What is Event Knowledge Graph: A Survey
- URL: http://arxiv.org/abs/2112.15280v1
- Date: Fri, 31 Dec 2021 03:42:55 GMT
- Title: What is Event Knowledge Graph: A Survey
- Authors: Saiping Guan, Xueqi Cheng, Long Bai, Fujun Zhang, Zixuan Li, Yutao
Zeng, Xiaolong Jin, and Jiafeng Guo
- Abstract summary: This paper provides a comprehensive survey of Event KG (EKG) from history, ontology, instance, and application views.
EKG plays an increasingly important role in many machine learning and artificial intelligence applications, such as intelligent search, question-answering, recommendation, and text generation.
- Score: 46.56390787391834
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Besides entity-centric knowledge, usually organized as Knowledge Graph (KG),
events are also an essential kind of knowledge in the world, which trigger the
spring up of event-centric knowledge representation form like Event KG (EKG).
It plays an increasingly important role in many machine learning and artificial
intelligence applications, such as intelligent search, question-answering,
recommendation, and text generation. This paper provides a comprehensive survey
of EKG from history, ontology, instance, and application views. Specifically,
to characterize EKG thoroughly, we focus on its history, definitions, schema
induction, acquisition, related representative graphs/systems, and
applications. The development processes and trends are studied therein. We
further summarize perspective directions to facilitate future research on EKG.
Related papers
- Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey [61.8716670402084]
This survey focuses on KG-aware research in two principal aspects: KG-driven Multi-Modal (KG4MM) learning, and Multi-Modal Knowledge Graph (MM4KG)
Our review includes two primary task categories: KG-aware multi-modal learning tasks, and intrinsic MMKG tasks.
For most of these tasks, we provide definitions, evaluation benchmarks, and additionally outline essential insights for conducting relevant research.
arXiv Detail & Related papers (2024-02-08T04:04:36Z) - On the Evolution of Knowledge Graphs: A Survey and Perspective [11.061075842989817]
Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications.
We provide a comprehensive survey on the evolution of various types of KGs and techniques for knowledge extraction and reasoning.
We propose our perspective on the future directions of knowledge engineering.
arXiv Detail & Related papers (2023-10-07T14:46:51Z) - SKG: A Versatile Information Retrieval and Analysis Framework for
Academic Papers with Semantic Knowledge Graphs [9.668240269886413]
We propose a Semantic Knowledge Graph that integrates semantic concepts from abstracts and other meta-information to represent the corpus.
The SKG can support various semantic queries in academic literature thanks to the high diversity and rich information content stored within.
arXiv Detail & Related papers (2023-06-07T20:16:08Z) - A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic,
and Multimodal [57.8455911689554]
Knowledge graph reasoning (KGR) aims to deduce new facts from existing facts based on mined logic rules underlying knowledge graphs (KGs)
It has been proven to significantly benefit the usage of KGs in many AI applications, such as question answering, recommendation systems, and etc.
arXiv Detail & Related papers (2022-12-12T08:40:04Z) - A Survey on Knowledge Graph-based Methods for Automated Driving [0.0]
Knowledge graphs (KG) have gained significant attention from both industry and academia for applications that benefit by exploiting structured, dynamic, and relational data.
We discuss current research challenges and propose promising future research directions for KG-based solutions for automated driving.
arXiv Detail & Related papers (2022-09-30T15:47:19Z) - KSG: Knowledge and Skill Graph [28.2974853907085]
We propose a novel dynamic knowledge and skill graph (KSG) based on CN-DBpedia.
KSG can search for different agents' skills in various environments and provide transferable information for acquiring new skills.
This is the first study that we are aware of that looks into dynamic KSG for skill retrieval and learning.
arXiv Detail & Related papers (2022-09-13T02:47:46Z) - Knowledge Graph Augmented Network Towards Multiview Representation
Learning for Aspect-based Sentiment Analysis [96.53859361560505]
We propose a knowledge graph augmented network (KGAN) to incorporate external knowledge with explicitly syntactic and contextual information.
KGAN captures the sentiment feature representations from multiple perspectives, i.e., context-, syntax- and knowledge-based.
Experiments on three popular ABSA benchmarks demonstrate the effectiveness and robustness of our KGAN.
arXiv Detail & Related papers (2022-01-13T08:25:53Z) - KACC: A Multi-task Benchmark for Knowledge Abstraction, Concretization
and Completion [99.47414073164656]
A comprehensive knowledge graph (KG) contains an instance-level entity graph and an ontology-level concept graph.
The two-view KG provides a testbed for models to "simulate" human's abilities on knowledge abstraction, concretization, and completion.
We propose a unified KG benchmark by improving existing benchmarks in terms of dataset scale, task coverage, and difficulty.
arXiv Detail & Related papers (2020-04-28T16:21:57Z) - Knowledge Graphs and Knowledge Networks: The Story in Brief [0.1933681537640272]
Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities.
For dynamic real-world applications such as social networks, recommender systems, computational biology, relational knowledge representation has emerged as a challenging research problem.
This article attempts to summarize the journey of KG for AI.
arXiv Detail & Related papers (2020-03-07T18:09:18Z) - 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.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.