203 citations · 1k across the 101 of their papers we have counts for
13 papers · 1 filter
-CQA: Towards Knowledge Graph Complex Query Answering beyond Set Operation
Hang Yin, Zihao Wang, Weizhi Fei +1
To answer complex queries on knowledge graphs, logical reasoning over incomplete knowledge is required due to the open-world assumption. Learning-based methods are essential becaus…
Knowledge Graph Reasoning over Entities and Numerical Values
Jiaxin Bai, Chen Luo, Zheng Li +3
A complex logic query in a knowledge graph refers to a query expressed in logic form that conveys a complex meaning, such as where did the Canadian Turing award winner graduate fro…
Wasserstein-Fisher-Rao Embedding: Logical Query Embeddings with Local Comparison and Global Transport
Zihao Wang, Weizhi Fei, Hang Yin +3
Answering complex queries on knowledge graphs is important but particularly challenging because of the data incompleteness. Query embedding methods address this issue by learning-b…
Rethinking Complex Queries on Knowledge Graphs with Neural Link Predictors
Hang Yin, Zihao Wang, Yangqiu Song
Reasoning on knowledge graphs is a challenging task because it utilizes observed information to predict the missing one. Particularly, answering complex queries based on first-orde…
Fair Representation Learning for Heterogeneous Information Networks
Ziqian Zeng, Rashidul Islam, Kamrun Naher Keya +3
Recently, much attention has been paid to the societal impact of AI, especially concerns regarding its fairness. A growing body of research has identified unfair AI systems and pro…
ASER: Towards Large-scale Commonsense Knowledge Acquisition via Higher-order Selectional Preference over Eventualities
Hongming Zhang, Xin Liu, Haojie Pan +4
Commonsense knowledge acquisition and reasoning have long been a core artificial intelligence problem. However, in the past, there has been a lack of scalable methods to collect co…
Learning Contextual Causality from Time-consecutive Images
Hongming Zhang, Yintong Huo, Xinran Zhao +2
Causality knowledge is crucial for many artificial intelligence systems. Conventional textual-based causality knowledge acquisition methods typically require laborious and expensiv…
Analogous Process Structure Induction for Sub-event Sequence Prediction
Hongming Zhang, Muhao Chen, Haoyu Wang +2
Computational and cognitive studies of event understanding suggest that identifying, comprehending, and predicting events depend on having structured representations of a sequence…
WinoWhy: A Deep Diagnosis of Essential Commonsense Knowledge for Answering Winograd Schema Challenge
Hongming Zhang, Xinran Zhao, Yangqiu Song
In this paper, we present the first comprehensive categorization of essential commonsense knowledge for answering the Winograd Schema Challenge (WSC). For each of the questions, we…
TransOMCS: From Linguistic Graphs to Commonsense Knowledge
Hongming Zhang, Daniel Khashabi, Yangqiu Song +1
Commonsense knowledge acquisition is a key problem for artificial intelligence. Conventional methods of acquiring commonsense knowledge generally require laborious and costly human…
ASER: A Large-scale Eventuality Knowledge Graph
Hongming Zhang, Xin Liu, Haojie Pan +2
Understanding human's language requires complex world knowledge. However, existing large-scale knowledge graphs mainly focus on knowledge about entities while ignoring knowledge ab…
Machine Learning with World Knowledge: The Position and Survey
Yangqiu Song, Dan Roth
Machine learning has become pervasive in multiple domains, impacting a wide variety of applications, such as knowledge discovery and data mining, natural language processing, infor…
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
Tong Che, Yanran Li, Ruixiang Zhang +4
Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather…