113,346 research outputs found

    Graph Structures for Knowledge Representation and Reasoning

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    This open access book constitutes the thoroughly refereed post-conference proceedings of the 6th International Workshop on Graph Structures for Knowledge Representation and Reasoning, GKR 2020, held virtually in September 2020, associated with ECAI 2020, the 24th European Conference on Artificial Intelligence. The 7 revised full papers presented together with 2 invited contributions were reviewed and selected from 9 submissions. The contributions address various issues for knowledge representation and reasoning and the common graph-theoretic background, which allows to bridge the gap between the different communities

    First IJCAI International Workshop on Graph Structures for Knowledge Representation and Reasoning (GKR@IJCAI'09)

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    International audienceThe development of effective techniques for knowledge representation and reasoning (KRR) is a crucial aspect of successful intelligent systems. Different representation paradigms, as well as their use in dedicated reasoning systems, have been extensively studied in the past. Nevertheless, new challenges, problems, and issues have emerged in the context of knowledge representation in Artificial Intelligence (AI), involving the logical manipulation of increasingly large information sets (see for example Semantic Web, BioInformatics and so on). Improvements in storage capacity and performance of computing infrastructure have also affected the nature of KRR systems, shifting their focus towards representational power and execution performance. Therefore, KRR research is faced with a challenge of developing knowledge representation structures optimized for large scale reasoning. This new generation of KRR systems includes graph-based knowledge representation formalisms such as Bayesian Networks (BNs), Semantic Networks (SNs), Conceptual Graphs (CGs), Formal Concept Analysis (FCA), CPnets, GAI-nets, all of which have been successfully used in a number of applications. The goal of this workshop is to bring together the researchers involved in the development and application of graph-based knowledge representation formalisms and reasoning techniques

    Learning and reasoning with graph data

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    Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields ranging from logic-oriented knowledge representation and reasoning to graph kernels and statistical relational learning. In this review we outline a broad map and inventory of the field of learning and reasoning with graphs that spans the spectrum from reasoning in the form of logical deduction to learning node embeddings. To obtain a unified perspective on such a diverse landscape we introduce a simple and general semantic concept of a model that covers logic knowledge bases, graph neural networks, kernel support vector machines, and many other types of frameworks. Still at a high semantic level, we survey common strategies for model specification using probabilistic factorization and standard feature construction techniques. Based on this semantic foundation we introduce a taxonomy of reasoning tasks that casts problems ranging from transductive link prediction to asymptotic analysis of random graph models as queries of different complexities for a given model. Similarly, we express learning in different frameworks and settings in terms of a common statistical maximum likelihood principle. Overall, this review aims to provide a coherent conceptual framework that provides a basis for further theoretical analyses of respective strengths and limitations of different approaches to handling graph data, and that facilitates combination and integration of different modeling paradigms

    Joint Video and Text Parsing for Understanding Events and Answering Queries

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    We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against ground-truth as well as accuracy of query answering and obtained satisfactory results

    The Unit Graphs Framework: A new graph-based Knowledge Representation Formalism for ECD Knowledge Representation

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    International audienceIn this paper we are interested in the choice of a graph-based knowledge representation formalism that would allow for the representation, manipulation, query, and reasoning over linguistic knowledge of the Explanatory Combinatorial Dictionary of the Meaning-Text Theory (MTT). We show that neither the semantic web formalisms nor the Conceptual Graphs Formalism are suitable for this task, and we justify the introduction of the new Unit Graphs framework. We then detail the core of this formalism which is a hierarchy of unit types driven by their actantial structure. Finally we define the new deep semantic representation level for the MTT, where the specialization of actantial structures of deep semantic unit types may correspond to a specialization of conveyed meanings

    Relational learning on temporal knowledge graphs

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    Over the last decade, there has been an increasing interest in relational machine learning (RML), which studies methods for the statistical analysis of relational or graph-structured data. Relational data arise naturally in many real-world applications, including social networks, recommender systems, and computational finance. Such data can be represented in the form of a graph consisting of nodes (entities) and labeled edges (relationships between entities). While traditional machine learning techniques are based on feature vectors, RML takes relations into account and permits inference among entities. Recently, performing prediction and learning tasks on knowledge graphs has become a main topic in RML. Knowledge graphs (KGs) are widely used resources for studying multi-relational data in the form of a directed graph, where each labeled edge describes a factual statement, such as (Munich, locatedIn, Germany). Traditionally, knowledge graphs are considered to represent stationary relationships, which do not change over time. In contrast, event-based multi-relational data exhibits complex temporal dynamics in addition to its multi-relational nature. For example, the political relationship between two countries would intensify because of trade fights; the president of a country may change after an election. To represent the temporal aspect, temporal knowledge graphs (tKGs) were introduced that store a temporal event as a quadruple by extending the static triple with a timestamp describing when this event occurred, i.e. (Barack Obama, visit, India, 2010-11-06). Thus, each edge in the graph has temporal information associated with it and may recur or evolve over time. Among various learning paradigms on KGs, knowledge representation learning (KRL), also known as knowledge graph embedding, has achieved great success. KRL maps entities and relations into low-dimensional vector spaces while capturing semantic meanings. However, KRL approaches have mostly been done for static KGs and lack the ability to utilize rich temporal dynamics available on tKGs. In this thesis, we study state-of-the-art representation learning techniques for temporal knowledge graphs that can capture temporal dependencies across entities in addition to their relational dependencies. We discover representations for two inference tasks, i.e., tKG forecasting and completion. The former is to forecast future events using historical observations up to the present time, while the latter predicts missing links at observed timestamps. For tKG forecasting, we show how to make the reasoning process interpretable while maintaining performance by employing a sequential reasoning process over local subgraphs. Besides, we propose a continuous-depth multi-relational graph neural network with a novel graph neural ordinary differential equation. It allows for learning continuous-time representations of tKGs, especially in cases with observations in irregular time intervals, as encountered in online analysis. For tKG completion, we systematically review multiple benchmark models. We thoroughly investigate the significance of the proposed temporal encoding technique in each model and provide the first unified open-source framework, which gathers the implementations of well-known tKG completion models. Finally, we discuss the power of geometric learning and show that learning evolving entity representations in a product of Riemannian manifolds can better reflect geometric structures on tKGs and achieve better performances than Euclidean embeddings while requiring significantly fewer model parameters

    Knowledge-based support in Non-Destructive Testing for health monitoring of aircraft structures

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    Maintenance manuals include general methods and procedures for industrial maintenance and they contain information about principles of maintenance methods. Particularly, Non-Destructive Testing (NDT) methods are important for the detection of aeronautical defects and they can be used for various kinds of material and in different environments. Conventional non-destructive evaluation inspections are done at periodic maintenance checks. Usually, the list of tools used in a maintenance program is simply located in the introduction of manuals, without any precision as regards to their characteristics, except for a short description of the manufacturer and tasks in which they are employed. Improving the identification concepts of the maintenance tools is needed to manage the set of equipments and establish a system of equivalence: it is necessary to have a consistent maintenance conceptualization, flexible enough to fit all current equipment, but also all those likely to be added/used in the future. Our contribution is related to the formal specification of the system of functional equivalences that can facilitate the maintenance activities with means to determine whether a tool can be substituted for another by observing their key parameters in the identified characteristics. Reasoning mechanisms of conceptual graphs constitute the baseline elements to measure the fit or unfit between an equipment model and a maintenance activity model. Graph operations are used for processing answers to a query and this graph-based approach to the search method is in-line with the logical view of information retrieval. The methodology described supports knowledge formalization and capitalization of experienced NDT practitioners. As a result, it enables the selection of a NDT technique and outlines its capabilities with acceptable alternatives

    Challenges in Bridging Social Semantics and Formal Semantics on the Web

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    This paper describes several results of Wimmics, a research lab which names stands for: web-instrumented man-machine interactions, communities, and semantics. The approaches introduced here rely on graph-oriented knowledge representation, reasoning and operationalization to model and support actors, actions and interactions in web-based epistemic communities. The re-search results are applied to support and foster interactions in online communities and manage their resources
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