a fundamental feature within ASP Chef, allowing users to interpret instances of a designed predicate to construct graphs from the data. Through this capability, ASP Chef facilitates the visualization and analysis of complex relationships and structure
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9

Gould, Adam, Guilherme Paulino-Passos, Seema Dadhania, Matthew Williams, and Francesca Toni. "Preference-Based Abstract Argumentation for Case-Based Reasoning." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/37.

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In the pursuit of enhancing the efficacy and flexibility of interpretable, data-driven classification models, this work introduces a novel incorporation of user-defined preferences with Abstract Argumentation and Case-Based Reasoning (CBR). Specifically, we introduce Preference-Based Abstract Argumentation for Case-Based Reasoning (which we call AA-CBR-P), allowing users to define multiple approaches to compare cases with an ordering that specifies their preference over these comparison approaches. We prove that the model inherently follows these preferences when making predictions and show th
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Okulmus, Cem, and Mantas Šimkus. "SHACL Validation under the Well-founded Semantics." In 21st International Conference on Principles of Knowledge Representation and Reasoning {KR-2023}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/kr.2024/52.

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W3C has recently introduced SHACL as a new standard for writing integrity constraints on graph-structured data (specifically, on RDF graphs). Unfortunately, the standard defines the semantics of non-recursive constraints only, leaving the case of recursive constraints open. This has spurred recent research efforts into finding a suitable, mathematically crisp semantics for constraints with cyclic dependencies. In this paper, we argue that recursive SHACL can be naturally equipped with a semantics inspired in the well-founded semantics for recursive logic programs with default negation. This se
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Звіти організацій з теми "Graph-based representation and reasoning"

1

Lutz, Carsten. Interval-based Temporal Reasoning with General TBoxes. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.109.

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Aus der Motivation: Description Logics (DLs) are a family of formalisms well-suited for the representation of and reasoning about knowledge. Whereas most Description Logics represent only static aspects of the application domain, recent research resulted in the exploration of various Description Logics that allow to, additionally, represent temporal information, see [4] for an overview. The approaches to integrate time differ in at least two important aspects: First, the basic temporal entity may be a time point or a time interval. Second, the temporal structure may be part of the semantics (y
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2

Brandt, Sebastian. Reasoning in ELH w.r.t. General Concept Inclusion Axioms. Technische Universität Dresden, 2004. http://dx.doi.org/10.25368/2022.140.

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In the area of Description Logic (DL) based knowledge representation, research on reasoning w.r.t. general terminologies has mainly focused on very expressive DLs. Recently, though, it was shown for the DL EL, providing only the constructors conjunction and existential restriction, that the subsumption problem w.r.t. cyclic terminologies can be decided in polynomial time, a surprisingly low upper bound. In this paper, we show that even admitting general concept inclusion (GCI) axioms and role hierarchies in EL terminologies preserves the polynomial time upper bound for subsumption. We also sho
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3

Lutz, Carsten. TheComplexity of Reasoning with Concrete Domains (Revised Version). Aachen University of Technology, 1999. http://dx.doi.org/10.25368/2022.88.

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Анотація:
Description logics are knowledge representation and reasoning formalisms which represent conceptual knowledge on an abstract logical level. Concrete domains are a theoretically well-founded approach to the integration of description logic reasoning with reasoning about concrete objects such as numbers, time intervals or spatial regions. In this paper, the complexity of combined reasoning with description logcis and on concrete domains is investigated. We extend ALC(D), which is the basic description logic for reasoning with concrete domains, by the operators 'feature agreement' and 'feature di
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4

Ruvinsky, Alicia, Maria Seale, R. Salter, and Natàlia Garcia-Reyero. An ontology for an epigenetics approach to prognostics and health management. Engineer Research and Development Center (U.S.), 2023. http://dx.doi.org/10.21079/11681/46632.

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Анотація:
Techniques in prognostics and health management have advanced considerably in the last few decades, enabled by breakthroughs in computational methods and supporting technologies. These predictive models, whether data-driven or physics-based, target the modeling of a system’s aggregate performance. As such, they generalize assumptions about the modelled system’s components, and are thus limited in their ability to represent individual components and the dynamic environmental factors that affect composite system health. To address this deficiency, we have developed an epigenetics-inspired knowle
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5

Lutz, Carsten, Carlos Areces, Ian Horrocks, and Ulrike Sattler. Keys, Nominals, and Concrete Domains. Technische Universität Dresden, 2002. http://dx.doi.org/10.25368/2022.122.

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Анотація:
Many description logics (DLs) combine knowledge representation on an abstract, logical level with an interface to 'concrete' domains such as numbers and strings with built-in predicates such as <, +, and prefix-of. These hybrid DLs have turned out to be quite useful for reasoning about conceptual models of information systems, and as the basis for expressive ontology languages. We propose to further extend such DLs with key constraints that allow the expression of statements like 'US citizens are uniquely identified by their social security number'. Based on this idea, we introduce a number
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6

Madhusudan, T. N. A Review of Bond-Graph Representation Based Design Methodologies. Defense Technical Information Center, 1995. http://dx.doi.org/10.21236/ada311292.

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7

SENGLAUB, MICHAEL E., DAVID L. HARRIS, and ELAINE M. RAYBOURN. Foundations for Reasoning in Cognition-Based Computational Representations of Human Decision Making. Office of Scientific and Technical Information (OSTI), 2001. http://dx.doi.org/10.2172/789585.

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8

Patwardhan, Kedar A., Guillermo Sapiro, and Vassilios Morellas. A Graph-based Foreground Representation and Its Application in Example Based People Matching in Video (PREPRINT). Defense Technical Information Center, 2007. http://dx.doi.org/10.21236/ada478409.

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9

Ferracina, Fabiana, Payton Beeler, Mahantesh Halappanavar, Bala Krishnamoorthy, Marco Minutoli, and Laura Fierce. Expanding the representation of aerosol, cloud, and precipitation processes with graph network-based simulators. Office of Scientific and Technical Information (OSTI), 2024. http://dx.doi.org/10.2172/2462750.

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10

Kularatne, Dhanushka N., Subhrajit Bhattacharya, and M. Ani Hsieh. Computing Energy Optimal Paths in Time-Varying Flows. Drexel University, 2016. http://dx.doi.org/10.17918/d8b66v.

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Autonomous marine vehicles (AMVs) are typically deployed for long periods of time in the ocean to monitor different physical, chemical, and biological processes. Given their limited energy budgets, it makes sense to consider motion plans that leverage the dynamics of the surrounding flow field so as to minimize energy usage for these vehicles. In this paper, we present two graph search based methods to compute energy optimal paths for AMVs in two-dimensional (2-D) time-varying flows. The novelty of the proposed algorithms lies in a unique discrete graph representation of the 3-D configuration
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