431 research outputs found
Knowledge Graph Alignment Network with Gated Multi-hop Neighborhood Aggregation
Graph neural networks (GNNs) have emerged as a powerful paradigm for
embedding-based entity alignment due to their capability of identifying
isomorphic subgraphs. However, in real knowledge graphs (KGs), the counterpart
entities usually have non-isomorphic neighborhood structures, which easily
causes GNNs to yield different representations for them. To tackle this
problem, we propose a new KG alignment network, namely AliNet, aiming at
mitigating the non-isomorphism of neighborhood structures in an end-to-end
manner. As the direct neighbors of counterpart entities are usually dissimilar
due to the schema heterogeneity, AliNet introduces distant neighbors to expand
the overlap between their neighborhood structures. It employs an attention
mechanism to highlight helpful distant neighbors and reduce noises. Then, it
controls the aggregation of both direct and distant neighborhood information
using a gating mechanism. We further propose a relation loss to refine entity
representations. We perform thorough experiments with detailed ablation studies
and analyses on five entity alignment datasets, demonstrating the effectiveness
of AliNet.Comment: Accepted by the 34th AAAI Conference on Artificial Intelligence (AAAI
2020
Neighborhood Matching Network for Entity Alignment
Structural heterogeneity between knowledge graphs is an outstanding challenge
for entity alignment. This paper presents Neighborhood Matching Network (NMN),
a novel entity alignment framework for tackling the structural heterogeneity
challenge. NMN estimates the similarities between entities to capture both the
topological structure and the neighborhood difference. It provides two
innovative components for better learning representations for entity alignment.
It first uses a novel graph sampling method to distill a discriminative
neighborhood for each entity. It then adopts a cross-graph neighborhood
matching module to jointly encode the neighborhood difference for a given
entity pair. Such strategies allow NMN to effectively construct
matching-oriented entity representations while ignoring noisy neighbors that
have a negative impact on the alignment task. Extensive experiments performed
on three entity alignment datasets show that NMN can well estimate the
neighborhood similarity in more tough cases and significantly outperforms 12
previous state-of-the-art methods.Comment: 11 pages, accepted by ACL 202
MHNF: Multi-hop Heterogeneous Neighborhood information Fusion graph representation learning
Attention mechanism enables the Graph Neural Networks(GNNs) to learn the
attention weights between the target node and its one-hop neighbors, the
performance is further improved. However, the most existing GNNs are oriented
to homogeneous graphs and each layer can only aggregate the information of
one-hop neighbors. Stacking multi-layer networks will introduce a lot of noise
and easily lead to over smoothing. We propose a Multi-hop Heterogeneous
Neighborhood information Fusion graph representation learning method (MHNF).
Specifically, we first propose a hybrid metapath autonomous extraction model to
efficiently extract multi-hop hybrid neighbors. Then, we propose a hop-level
heterogeneous Information aggregation model, which selectively aggregates
different-hop neighborhood information within the same hybrid metapath.
Finally, a hierarchical semantic attention fusion model (HSAF) is proposed,
which can efficiently integrate different-hop and different-path neighborhood
information respectively. This paper can solve the problem of aggregating the
multi-hop neighborhood information and can learn hybrid metapaths for target
task, reducing the limitation of manually specifying metapaths. In addition,
HSAF can extract the internal node information of the metapaths and better
integrate the semantic information of different levels. Experimental results on
real datasets show that MHNF is superior to state-of-the-art methods in node
classification and clustering tasks (10.94% - 69.09% and 11.58% - 394.93%
relative improvement on average, respectively)
Graph Neural Networks for Natural Language Processing: A Survey
Deep learning has become the dominant approach in coping with various tasks
in Natural LanguageProcessing (NLP). Although text inputs are typically
represented as a sequence of tokens, there isa rich variety of NLP problems
that can be best expressed with a graph structure. As a result, thereis a surge
of interests in developing new deep learning techniques on graphs for a large
numberof NLP tasks. In this survey, we present a comprehensive overview onGraph
Neural Networks(GNNs) for Natural Language Processing. We propose a new
taxonomy of GNNs for NLP, whichsystematically organizes existing research of
GNNs for NLP along three axes: graph construction,graph representation
learning, and graph based encoder-decoder models. We further introducea large
number of NLP applications that are exploiting the power of GNNs and summarize
thecorresponding benchmark datasets, evaluation metrics, and open-source codes.
Finally, we discussvarious outstanding challenges for making the full use of
GNNs for NLP as well as future researchdirections. To the best of our
knowledge, this is the first comprehensive overview of Graph NeuralNetworks for
Natural Language Processing.Comment: 127 page
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