Embedding Knowledge Graphs With Rdf2vec
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Embedding Knowledge Graphs With Rdf2vec
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Author : Heiko Paulheim
language : en
Publisher: Springer Nature
Release Date : 2023-06-03
Embedding Knowledge Graphs With Rdf2vec written by Heiko Paulheim and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-03 with Computers categories.
This book explains the ideas behind one of the most well-known methods for knowledge graph embedding of transformations to compute vector representations from a graph, known as RDF2vec. The authors describe its usage in practice, from reusing pre-trained knowledge graph embeddings to training tailored vectors for a knowledge graph at hand. They also demonstrate different extensions of RDF2vec and how they affect not only the downstream performance, but also the expressivity of the resulting vector representation, and analyze the resulting vector spaces and the semantic properties they encode.
Rdf2vec Light A Lightweight Approach For Knowledge Graph Embeddings
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Author : Jan Portisch
language : en
Publisher:
Release Date : 2020
Rdf2vec Light A Lightweight Approach For Knowledge Graph Embeddings written by Jan Portisch and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Entity Matching And Disambiguation Across Multiple Knowledge Graphs
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Author : Michael Farag
language : en
Publisher:
Release Date : 2019
Entity Matching And Disambiguation Across Multiple Knowledge Graphs written by Michael Farag and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with categories.
Knowledge graphs are considered an important representation that lie between free text on one hand and fully-structured relational data on the other. Knowledge graphs are a back-bone of many applications on the Web. With the rise of many large-scale open-domain knowledge graphs like Freebase, DBpedia, and Yago, various applications including document retrieval, question answering, and data integration have been relying on them. In this thesis, We are primarily interested in knowledge graphs from the perspective of integrating disparate heterogeneous sources, with an eye towards applications such as document retrieval and question answering. Integrating different knowledge graphs is very important for enriching the knowledge shared among them. The core part of this integration process is matching entities across the knowledge graphs. The biggest challenge to entity matching is the ambiguity. The obvious solution is to make use of the graph structure and entity neighbourhoods for matching and disambiguating entities. We formalize the entity matching problem and present the rst large-scale dataset, Ambiguous DBpedia-Wikidata, for this task based on exiting cross-ontology links between DBpedia and Wikidata, focused on several hundred thousand ambiguous entities. We propose an entity matching framework that is capable of disambiguating entities across different knowledge graphs. The framework consists of fuzzy string matcher and graph embedding-based matcher. Using a classifi cation-based approach, we find that a simple multi-layered perceptron based on representations derived from RDF2VEC graph embeddings of entities in each knowledge graph is sufficient to achieve high accuracy, with only limited training data. The contribution of our work is both a large dataset for examining this problem and strong baselines on which future work can be based. We also present SimpleDBpediaQA, a new benchmark dataset for simple question answering over knowledge graphs that was created by mapping SimpleQuestions entities and predicates from Freebase to DBpedia. We show how entity matching using manual annotations can be used for migrating datasets across knowledge graphs. Although this mapping is conceptually straightforward, there are a number of nuances that make the task non-trivial, owing to the different conceptual organizations of the two knowledge graphs. Finally, if manual annotations are scarce, we show how our entity matching framework can be used to generate free annotations to train our model and then use it for disambiguation. In that essence, we introduce SimpleQuestions++, a new question answering benchmark that have all questions linked to Freebase, DBpedia, and Wikidata.
Reproducing And Explaining Entity And Relation Embeddings For Link Prediction In Knowledge Graphs
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Author : Narayanan Asuri Krishnan
language : en
Publisher:
Release Date : 2021
Reproducing And Explaining Entity And Relation Embeddings For Link Prediction In Knowledge Graphs written by Narayanan Asuri Krishnan and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with Data structures (Computer science) categories.
"Embedding knowledge graphs is a common method used to encode information from the graph at hand projected in a low dimensional space. There are two shortcomings in the field of knowledge graph embeddings for link prediction. The first shortcoming is that, as far as we know, current software libraries to compute knowledge graph embeddings differ from the original papers proposing these embeddings. Certain implementations are faithful to the original papers, while others range from minute differences to significant variations. Due to these implementation variations, it is difficult to compare the same algorithm from multiple libraries and also affects our ability to reproduce results. In this report, we describe a new framework, AugmentedKGE (aKGE), to embed knowledge graphs. The library features multiple knowledge graph embedding algorithms, a rank-based evaluator, and is developed completely using Python and PyTorch. The second shortcoming is that, during the evaluation process of link prediction, the goal is to rank based on scores a positive triple over a (typically large) number of negative triples. Accuracy metrics used in the evaluation of link prediction are aggregations of the ranks of the positive triples under evaluation and do not typically provide enough details as to why a number of negative triples are ranked higher than their positive counterparts. Providing explanations to these triples aids in understanding the results of the link predictions based on knowledge graph embeddings. Current approaches mainly focus on explaining embeddings rather than predictions and single predictions rather than all the link predictions made by the embeddings of a certain knowledge graph. In this report, we present an approach to explain all these predictions by providing two metrics that serve to quantify and compare the explainability of different embeddings. From the results of evaluating aKGE, we observe that the accuracy metrics are better than the accuracy metrics obtained from the standard implementation of OpenKE. From the results of explainability, we observe that the horn rules obtained explain more than 50% of all the negative triples generated."--Abstract.
Representation Learning Based Query Answering On Knowledge Graphs
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Author : Xuelu Chen
language : en
Publisher:
Release Date : 2021
Representation Learning Based Query Answering On Knowledge Graphs written by Xuelu Chen and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021 with categories.
Knowledge graphs provide structured representations of facts about real-world entities and relations, serving as a vital knowledge source for numerous artificial intelligence applications. This dissertation seeks to extend the scope and provide theoretical guidance for representation learning based query answering on knowledge graphs. The incompleteness of knowledge graphs has recently motivated the use of representation learning models in recent years to generalize from known facts and infer new knowledge for query answering. Despite advances in answering atomic queries by representing deterministic facts within a monolingual knowledge graph, existing models must overcome the following three challenges: (i) they must address the need to incorporate uncertainty information into query answering, which is critical to many knowledge-driven applications; (ii) they must effectively leverage complementary knowledge from knowledge graphs in different languages; (iii) they must be able to embed complex first-order logical queries.In this dissertation, we address the aforementioned challenges and extend the scope of query answering on knowledge graphs through contributions on the following three fronts: (i) To capture fact uncertainty and support reasoning under uncertainty, we propose two knowledge graph embedding models that are capable of encoding uncertain facts in the embedding space. Our proposed models thus learn entity and relation embeddings according to the confidence scores of uncertain facts. We introduce probabilistic soft logic to infer the confidence score to provide extra supervision for training. We also explore using box embeddings to embed uncertain knowledge graphs and imposing relation property constraints to enhance performance on sparse uncertain knowledge graphs. (ii) To effectively combine knowledge graphs in different languages, we introduce an ensemble learning framework that embeds all knowledge graphs in a shared embedding space, where the association of entities is captured based on self-learning. The framework performs ensemble inference to combine prediction results from embeddings of multiple language-specific knowledge graphs, for which multiple ensemble techniques are investigated. (iii) To support answering complex first-order logical queries, we present a query embedding framework based on fuzzy logic that allows us to define logical operators in a principled and learning-free manner, whereby learn- ing is only required for entity and relation embeddings. The proposed model can further benefit when complex logical queries are available for training. As a result of this research we were able to identify some of the desirable properties that embedding models ought to possess and analyze which of the existing models have these properties. Therefore, the results presented in this dissertation advance the state-of-the-art of query answering on knowledge graphs along different axes and provide conceptual guidance for future research in this field.
Knowledge Graphs
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Author : Aidan Hogan
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2021-11-08
Knowledge Graphs written by Aidan Hogan and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-11-08 with Computers categories.
This book provides a comprehensive and accessible introduction to knowledge graphs, which have recently garnered notable attention from both industry and academia. Knowledge graphs are founded on the principle of applying a graph-based abstraction to data, and are now broadly deployed in scenarios that require integrating and extracting value from multiple, diverse sources of data at large scale. The book defines knowledge graphs and provides a high-level overview of how they are used. It presents and contrasts popular graph models that are commonly used to represent data as graphs, and the languages by which they can be queried before describing how the resulting data graph can be enhanced with notions of schema, identity, and context. The book discusses how ontologies and rules can be used to encode knowledge as well as how inductive techniques—based on statistics, graph analytics, machine learning, etc.—can be used to encode and extract knowledge. It covers techniques for the creation, enrichment, assessment, and refinement of knowledge graphs and surveys recent open and enterprise knowledge graphs and the industries or applications within which they have been most widely adopted. The book closes by discussing the current limitations and future directions along which knowledge graphs are likely to evolve. This book is aimed at students, researchers, and practitioners who wish to learn more about knowledge graphs and how they facilitate extracting value from diverse data at large scale. To make the book accessible for newcomers, running examples and graphical notation are used throughout. Formal definitions and extensive references are also provided for those who opt to delve more deeply into specific topics.
Representation And Curation Of Knowledge Graphs With Embeddings
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Author : Nitisha Jain
language : en
Publisher:
Release Date : 2022
Representation And Curation Of Knowledge Graphs With Embeddings written by Nitisha Jain and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022 with categories.
Knowledge graphs are structured repositories of knowledge that store facts about the general world or a particular domain in terms of entities and their relationships. Owing to the heterogeneity of use cases that are served by them, there arises a need for the automated construction of domain- specific knowledge graphs from texts. While there have been many research efforts towards open information extraction for automated knowledge graph construction, these techniques do not perform well in domain-specific settings. Furthermore, regardless of whether they are constructed automatically from specific texts or based on real-world facts that are constantly evolving, all knowledge graphs inherently suffer from incompleteness as well as errors in the information they hold. This thesis investigates the challenges encountered during knowledge graph construction and proposes techniques for their curation (a.k.a. refinement) including the correction of semantic ambiguities and the completion of missing facts. Firstly, we leverage ...
More Is Not Always Better The Negative Impact Of A Box Materialization On Rdf2vec Knowledge Graph Embeddings
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Author : Andreea Iana
language : en
Publisher:
Release Date : 2020
More Is Not Always Better The Negative Impact Of A Box Materialization On Rdf2vec Knowledge Graph Embeddings written by Andreea Iana and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with categories.
Enriching Knowledge Graphs Using Machine Learning Techniques
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Author : Mohamed Gharibi
language : en
Publisher:
Release Date : 2020
Enriching Knowledge Graphs Using Machine Learning Techniques written by Mohamed Gharibi and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Electronic dissertations categories.
A knowledge graph represents millions of facts and reliable information about people, places, and things. These knowledge graphs have proven their reliability and their usage for providing better search results; answering ambiguous questions regarding entities; and training semantic parsers to enhance the semantic relationships over the Semantic Web. However, while there exist a plethora of datasets on the Internet related to Food, Energy, and Water (FEW), there is a real lack of reliable methods and tools that can consume these resources. This hinders the development of novel decision-making applications utilizing knowledge graphs. In this dissertation, we introduce a novel tool, called FoodKG, that enriches FEW knowledge graphs using advanced machine learning techniques. Our overarching goal is to improve decision-making, knowledge discovery, and provide improved search results for data scientists in the FEW domains. Given an input knowledge graph (constructed on raw FEW datasets), FoodKG enriches it with semantically related triples, relations, and images based on the original dataset terms and classes. FoodKG employs an existing graph embedding technique trained on a controlled vocabulary called AGROVOC, which is published by the Food and Agriculture Organization of the United Nations. AGROVOC includes terms and classes in the agriculture and food domains. As a result, FoodKG can enhance knowledge graphs with semantic similarity scores and relations between different classes, classify the existing entities, and allow FEW experts and researchers to use scientific terms for describing FEW concepts. The resulting model obtained after training on AGROVOC was evaluated against the state-of-the-art word embedding and knowledge graph embedding models that were trained on the same dataset. We observed that this model outperformed its competitors based on the Spearman Correlation Coefficient score. We introduced Federated Learning (FL) techniques to further extend our work and include private datasets by training smaller version of the models at each dataset site without accessing the data and then aggregating all the models at the server-side. We propose an algorithm that we called RefinedFed to further extend the current FL work by filtering the models at each dataset site before the aggregation phase. Our algorithm improves the current FL model accuracy from 84% to 91% on MNIST dateset.
The Semantic Web Iswc 2016
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Author : Paul Groth
language : en
Publisher: Springer
Release Date : 2016-10-05
The Semantic Web Iswc 2016 written by Paul Groth and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-10-05 with Computers categories.
The two-volume set LNCS 9981 and 9982 constitutes the refereed proceedings of the 15th International Semantic Web Conference, ISWC 2016, which was held in Kobe, Japan, in October 2016. The 75 full papers presented in these proceedings were carefully reviewed and selected from 326 submissions. The International Semantic Web Conference is the premier forum for Semantic Web research, where cutting edge scientific results and technological innovations are presented, where problems and solutions are discussed, and where the future of this vision is being developed. It brings together specialists in fields such as artificial intelligence, databases, social networks, distributed computing, Web engineering, information systems, human-computer interaction, natural language processing, and the social sciences. The Research Track solicited novel and significant research contributions addressing theoretical, analytical, empirical, and practical aspects of the Semantic Web. The Applications Track solicited submissions exploring the benefits and challenges of applying semantic technologies in concrete, practical applications, in contexts ranging from industry to government and science. The newly introduced Resources Track sought submissions providing a concise and clear description of a resource and its (expected) usage. Traditional resources include ontologies, vocabularies, datasets, benchmarks and replication studies, services and software. Besides more established types of resources, the track solicited submissions of new types of resources such as ontology design patterns, crowdsourcing task designs, workflows, methodologies, and protocols and measures.