Relevant bibliographies by topics / Knowledge Graphs

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

Academic literature on the topic 'Knowledge Graphs'

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Published: 4 June 2021
Last updated: 15 March 2025

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Journal articles on the topic "Knowledge Graphs"

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Gutierrez, Claudio, and Juan F. Sequeda. "Knowledge graphs." Communications of the ACM 64, no. 3 (March 2021): 96–104. http://dx.doi.org/10.1145/3418294.

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Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Sabrina Kirrane, et al. "Knowledge Graphs." Synthesis Lectures on Data, Semantics, and Knowledge 12, no. 2 (November 8, 2021): 1–257. http://dx.doi.org/10.2200/s01125ed1v01y202109dsk022.

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Hogan, Aidan, Eva Blomqvist, Michael Cochez, Claudia D’amato, Gerard De Melo, Claudio Gutierrez, Sabrina Kirrane, et al. "Knowledge Graphs." ACM Computing Surveys 54, no. 4 (July 2021): 1–37. http://dx.doi.org/10.1145/3447772.

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In this article, we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models, as well as languages used to query and validate knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We conclude with high-level future research directions for knowledge graphs.
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Weikum, Gerhard. "Knowledge graphs 2021." Proceedings of the VLDB Endowment 14, no. 12 (July 2021): 3233–38. http://dx.doi.org/10.14778/3476311.3476393.

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Providing machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e.g., height of buildings or energy efficiency of cars), from text and web tables, presents an opportunity to further enhance the scope and value of knowledge bases.
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Khan, Arijit. "Knowledge Graphs Querying." ACM SIGMOD Record 52, no. 2 (August 10, 2023): 18–29. http://dx.doi.org/10.1145/3615952.3615956.

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Knowledge graphs (KGs) such as DBpedia, Freebase, YAGO, Wikidata, and NELL were constructed to store large-scale, real-world facts as (subject, predicate, object) triples - that can also be modeled as a graph, where a node (a subject or an object) represents an entity with attributes, and a directed edge (a predicate) is a relationship between two entities. Querying KGs is critical in web search, question answering (QA), semantic search, personal assistants, fact checking, and recommendation. While significant progress has been made on KG construction and curation, thanks to deep learning recently we have seen a surge of research on KG querying and QA. The objectives of our survey are two-fold. First, research on KG querying has been conducted by several communities, such as databases, data mining, semantic web, machine learning, information retrieval, and natural language processing (NLP), with different focus and terminologies; and also in diverse topics ranging from graph databases, query languages, join algorithms, graph patterns matching, to more sophisticated KG embedding and natural language questions (NLQs). We aim at uniting different interdisciplinary topics and concepts that have been developed for KG querying. Second, many recent advances on KG and query embedding, multimodal KG, and KG-QA come from deep learning, IR, NLP, and computer vision domains. We identify important challenges of KG querying that received less attention by graph databases, and by the DB community in general, e.g., incomplete KG, semantic matching, multimodal data, and NLQs. We conclude by discussing interesting opportunities for the data management community, for instance, KG as a unified data model and vector-based query processing.
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Liu, Wenqiang, Jun Liu, Mengmeng Wu, Samar Abbas, Wei Hu, Bifan Wei, and Qinghua Zheng. "Representation learning over multiple knowledge graphs for knowledge graphs alignment." Neurocomputing 320 (December 2018): 12–24. http://dx.doi.org/10.1016/j.neucom.2018.08.070.

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Telnov, V. P., and Yu A. Korovin. "Programming of Knowledge Graphs, Reasoning on Graphs." PROGRAMMNAYA INGENERIA 10, no. 2 (February 26, 2019): 59–68. http://dx.doi.org/10.17587/prin.10.59-68.

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Shah, Rita. "Reimagine Pharma Regulatory Operations using Knowledge Graphs." International Journal of Science and Research (IJSR) 13, no. 5 (May 5, 2024): 1372–73. http://dx.doi.org/10.21275/sr24517123345.

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Chen, Xuelu, Muhao Chen, Weijia Shi, Yizhou Sun, and Carlo Zaniolo. "Embedding Uncertain Knowledge Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 3363–70. http://dx.doi.org/10.1609/aaai.v33i01.33013363.

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Embedding models for deterministic Knowledge Graphs (KG) have been extensively studied, with the purpose of capturing latent semantic relations between entities and incorporating the structured knowledge they contain into machine learning. However, there are many KGs that model uncertain knowledge, which typically model the inherent uncertainty of relations facts with a confidence score, and embedding such uncertain knowledge represents an unresolved challenge. The capturing of uncertain knowledge will benefit many knowledge-driven applications such as question answering and semantic search by providing more natural characterization of the knowledge. In this paper, we propose a novel uncertain KG embedding model UKGE, which aims to preserve both structural and uncertainty information of relation facts in the embedding space. Unlike previous models that characterize relation facts with binary classification techniques, UKGE learns embeddings according to the confidence scores of uncertain relation facts. To further enhance the precision of UKGE, we also introduce probabilistic soft logic to infer confidence scores for unseen relation facts during training. We propose and evaluate two variants of UKGE based on different confidence score modeling strategies. Experiments are conducted on three real-world uncertain KGs via three tasks, i.e. confidence prediction, relation fact ranking, and relation fact classification. UKGE shows effectiveness in capturing uncertain knowledge by achieving promising results, and it consistently outperforms baselines on these tasks.
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Noy, Natasha, Yuqing Gao, Anshu Jain, Anant Narayanan, Alan Patterson, and Jamie Taylor. "Industry-scale knowledge graphs." Communications of the ACM 62, no. 8 (July 24, 2019): 36–43. http://dx.doi.org/10.1145/3331166.

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Dissertations / Theses on the topic "Knowledge Graphs"

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Ghiasnezhad, Omran Pouya. "Rule Learning in Knowledge Graphs." Thesis, Griffith University, 2018. http://hdl.handle.net/10072/382680.

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With recent advancements in knowledge extraction and knowledge management systems, an enormous number of knowledge bases have been constructed, such as YAGO, and Wikidata. These automatically built knowledge bases which contain millions of entities and their relations have been stored in graph-based schemas, and thus are usually referred to as knowledge graphs (KGs). Since KGs have been built based on the limited available data, they are far from complete. However, learning frequent patterns in the form of logical rules from these incomplete KGs has two main advantages. First, by applying the learned rules, we can infer new facts, so we could complete the KGs. Second, the rules are stand-alone knowledge which express valuable insight about the data. However, learning rules from KGs in relation to the real-world scenarios imposes several challenges. First, due to the vast size of real-world KGs, developing a rule learning method is challenging. In fact, existing methods are not scalable for learning rst order rules, while various optimisation strategies are used such as sampling and language bias (i.e., restrictions on the form of rules). Second, applying the learned rules to the vast KG and inferring new facts is another di cult issue. Learned rules usually contain a lot of noises and adding new facts can cause inconsistency of KGs. Third, it is useful but non-trivial to extend an existing method of rule learning to the case of stream KGs. Forth, in many data repositories, the facts are augmented with time stamps. In this case, we face a stream of data (KGs). Considering time as a new dimension of data imposes some challenges to the rule learning process. It would be useful to construct a time-sensitive model from the stream of data and apply the obtained model to stream KGs. Last, the density of information in a KG is varied. Although the size of a KG is vast, it contains a limited amount of information for some relations. Consequently, that part of KG is sparse. Learning a set of accurate and informative rules regarding the sparse part of a KG is challenging due to the lack of su cient training data. In this thesis, we investigate these research problems and present our methods for rule learning in various scenarios. We have rst developed a new approach, named Rule Learning via Learning Representation (RLvLR), to learning rules from KGs by using the technique of embedding in representation learning together with a new sampling method. RLvLR learns rst-order rules from vast KGs by exploring the embedding space. It can handle some large KGs that cannot be handled by existing rule learners e ciently, due to a novel sampling method. To improve the performance of RLvLR for handling sparse data, we propose a transfer learning method, Transfer Rule Learner (TRL), for rule learning. Based on a similarity characterised by the embedding representation, our method is able to select most relevant KGs and rules to transfer from a pool of KGs whose rules have been obtained. We have also adapted RLvLR to handle stream KGs instead of static KGs. Then a system called StreamLearner is developed for learning rules from stream KGs. These proposed methods can only learn so-called closed path rules, which is a proper subset of Horn rules. Thus, we have also developed a transfer rule learner (T-LPAD) that learns the structure of logic program with annotated disjunctions. T-LPAD is created by employing transfer learning to explore the space of rules' structures more e ciently. Various experiments have been conducted to test and validate the proposed methods. Our experimental results show that our methods outperform state-of-the-art methods in many ways.
Thesis (PhD Doctorate)
Doctor of Philosophy (PhD)
School of Info & Comm Tech
Science, Environment, Engineering and Technology
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Hossayni, Hicham. "Enabling industrial maintenance knowledge sharing by using knowledge graphs." Electronic Thesis or Diss., Institut polytechnique de Paris, 2021. http://www.theses.fr/2021IPPAS017.

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Autrefois considérée comme faisant partie des coûts généraux de l'entreprise, la maintenance industrielle est devenue critique pour la continuité de l'activité et une véritable source de données. Malgré les sommes importantes investies par les entreprises dans la fabrication intelligente, les pratiques traditionnelles en maintenance dominent toujours le paysage industriel. Dans cette thèse, nous étudions le partage des connaissances comme une solution potentielle qui peut inverser la tendance et améliorer l'activité de maintenance pour se conformer à l'esprit de l'industrie 4.0. Nous considérons spécifiquement les graphes de connaissances comme un outil permettant de partager les connaissances de maintenance entre les différents acteurs de l'industrie.Dans la première contribution de cette thèse, nous avons mené une étude de terrain à travers une campagne d'entretiens avec des experts aux profils différents et issus de divers domaines industriels. Cela nous a permis de tester l'hypothèse de l'amélioration de l'activité de maintenance via le partage des connaissances, qui est un concept assez nouveau dans de nombreuses industries. Les résultats de cette activité montrent clairement un intérêt réel pour notre approche et révèlent les différents besoins et défis à relever.La deuxième contribution est la conception et le prototype de "SemKoRe"; une solution s'appuyant sur le Web sémantique pour partager les connaissances de maintenance. Elle collecte les données liées aux défaillances de machine, les structure dans un graphe de connaissances et les partage entre tous les clients connectés pour réparer facilement les futures défaillances du même type. Une architecture flexible a été proposée pour couvrir les besoins des différents clients. SemKoRe a reçu l'approbation de plusieurs clients de Schneider implantés dans plusieurs pays et de différents segments.Dans la troisième contribution, nous avons conçu et mis en oeuvre une nouvelle solution pour la détection automatique des données sensibles dans les rapports de maintenance. En effet, les rapports de maintenance peuvent contenir des données confidentielles susceptibles de compromettre ou d'avoir un impact négatif sur l'activité de l'entreprise si elles sont révélées. Cette fonctionnalité est perçue, par les experts du domaine comme un point essentiel et critique pour SemKoRe. Elle permet d'éviter la divulgation de données sensibles lors de l'activité de partage des connaissances. Dans cette contribution, nous nous sommes appuyés sur le web sémantique et le traitement du langage naturel pour développer des modèles personnalisés pour la détection de données sensibles. La construction et l'apprentissage de tels modèles nécessitent une quantité considérable de données. Par conséquent, nous avons mis en place plusieurs services pour la collecte collaborative de données, l'annotation de texte et la construction de corpus. Aussi, une architecture et un workflow simplifié ont été proposés pour la génération et le déploiement de modèles de détection de données sensibles personnalisables sur les passerelles de périphérie.En plus de ces contributions, nous avons travaillé sur différentes fonctionnalités connexes à forte valeur ajoutée pour le projet SemKoRe, et qui ont abouti à différents brevets. Par exemple, nous avons développé et breveté une nouvelle méthode pour interagir avec les données de séries chronologiques à l'aide de critères sémantiques. Elle combine l'utilisation d'ontologies et de bases de données de séries chronologiques pour offrir un ensemble utile de fonctionnalités même sur des passerelles périphériques aux ressources limitées. Nous avons également conçu un nouvel outil qui aide les développeurs à interagir facilement avec des graphes de connaissances avec peu ou pas de connaissance des technologies du Web sémantique. Cette solution a été brevetée et s'avère utile pour d'autres projets basés sur des ontologies
Formerly considered as part of general enterprise costs, industrial maintenance has become critical for business continuity and a real source of data. Despite the heavy investments made by companies in smart manufacturing, traditional maintenance practices still dominate the industrial landscape. In this Ph.D., we investigate maintenance knowledge sharing as a potential solution that can invert the trend and enhance the maintenance activity to comply with the Industry 4.0 spirit. We specifically consider the knowledge graphs as an enabler to share the maintenance knowledge among the different industry players.In the first contribution of this thesis, we conducted a field study through a campaign of interviews with different experts with different profiles and from different industry domains. This allowed us to test the hypothesis of improving the maintenance activity via knowledge sharing which is quite a novel concept in many industries. The results of this activity clearly show a real interest in our approach and reveal the different requirements and challenges that need to be addressed.The second contribution is the concept, design, and prototype of "SemKoRe" which is a vendor-agnostic solution relying on Semantic Web technologies to share the maintenance knowledge. It gathers all machine failure-related data in the knowledge graph and shares it among all connected customers to easily solve future failures of the same type. A flexible architecture was proposed to cover the varied needs of the different customers. SemKoRe received approval of several Schneider clients located in several countries and from various segments.In the third contribution, we designed and implemented a novel solution for the automatic detection of sensitive data in maintenance reports. In fact, maintenance reports may contain some confidential data that can compromise or negatively impact the company's activity if revealed. This feature came up as the make or break point for SemKoRe for the interviewed domain experts. It allows avoiding sensitive data disclosure during the knowledge-sharing activity. In this contribution, we relied on semantic web and natural language processing techniques to develop custom models for sensitive data detection. The construction and training of such models require a considerable amount of data. Therefore, we implemented several services for collaborative data collection, text annotation, and corpus construction. Also, an architecture and a simplified workflow were proposed for the generation and deployment of customizable sensitive data detection models on edge gateways.In addition to these contributions, we worked on different peripheral features with a strong value for the SemKoRe project, and that has resulted in different patents. For instance, we prototyped and patented a novel method to query time series data using semantic criteria. It combines the use of ontologies and time-series databases to offer a useful set of querying capabilities even on resource-constrained edge gateways. We also designed a novel tool that helps software developers to easily interact with knowledge graphs with little or no knowledge of semantic web technologies. This solution has been patented and turns out to be useful for other ontology-based projects
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Xu, Keyulu. "Graph structures, random walks, and all that : learning graphs with jumping knowledge networks." Thesis, Massachusetts Institute of Technology, 2019. https://hdl.handle.net/1721.1/121660.

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This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.
Thesis: S.M., Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, 2019
Cataloged from student-submitted PDF version of thesis.
Includes bibliographical references (pages 51-54).
Graph representation learning aims to extract high-level features from the graph structures and node features, in order to make predictions about the nodes and the graphs. Applications include predicting chemical properties of drugs, community detection in social networks, and modeling interactions in physical systems. Recent deep learning approaches for graph representation learning, namely Graph Neural Networks (GNNs), follow a neighborhood aggregation procedure, where the representation vector of a node is computed by recursively aggregating and transforming feature vectors of its neighboring nodes. We analyze some important properties of these models, and propose a strategy to overcome the limitations. In particular, the range of neighboring nodes that a node's representation draws from strongly depends on the graph structure, analogous to the spread of a random walk. To adapt to local neighborhood properties and tasks, we explore an architecture - jumping knowledge (JK) networks that flexibly leverages, for each node, different neighborhood ranges to enable better structure-aware representation. In a number of experiments on social, bioinformatics and citation networks, we demonstrate that our model achieves state-of-the-art performance. Furthermore, combining the JK framework with models like Graph Convolutional Networks, GraphSAGE and Graph Attention Networks consistently improves those models' performance.
by Keyulu Xu.
S.M.
S.M. Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science
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Mulder, Jan A. "Using discrimination graphs to represent visual knowledge." Thesis, University of British Columbia, 1985. http://hdl.handle.net/2429/25943.

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This dissertation is concerned with the representation of visual knowledge. Image features often have many different local interpretations. As a result, visual interpretations are often ambiguous and hypothetical. In many model-based vision systems the problem of representing ambiguous and hypothetical interpretations is not very specifically addressed. Generally, specialization hierarchies are used to suppress a potential explosion in local interpretations. Such a solution has problems, as many local interpretations cannot be represented by a single hierarchy. As well, ambiguous and hypothetical interpretations tend to be represented along more than one knowledge representation dimension limiting modularity in representation and control. In this dissertation a better solution is proposed. Classes of objects which have local features with similar appearance in the image are represented by discrimination graphs. Such graphs are directed and acyclic. Their leaves represent classes of elementary objects. All other nodes represent abstract (and sometimes unnatural) classes of objects, which intensionally represent the set of elementary object classes that descend from them. Rather than interpreting each image feature as an elementary object, we use the abstract class that represents the complete set of possible (elementary) objects. Following the principle of least commitment, the interpretation of each image feature is repeatedly forced into more restrictive classes as the context for the image feature is expanded, until the image no longer provides subclassification information. This approach is called discrimination vision, and it has several attractive features. First, hypothetical and ambiguous interpretations can be represented along one knowledge representation dimension. Second, the number of hypotheses represented for a single image feature can be kept small. Third, in an interpretation graph competing hypotheses can be represented in the domain of a single variable. This often eliminates the need for restructuring the graph when a hypothesis is invalidated. Fourth, the problem of resolving ambiguity can be treated as a constraint satisfaction problem which is a well researched problem in Computational Vision. Our system has been implemented as Mapsee-3, a program for interpreting sketch maps. A hierarchical arc consistency algorithm has been used to deal with the inherently hierarchical discrimination graphs. Experimental data show that, for the domain implemented, this algorithm is more efficient than standard arc consistency algorithms.
Science, Faculty of
Computer Science, Department of
Graduate
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Sandelius, Hugo. "Creating Knowledge Graphs using Distributional Semantic Models." Thesis, KTH, Skolan för datavetenskap och kommunikation (CSC), 2016. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-199702.

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This report researches a method for creating knowledge graphs, a specific way of structuring information, using distributional semantic models. Two different algorithms for selecting graph edges and two different algorithms for labelling edges are tried, and variations of those are evaluated. We perform experiments comparing our knowledge graphs with existing manually constructed knowledge graphs of high quality, with respect to graph structure and edge labels. We find that the algorithms usually produces graphs with a structure similar to that of manually constructed knowledge graphs, as long as the data set is sufficiently large and general, and that the similarity of edge labels to manually chosen edge labels vary widely depending on input.
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Salehpour, Masoud. "High-performance Query Processing over Knowledge Graphs." Thesis, The University of Sydney, 2022. https://hdl.handle.net/2123/28569.

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The label “Knowledge Graph” (KG) has been used in the literature for over four decades, typically to refer to a collection of information about real-world entities and their inter-relationships. The proliferation of KGs in recent times opens up exciting opportunities for a broad range of semantic applications such as recommendations. However, unlocking the full potential of KGs in response to the growing deployment requires data platforms to efficiently store and process the content to support various applications. What began with extensions of relational database systems to store the content of KGs led to the design and development of a number of new specialized data management systems. Although progress has been made around building efficient KG data management systems, developing high-performance systems continues to pose research challenges. In this research, we studied the efficiency of existing systems for storing and processing KG content. Our results pointed to performance inconsistencies in representative systems across diverse query types. We address this by introducing a polyglot model of KG query processing to analyze each query and match it to the best-performing available systems. Experimental evaluation highlighted that our proposed approach provides consistently high performance. Finally, we investigated leveraging emerging hardware and its benefits to RDF data management and performance. To this end, we introduced a novel index structure, RDFix, that utilizes Persistent Memory (PM) to outperform existing read-optimized indexes as shown experimentally.
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BONOMO, Mariella. "Knowledge Extraction from Biological and Social Graphs." Doctoral thesis, Università degli Studi di Palermo, 2022. https://hdl.handle.net/10447/576508.

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Many problems from real life deal with the generation of enormous, varied, dynamic, and interconnected datasets coming from different and heterogeneous sources. Analysing large volumes of data makes it possible to generate new knowledge useful for making more informed decisions, in business and beyond. From personalising customer communication to streamlining production processes, via flow and emergency management, Big Data Analytics has an impact on all processes. The potential uses of Big Data go much further: two of the largest sources of data are including individual traders’ purchasing history, the use of Biological Networks for disease prediction or the reduction and study of Biological Networks. From a computer science point of view, the networks are graphs with various characteristics specific to the application domain. This PhD Thesis focuses on the proposal of novel knowledge extraction techniques from large graphs, mainly based on Big Data methodologies. Two application contexts are considered and three specific problems have been solved: Social data, for the optimization of advertising campaigns, the comparison of user profiles, and neighborhood analysis. Biological and Medical data, with the final aim of identifying biomarkers for diagnosis, treatment, prognosis, and prevention of diseases.
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Simonne, Lucas. "Mining differential causal rules in knowledge graphs." Electronic Thesis or Diss., université Paris-Saclay, 2023. http://www.theses.fr/2023UPASG008.

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La fouille de règles d'association au sein de graphes de connaissances est un domaine de recherche important.En effet, ce type de règle permet de représenter des connaissances, et leur application permet de compléter un graphe en ajoutant des données manquantes ou de supprimer des données erronées.Cependant, ces règles ne permettent pas d'exprimer des relations causales, dont la sémantique diffère d'une simple association ou corrélation. Dans un système, un lien de causalité entre une variable A et une variable B est une relation orientée de A vers B et indique qu'un changement dans A cause un changement dans B, les autres variables du système conservant les mêmes valeurs.Plusieurs cadres d'étude existent pour déterminer des relations causales, dont le modèle d'étude des résultats potentiels, qui consiste à apparier des instances similaires ayant des valeurs différentes sur une variable nommée traitement pour étudier l'effet de ce traitement sur une autre variable nommée résultat.Nous proposons dans cette thèse plusieurs approches permettant de définir des règles représentant l'effet causal d'un traitement sur un résultat.Cet effet peut être local, i.e., valide pour un sous-ensemble d'instances d'un graphe de connaissances défini par un motif de graphe, ou bien moyen, i.e., valide en moyenne pour l'ensemble d'instances de la classe considérée. La découverte de ces règles se base sur le cadre d'étude des résultats potentiels en appariant des instances similaires, en comparant leurs descriptions RDF au sein du graphe ou bien leurs représentations vectorielles apprises à travers des modèles de plongements de graphes
The mining of association rules within knowledge graphs is an important area of research.Indeed, this type of rule makes it possible to represent knowledge, and their application makes it possible to complete a knowledge graph by adding missing triples or to remove erroneous triples.However, these rules express associations and do not allow the expression of causal relations, whose semantics differ from an association or a correlation.In a system, a causal link between variable A and variable B is a relationship oriented from A to B. It indicates that a change in A causes a change in B, with the other variables in the system maintaining the same values.Several frameworks exist for determining causal relationships, including the potential outcome framework, which involves matching similar instances with different values on a variable named treatment to study the effect of that treatment on another variable named the outcome.In this thesis, we propose several approaches to define rules representing a causal effect of a treatment on an outcome.This effect can be local, i.e., valid for a subset of instances of a knowledge graph defined by a graph pattern, or average, i.e., valid on average for the whole set of graph instances.The discovery of these rules is based on the framework of studying potential outcomes by matching similar instances and comparing their RDF descriptions or their learned vectorial representations through graph embedding models
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Boschin, Armand. "Machine learning techniques for automatic knowledge graph completion." Electronic Thesis or Diss., Institut polytechnique de Paris, 2023. http://www.theses.fr/2023IPPAT016.

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Un graphe de connaissances est un graphe orienté dont les nœuds sont des entités et les arêtes, typées par une relation, représentent des faits connus liant les entités. Ces graphes sont capables d'encoder une grande variété d'information mais leur construction et leur exploitation peut se révéler complexe. Historiquement, des méthodes symboliques ont permis d'extraire des règles d'interaction entre entités et relations, afin de corriger des anomalies ou de prédire des faits manquants. Plus récemment, des méthodes d'apprentissage de représentations vectorielles, ou plongements, ont tenté de résoudre ces mêmes tâches. Initialement purement algébriques ou géométriques, ces méthodes se sont complexifiées avec les réseaux de neurones profonds et ont parfois été combinées à des techniques symboliques antérieures.Dans cette thèse, on s'intéresse tout d'abord au problème de l'implémentation. En effet, la grande diversité des bibliothèques utilisées rend difficile la comparaison des résultats obtenus par différents modèles. Dans ce contexte, la bibliothèque Python TorchKGE a été développée afin de proposer un environnement unique pour l'implémentation de modèles de plongement et un module hautement efficace d'évaluation par prédiction de liens. Cette bibliothèque repose sur l'accélération graphique de calculs tensoriels proposée par PyTorch, est compatible avec les bibliothèques d'optimisation usuelles et est disponible en source ouverte.Ensuite, les travaux portent sur l'enrichissement automatique de Wikidata par typage des hyperliens liant les articles de Wikipedia. Une étude préliminaire a montré que le graphe des articles de Wikipedia est beaucoup plus dense que le graphe de connaissances correspondant dans Wikidata. Une nouvelle méthode d'entrainement impliquant les relations et une méthode d'inférence utilisant les types des entités ont été proposées et des expériences ont montré la pertinence de l'approche, y compris sur un nouveau jeu de données.Enfin, le typage automatique d'entités est exploré comme une tâche de classification hiérarchique. Ceci a mené à la conception d'une fonction d'erreur hiérarchique, utilisée pour l'entrainement de modèles tensoriels, ainsi qu'un nouveau type d'encodeur. Des expériences ont permis une bonne compréhension de l'impact que peut avoir une connaissance a priori de la taxonomie des classes sur la classification. Elles ont aussi renforcé l'intuition que la hiérarchie peut être apprise à partir des données si le jeu est suffisamment riche
A knowledge graph is a directed graph in which nodes are entities and edges, typed by a relation, represent known facts linking two entities. These graphs can encode a wide variety of information, but their construction and exploitation can be complex. Historically, symbolic methods have been used to extract rules about entities and relations, to correct anomalies or to predict missing facts. More recently, techniques of representation learning, or embeddings, have attempted to solve these same tasks. Initially purely algebraic or geometric, these methods have become more complex with deep neural networks and have sometimes been combined with pre-existing symbolic techniques.In this thesis, we first focus on the problem of implementation. Indeed, the diversity of libraries used makes the comparison of results obtained by different models a complex task. In this context, the Python library TorchKGE was developed to provide a unique setup for the implementation of embedding models and a highly efficient inference evaluation module. This library relies on graphic acceleration of tensor computation provided by PyTorch, is compatible with widespread optimization libraries and is available as open source.We then consider the automatic enrichment of Wikidata by typing the hyperlinks linking Wikipedia pages. A preliminary study showed that the graph of Wikipedia articles is much denser than the corresponding knowledge graph in Wikidata. A new training method involving relations and an inference method using entity types were proposed and experiments showed the relevance of the combined approach, including on a new dataset.Finally, we explore automatic entity typing as a hierarchical classification task. That led to the design of a new hierarchical loss used to train tensor-based models along with a new type of encoder. Experiments on two datasets have allowed a good understanding of the impact a prior knowledge of class taxonomy can have on a classifier but also reinforced the intuition that the hierarchy can be learned from the features if the dataset is large enough
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Maher, Peter E. "A Prolog implementation of conceptual graphs." Thesis, Cardiff University, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.257650.

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Books on the topic "Knowledge Graphs"

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli, et al. Knowledge Graphs. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0.

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Fensel, Dieter, Umutcan Şimşek, Kevin Angele, Elwin Huaman, Elias Kärle, Oleksandra Panasiuk, Ioan Toma, Jürgen Umbrich, and Alexander Wahler. Knowledge Graphs. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-37439-6.

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Villazón-Terrazas, Boris, Fernando Ortiz-Rodríguez, Sanju Tiwari, Ayush Goyal, and MA Jabbar, eds. Knowledge Graphs and Semantic Web. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-91305-2.

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van Erp, Marieke, Sebastian Hellmann, John P. McCrae, Christian Chiarcos, Key-Sun Choi, Jorge Gracia, Yoshihiko Hayashi, et al., eds. Knowledge Graphs and Language Technology. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-68723-0.

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Mineau, Guy W., Bernard Moulin, and John F. Sowa, eds. Conceptual Graphs for Knowledge Representation. Berlin, Heidelberg: Springer Berlin Heidelberg, 1993. http://dx.doi.org/10.1007/3-540-56979-0.

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Villazón-Terrazas, Boris, Fernando Ortiz-Rodríguez, Sanju M. Tiwari, and Shishir K. Shandilya, eds. Knowledge Graphs and Semantic Web. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65384-2.

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Villazón-Terrazas, Boris, and Yusniel Hidalgo-Delgado, eds. Knowledge Graphs and Semantic Web. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-21395-4.

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Villazón-Terrazas, Boris, Fernando Ortiz-Rodriguez, Sanju Tiwari, Miguel-Angel Sicilia, and David Martín-Moncunill, eds. Knowledge Graphs and Semantic Web. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-21422-6.

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Ortiz-Rodriguez, Fernando, Boris Villazón-Terrazas, Sanju Tiwari, and Carlos Bobed, eds. Knowledge Graphs and Semantic Web. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-47745-4.

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Serles, Umutcan, and Dieter Fensel. An Introduction to Knowledge Graphs. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45256-7.

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Book chapters on the topic "Knowledge Graphs"

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli, et al. "Inductive Knowledge." In Knowledge Graphs, 67–104. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_5.

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli, et al. "Deductive Knowledge." In Knowledge Graphs, 47–65. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_4.

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Tommasini, Riccardo, Paul Groth, and empty Juan. "Knowledge Graphs." In Encyclopedia of Big Data Technologies, 1–7. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-319-63962-8_341-1.

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Serles, Umutcan, and Dieter Fensel. "Knowledge Graphs." In An Introduction to Knowledge Graphs, 85–87. Cham: Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-45256-7_10.

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Gujral, Garima, and J. Shivarama. "Knowledge Graphs." In Data Science with Semantic Technologies, 13–28. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003310792-2.

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Feng, Zhiwei. "Knowledge Graphs." In Formal Analysis for Natural Language Processing: A Handbook, 753–85. Singapore: Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-16-5172-4_12.

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Shamaeva, Irina, and David Galley. "Knowledge Graphs." In Custom Search – Discover more:, 97–101. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003100133-16.

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Aggarwal, Charu C. "Knowledge Graphs." In Artificial Intelligence, 409–35. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-72357-6_12.

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Kejriwal, Mayank. "Knowledge Graphs." In Applied Data Science in Tourism, 423–49. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-88389-8_20.

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Hogan, Aidan, Claudio Gutierrez, Michael Cochcz, Gerard de Melo, Sabrina Kirranc, Axel Pollcrcs, Roberto Navigli, et al. "Data Graphs." In Knowledge Graphs, 5–23. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-01918-0_2.

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Conference papers on the topic "Knowledge Graphs"

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Xie, Xinjiu, and Jinxian Zhang. "Link Prediction in Knowledge Graphs for Graph Convolutional Networks." In 2024 International Conference on Intelligent Algorithms for Computational Intelligence Systems (IACIS), 1–6. IEEE, 2024. http://dx.doi.org/10.1109/iacis61494.2024.10721627.

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Liao, Wen-Hwa, Hsin-Fa Wen, and Ssu-Chi Kuai. "Recommendation System using Knowledge Graphs." In 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), 49–50. IEEE, 2024. http://dx.doi.org/10.1109/icce-taiwan62264.2024.10674500.

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Huang, Weide, Linlan Liu, and Jian Shu. "A Lightweight GNN-Based Graph Embedding Method for Knowledge Graphs." In 2024 IEEE 7th International Conference on Big Data and Artificial Intelligence (BDAI), 18–22. IEEE, 2024. http://dx.doi.org/10.1109/bdai62182.2024.10692622.

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Aghaei, Sareh, and Anna Fensel. "Finding Similar Entities Across Knowledge Graphs." In 7th International Conference on Advances in Computer Science and Information Technology (ACSTY 2021). AIRCC Publishing Corporation, 2021. http://dx.doi.org/10.5121/csit.2021.110301.

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Finding similar entities among knowledge graphs is an essential research problem for knowledge integration and knowledge graph connection. This paper aims at finding semantically similar entities between two knowledge graphs. This can help end users and search agents more effectively and easily access pertinent information across knowledge graphs. Given a query entity in one knowledge graph, the proposed approach tries to find the most similar entity in another knowledge graph. The main idea is to leverage graph embedding, clustering, regression and sentence embedding. In this approach, RDF2Vec has been employed to generate vector representations of all entities of the second knowledge graph and then the vectors have been clustered based on cosine similarity using K medoids algorithm. Then, an artificial neural network with multilayer perception topology has been used as a regression model to predict the corresponding vector in the second knowledge graph for a given vector from the first knowledge graph. After determining the cluster of the predicated vector, the entities of the detected cluster are ranked through sentence-BERT method and finally the entity with the highest rank is chosen as the most similar one. To evaluate the proposed approach, experiments have been conducted on real-world knowledge graphs. The experimental results demonstrate the effectiveness of the proposed approach.
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Vargas, Hernán, Carlos Buil-Aranda, Aidan Hogan, and Claudia López. "A User Interface for Exploring and Querying Knowledge Graphs (Extended Abstract)." 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.2020/666.

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As the adoption of knowledge graphs grows, more and more non-experts users need to be able to explore and query such graphs. These users are not typically familiar with graph query languages such as SPARQL, and may not be familiar with the knowledge graph's structure. In this extended abstract, we provide a summary of our work on a language and visual interface -- called RDF Explorer -- that help non-expert users to navigate and query knowledge graphs. A usability study over Wikidata shows that users successfully complete more tasks with RDF Explorer than with the existing Wikidata Query Helper interface.
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Gutierrez, Claudio, and Juan F. Sequeda. "Knowledge Graphs." In CIKM '20: The 29th ACM International Conference on Information and Knowledge Management. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3340531.3412176.

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Faralli, Stefano, Irene Finocchi, Simone Paolo Ponzetto, and Paola Velardi. "Efficient Pruning of Large Knowledge Graphs." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. California: International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/564.

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In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information and without incurring in computational bottlenecks, which are the main problem of state-of-the-art methods for cleaning large, i.e., Web-scale, knowledge graphs. We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a SemEval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from the Wikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency.
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Fu, Xiaoyi, Jie Zhang, Hao Yu, Jiachen Li, Dong Chen, Jie Yuan, and Xindong Wu. "A Speech-to-Knowledge-Graph Construction System." 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.2020/777.

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This paper presents a HAO-Graph system that generates and visualizes knowledge graphs from a speech in real-time. When a user speaks to the system, HAO-Graph transforms the voice into knowledge graphs with key phrases from the original speech as nodes and edges. Different from language-to-language systems, such as Chinese-to-English and English-to-English, HAO-Graph converts a speech into graphs, and is the first of its kind. The effectiveness of our HAO-Graph system is verified by a two-hour chairman's talk in front of two thousand participants at an annual meeting in the form of a satisfaction survey.
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Seyler, Dominic, Mohamed Yahya, and Klaus Berberich. "Knowledge Questions from Knowledge Graphs." In ICTIR '17: ACM SIGIR International Conference on the Theory of Information Retrieval. New York, NY, USA: ACM, 2017. http://dx.doi.org/10.1145/3121050.3121073.

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Liu, Jiaqi, Qin Zhang, Luoyi Fu, Xinbing Wang, and Songwu Lu. "Evolving Knowledge Graphs." In IEEE INFOCOM 2019 - IEEE Conference on Computer Communications. IEEE, 2019. http://dx.doi.org/10.1109/infocom.2019.8737547.

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Reports on the topic "Knowledge Graphs"

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Cao, Larry. IV. Chatbot, Knowledge Graphs, and AI Infrastructure. CFA Institute Research Foundation, April 2023. http://dx.doi.org/10.56227/23.1.10.

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Expert contributors discuss AI and big data applications that are being developed for financial services, such as AI-powered intelligent customer service systems; “factories” for data processing, AI, simulation, and visualization; and symbolic AI.
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Kriegel, Francesco. Terminological knowledge aquisition in probalistic description logic. Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.239.

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For a probabilistic extension of the description logic EL⊥, we consider the task of automatic acquisition of terminological knowledge from a given probabilistic interpretation. Basically, such a probabilistic interpretation is a family of directed graphs the vertices and edges of which are labeled, and where a discrete probabilitymeasure on this graph family is present. The goal is to derive so-called concept inclusions which are expressible in the considered probabilistic description logic and which hold true in the given probabilistic interpretation. A procedure for an appropriate axiomatization of such graph families is proposed and its soundness and completeness is justified.
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Kriegel, Francesco. Learning description logic axioms from discrete probability distributions over description graphs (Extended Version). Technische Universität Dresden, 2018. http://dx.doi.org/10.25368/2022.247.

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Description logics in their standard setting only allow for representing and reasoning with crisp knowledge without any degree of uncertainty. Of course, this is a serious shortcoming for use cases where it is impossible to perfectly determine the truth of a statement. For resolving this expressivity restriction, probabilistic variants of description logics have been introduced. Their model-theoretic semantics is built upon so-called probabilistic interpretations, that is, families of directed graphs the vertices and edges of which are labeled and for which there exists a probability measure on this graph family. Results of scientific experiments, e.g., in medicine, psychology, or biology, that are repeated several times can induce probabilistic interpretations in a natural way. In this document, we shall develop a suitable axiomatization technique for deducing terminological knowledge from the assertional data given in such probabilistic interpretations. More specifically, we consider a probabilistic variant of the description logic EL⊥, and provide a method for constructing a set of rules, so-called concept inclusions, from probabilistic interpretations in a sound and complete manner.
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Borchmann, Daniel, Felix Distel, and Francesco Kriegel. Axiomatization of General Concept Inclusions from Finite Interpretations. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.219.

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Description logic knowledge bases can be used to represent knowledge about a particular domain in a formal and unambiguous manner. Their practical relevance has been shown in many research areas, especially in biology and the semantic web. However, the tasks of constructing knowledge bases itself, often performed by human experts, is difficult, time-consuming and expensive. In particular the synthesis of terminological knowledge is a challenge every expert has to face. Because human experts cannot be omitted completely from the construction of knowledge bases, it would therefore be desirable to at least get some support from machines during this process. To this end, we shall investigate in this work an approach which shall allow us to extract terminological knowledge in the form of general concept inclusions from factual data, where the data is given in the form of vertex and edge labeled graphs. As such graphs appear naturally within the scope of the Semantic Web in the form of sets of RDF triples, the presented approach opens up the possibility to extract terminological knowledge from the Linked Open Data Cloud. We shall also present first experimental results showing that our approach has the potential to be useful for practical applications.
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Küsters, Ralf, and Ralf Molitor. Computing Most Specific Concepts in Description Logics with Existential Restrictions. Aachen University of Technology, 2000. http://dx.doi.org/10.25368/2022.108.

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Computing the most specific concept (msc) is an inference task that can be used to support the 'bottom-up' construction of knowledge bases for KR systems based on description logics. For description logics that allow for number restrictions or existential restrictions, the msc need not exist, though. Previous work on this problem has concentrated on description logics that allow for universal value restrictions and number restrictions, but not for existential restrictions. The main new contribution of this paper is the treatment of description logics with existential restrictions. More precisely, we show that, for the description logic ALE (which allows for conjunction, universal value restrictions, existential restrictions, negation of atomic concepts) the msc of an ABox-individual only exists in case of acyclic ABoxes. For cyclic ABoxes, we show how to compute an approximation of the msc. Our approach for computing the (approximation of the) msc is based on representing concept descriptions by certain trees and ABoxes by certain graphs, and then characterizing instance relationships by homomorphisms from trees into graphs. The msc/approximation operation then mainly corresponds to unraveling the graphs into trees and translating them back into concept descriptions.
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Goldberg, Sean, and Daisy Zhe Wang. Graph Learning in Knowledge Bases. Office of Scientific and Technical Information (OSTI), September 2017. http://dx.doi.org/10.2172/1390764.

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Reiter, Ehud. Knowledge-Based Automatic Graph Layout. Fort Belvoir, VA: Defense Technical Information Center, June 1995. http://dx.doi.org/10.21236/ada296735.

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Horan, Victoria, and Michael Gudaitis. Investigation of Zero Knowledge Proof Approaches Based on Graph Theory. Fort Belvoir, VA: Defense Technical Information Center, February 2011. http://dx.doi.org/10.21236/ada540835.

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Brian, Dominikus. Exploring the Intersection of Artificial Intelligence and Decentralized Science: The Decentralized Knowledge Graph. ResearchHub Technologies, Inc., August 2024. http://dx.doi.org/10.55277/researchhub.nwoettub.

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Kenkel, Donald, Alan Mathios, Grace Phillips, Revathy Suryanarayana, Hua Wang, and Sen Zeng. Fear or Knowledge The Impact of Graphic Cigarette Warnings on Tobacco Product Choices. Cambridge, MA: National Bureau of Economic Research, August 2023. http://dx.doi.org/10.3386/w31534.

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