Meng Qu

Publications47
h-index16
Citations3,522
Highly Influential Citations870
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LINE: Large-scale Information Network Embedding
TLDR
This paper studies the problem of embedding very large information networks into low-dimensional vector spaces, which is useful in many tasks such as visualization, node classification, and link prediction. Expand
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CoType: Joint Extraction of Typed Entities and Relations with Knowledge Bases
TLDR
We propose a novel domain-independent framework, called CoType, that runs a data-driven text segmentation algorithm to extract entity mentions, relation mentions, text features and type labels into two low-dimensional spaces, where, in each space, objects whose types are close will also have similar representations. Expand
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AFET: Automatic Fine-Grained Entity Typing by Hierarchical Partial-Label Embedding
TLDR
This paper proposes a novel embedding method to separately model “clean” and “noisy” mentions, and incorporates the given type hierarchy to induce loss functions. Expand
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An Attention-based Collaboration Framework for Multi-View Network Representation Learning
TLDR
This paper studies learning node representations for networks with multiple views, which aims to infer robust node representations across different views. Expand
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Label Noise Reduction in Entity Typing by Heterogeneous Partial-Label Embedding
TLDR
We propose a general framework, called PLE, to jointly embed entity mentions, text features and entity types into the same low-dimensional space where, in that space, objects whose types are semantically close have similar representations. Expand
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GMNN: Graph Markov Neural Networks
TLDR
We propose the Graph Markov Neural Network (GMNN) that combines the advantages of both worlds. Expand
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Meta-Path Guided Embedding for Similarity Search in Large-Scale Heterogeneous Information Networks
TLDR
In this paper, we re-examine similarity search in HINs and propose a novel embedding-based framework to explore network structure-embedded similarity. Expand
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Automatic Synonym Discovery with Knowledge Bases
TLDR
We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. Expand
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Unified Point-of-Interest Recommendation with Temporal Interval Assessment
TLDR
In this paper, we propose a unified recommender system, named the 'Where and When to gO' (WWO) recommender, to integrate the user interests and their evolving sequential preferences with temporal interval assessment. Expand
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Probabilistic Logic Neural Networks for Reasoning
TLDR
Knowledge graph reasoning, which aims at predicting the missing facts through reasoning with the observed facts, is critical to many applications. Expand
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