Results 1 to 10 of about 1,540,853 (195)

Construction and Application of Teaching System Based on Crowdsourcing Knowledge Graph [PDF]

4th China Conference on Knowledge Graph and Semantic Computing, CCKS 2019, 2020
Through the combination of crowdsourcing knowledge graph and teaching system, research methods to generate knowledge graph and its applications. Using two crowdsourcing approaches, crowdsourcing task distribution and reverse captcha generation, to construct knowledge graph in the field of teaching system.
arxiv   +1 more source

Knowledge Graphs: Opportunities and Challenges [PDF]

arXiv, 2023
With the explosive growth of artificial intelligence (AI) and big data, it has become vitally important to organize and represent the enormous volume of knowledge appropriately. As graph data, knowledge graphs accumulate and convey knowledge of the real world.
arxiv  

Joint Embedding Learning of Educational Knowledge Graphs [PDF]

Artificial Intelligence Supported Educational Technologies (2020): 209-224, 2019
As an efficient model for knowledge organization, the knowledge graph has been widely adopted in several fields, e.g., biomedicine, sociology, and education. And there is a steady trend of learning embedding representations of knowledge graphs to facilitate knowledge graph construction and downstream tasks.
arxiv   +1 more source

Fast Knowledge Graph Completion using Graphics Processing Units [PDF]

arXiv, 2023
Knowledge graphs can be used in many areas related to data semantics such as question-answering systems, knowledge based systems. However, the currently constructed knowledge graphs need to be complemented for better knowledge in terms of relations. It is called knowledge graph completion.
arxiv  

Towards Loosely-Coupling Knowledge Graph Embeddings and Ontology-based Reasoning [PDF]

arXiv, 2022
Knowledge graph completion (a.k.a.~link prediction), i.e.,~the task of inferring missing information from knowledge graphs, is a widely used task in many applications, such as product recommendation and question answering. The state-of-the-art approaches of knowledge graph embeddings and/or rule mining and reasoning are data-driven and, thus, solely ...
arxiv  

Billion-scale Pre-trained E-commerce Product Knowledge Graph Model [PDF]

arXiv, 2021
In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people's life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services
arxiv  

Farspredict: A benchmark dataset for link prediction [PDF]

arXiv, 2023
Link prediction with knowledge graph embedding (KGE) is a popular method for knowledge graph completion. Furthermore, training KGEs on non-English knowledge graph promote knowledge extraction and knowledge graph reasoning in the context of these languages. However, many challenges in non-English KGEs pose to learning a low-dimensional representation of
arxiv  

A Comprehensive Survey on Automatic Knowledge Graph Construction [PDF]

arXiv, 2023
Automatic knowledge graph construction aims to manufacture structured human knowledge. To this end, much effort has historically been spent extracting informative fact patterns from different data sources. However, more recently, research interest has shifted to acquiring conceptualized structured knowledge beyond informative data.
arxiv  

Finding Motifs in Knowledge Graphs using Compression [PDF]

arXiv, 2021
We introduce a method to find network motifs in knowledge graphs. Network motifs are useful patterns or meaningful subunits of the graph that recur frequently. We extend the common definition of a network motif to coincide with a basic graph pattern.
arxiv  

Graph-based Knowledge Distillation: A survey and experimental evaluation [PDF]

arXiv, 2023
Graph, such as citation networks, social networks, and transportation networks, are prevalent in the real world. Graph Neural Networks (GNNs) have gained widespread attention for their robust expressiveness and exceptional performance in various graph applications.
arxiv