DKG: A Descriptive Knowledge Graph for Explaining Relationships between Entities

• URL: http://arxiv.org/abs/2205.10479v1
• Date: Sat, 21 May 2022 01:16:04 GMT
• Title: DKG: A Descriptive Knowledge Graph for Explaining Relationships between Entities
• Authors: Jie Huang, Kerui Zhu, Kevin Chen-Chuan Chang, Jinjun Xiong, Wen-mei Hwu
• Abstract summary: We propose Descriptive Knowledge Graph (DKG) - an open and interpretable form of modeling relationships between entities. To construct DKGs, we propose a self-supervised learning method to extract relation descriptions. Experiments demonstrate that our system can extract and generate high-quality relation descriptions.
• Score: 34.14526494269527
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose Descriptive Knowledge Graph (DKG) - an open and interpretable form of modeling relationships between entities. In DKGs, relationships between entities are represented by relation descriptions. For instance, the relationship between entities of machine learning and algorithm can be described as "Machine learning explores the study and construction of algorithms that can learn from and make predictions on data." To construct DKGs, we propose a self-supervised learning method to extract relation descriptions with the analysis of dependency patterns and a transformer-based relation description synthesizing model to generate relation descriptions. Experiments demonstrate that our system can extract and generate high-quality relation descriptions for explaining entity relationships.

Related papers

• Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems [58.561904356651276]
  We introduce the Knowledge-Enhanced Entity Representation Learning (KERL) framework to improve the semantic understanding of entities for Conversational recommender systems. KERL uses a knowledge graph and a pre-trained language model to improve the semantic understanding of entities. KERL achieves state-of-the-art results in both recommendation and response generation tasks.
  arXiv  Detail & Related papers  (2023-12-18T06:41:23Z)
• Research on Joint Representation Learning Methods for Entity Neighborhood Information and Description Information [2.206623168926072]
  A joint learning model that combines entity neighborhood infor-mation and description information is proposed. Experimental results demonstrate that the proposed model achieves favorable performance on the knowledge graph dataset of the pro-gramming design course.
  arXiv  Detail & Related papers  (2023-09-15T01:38:07Z)
• ViRel: Unsupervised Visual Relations Discovery with Graph-level Analogy [65.5580334698777]
  ViRel is a method for unsupervised discovery and learning of Visual Relations with graph-level analogy. We show that our method achieves above 95% accuracy in relation classification. We further generalizes to unseen tasks with more complicated relational structures.
  arXiv  Detail & Related papers  (2022-07-04T16:56:45Z)
• Jointly Learning Knowledge Embedding and Neighborhood Consensus with Relational Knowledge Distillation for Entity Alignment [9.701081498310165]
  Entity alignment aims at integrating heterogeneous knowledge from different knowledge graphs. Recent studies employ embedding-based methods by first learning representation of Knowledge Graphs and then performing entity alignment. We propose a Graph Convolutional Network (GCN) model equipped with knowledge distillation for entity alignment.
  arXiv  Detail & Related papers  (2022-01-25T02:47:14Z)
• Open Relation Modeling: Learning to Define Relations between Entities [24.04238065663009]
  We propose to teach machines to generate definition-like relation descriptions by letting them learn from definitions of entities. Specifically, we fine-tune Pre-trained Language Models (PLMs) to produce definitions conditioned on extracted entity pairs. We show that PLMs can select interpretable and informative reasoning paths by confidence estimation, and the selected path can guide PLMs to generate better relation descriptions.
  arXiv  Detail & Related papers  (2021-08-20T16:03:23Z)
• Unified Graph Structured Models for Video Understanding [93.72081456202672]
  We propose a message passing graph neural network that explicitly models relational-temporal relations. We show how our method is able to more effectively model relationships between relevant entities in the scene.
  arXiv  Detail & Related papers  (2021-03-29T14:37:35Z)
• Knowledge Graph Embedding using Graph Convolutional Networks with Relation-Aware Attention [3.803929794912623]
  Knowledge graph embedding methods learn embeddings of entities and relations in a low dimensional space. Various graph convolutional network methods have been proposed which use different types of information to learn the features of entities and relations. We propose a relation-aware graph attention model that leverages relation information to compute different weights to the neighboring nodes for learning embeddings of entities and relations.
  arXiv  Detail & Related papers  (2021-02-14T17:19:44Z)
• Context-Enhanced Entity and Relation Embedding for Knowledge Graph Completion [2.580765958706854]
  We propose a model named AggrE, which conducts efficient aggregations on entity context and relation context in multi-hops. Experiment results show that AggrE is competitive to existing models.
  arXiv  Detail & Related papers  (2020-12-13T09:20:42Z)
• Learning to Decouple Relations: Few-Shot Relation Classification with Entity-Guided Attention and Confusion-Aware Training [49.9995628166064]
  We propose CTEG, a model equipped with two mechanisms to learn to decouple easily-confused relations. On the one hand, an EGA mechanism is introduced to guide the attention to filter out information causing confusion. On the other hand, a Confusion-Aware Training (CAT) method is proposed to explicitly learn to distinguish relations.
  arXiv  Detail & Related papers  (2020-10-21T11:07:53Z)
• HittER: Hierarchical Transformers for Knowledge Graph Embeddings [85.93509934018499]
  We propose Hitt to learn representations of entities and relations in a complex knowledge graph. Experimental results show that Hitt achieves new state-of-the-art results on multiple link prediction. We additionally propose a simple approach to integrate Hitt into BERT and demonstrate its effectiveness on two Freebase factoid answering datasets.
  arXiv  Detail & Related papers  (2020-08-28T18:58:15Z)

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.