Related papers: Knowledge Graphs

Knowledge Graphs

URL: http://arxiv.org/abs/2003.02320v6
Date: Sat, 11 Sep 2021 21:36:53 GMT
Title: Knowledge Graphs
Authors: Aidan Hogan, Eva Blomqvist, Michael Cochez, Claudia d'Amato, Gerard de Melo, Claudio Gutierrez, Jos\'e Emilio Labra Gayo, Sabrina Kirrane, Sebastian Neumaier, Axel Polleres, Roberto Navigli, Axel-Cyrille Ngonga Ngomo, Sabbir M. Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann
Abstract summary: We motivate and contrast various graph-based data models and query languages that are used for 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.
Score: 43.06435841693428
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: In this paper 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 and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs.

Related papers

Knowledge Graph Extension by Entity Type Recognition [2.8231106019727195]
We propose a novel knowledge graph extension framework based on entity type recognition. The framework aims to achieve high-quality knowledge extraction by aligning the schemas and entities across different knowledge graphs.
arXiv Detail & Related papers (2024-05-03T19:55:03Z)
A Survey on Temporal Knowledge Graph: Representation Learning and Applications [16.447980641446602]
temporal knowledge graph representation learning aims to learn low-dimensional vector embeddings for entities and relations in a knowledge graph. We conduct a comprehensive survey of temporal knowledge graph representation learning and its applications.
arXiv Detail & Related papers (2024-03-02T16:21:45Z)
State of the Art and Potentialities of Graph-level Learning [54.68482109186052]
Graph-level learning has been applied to many tasks including comparison, regression, classification, and more. Traditional approaches to learning a set of graphs rely on hand-crafted features, such as substructures. Deep learning has helped graph-level learning adapt to the growing scale of graphs by extracting features automatically and encoding graphs into low-dimensional representations.
arXiv Detail & Related papers (2023-01-14T09:15:49Z)
Conditional Attention Networks for Distilling Knowledge Graphs in Recommendation [74.14009444678031]
We propose Knowledge-aware Conditional Attention Networks (KCAN) to incorporate knowledge graph into a recommender system. We use a knowledge-aware attention propagation manner to obtain the node representation first, which captures the global semantic similarity on the user-item network and the knowledge graph. Then, by applying a conditional attention aggregation on the subgraph, we refine the knowledge graph to obtain target-specific node representations.
arXiv Detail & Related papers (2021-11-03T09:40:43Z)
A Survey of Knowledge Graph Embedding and Their Applications [0.17205106391379024]
Knowledge graph embedding enables the real-world application to consume information to improve performance. This paper introduces growth in the field of KG embedding from simple translation-based models to enrichment-based models.
arXiv Detail & Related papers (2021-07-16T12:07:53Z)
Edge: Enriching Knowledge Graph Embeddings with External Text [32.01476220906261]
We propose a knowledge graph enrichment and embedding framework named Edge. Given an original knowledge graph, we first generate a rich but noisy augmented graph using external texts in semantic and structural level. To distill the relevant knowledge and suppress the introduced noise, we design a graph alignment term in a shared embedding space between the original graph and augmented graph.
arXiv Detail & Related papers (2021-04-11T03:47:06Z)
KompaRe: A Knowledge Graph Comparative Reasoning System [85.72488258453926]
This paper introduces comparative reasoning over knowledge graphs, which aims to infer the commonality and inconsistency with respect to multiple pieces of clues. We develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
arXiv Detail & Related papers (2020-11-06T04:57:37Z)
A Heterogeneous Graph with Factual, Temporal and Logical Knowledge for Question Answering Over Dynamic Contexts [81.4757750425247]
We study question answering over a dynamic textual environment. We develop a graph neural network over the constructed graph, and train the model in an end-to-end manner.
arXiv Detail & Related papers (2020-04-25T04:53:54Z)
A Survey on Knowledge Graphs: Representation, Acquisition and Applications [89.78089494738002]
We review research topics about 1) knowledge graph representation learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph, and 4) knowledge-aware applications. For knowledge acquisition, especially knowledge graph completion, embedding methods, path inference, and logical rule reasoning, are reviewed. We explore several emerging topics, including meta learning, commonsense reasoning, and temporal knowledge graphs.
arXiv Detail & Related papers (2020-02-02T13:17:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.