TeKo: Text-Rich Graph Neural Networks with External Knowledge
- URL: http://arxiv.org/abs/2206.07253v1
- Date: Wed, 15 Jun 2022 02:33:10 GMT
- Title: TeKo: Text-Rich Graph Neural Networks with External Knowledge
- Authors: Zhizhi Yu, Di Jin, Jianguo Wei, Ziyang Liu, Yue Shang, Yun Xiao,
Jiawei Han, and Lingfei Wu
- Abstract summary: We propose a novel text-rich graph neural network with external knowledge (TeKo)
We first present a flexible heterogeneous semantic network that incorporates high-quality entities.
We then introduce two types of external knowledge, that is, structured triplets and unstructured entity description.
- Score: 75.91477450060808
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Graph Neural Networks (GNNs) have gained great popularity in tackling various
analytical tasks on graph-structured data (i.e., networks). Typical GNNs and
their variants follow a message-passing manner that obtains network
representations by the feature propagation process along network topology,
which however ignore the rich textual semantics (e.g., local word-sequence)
that exist in many real-world networks. Existing methods for text-rich networks
integrate textual semantics by mainly utilizing internal information such as
topics or phrases/words, which often suffer from an inability to
comprehensively mine the text semantics, limiting the reciprocal guidance
between network structure and text semantics. To address these problems, we
propose a novel text-rich graph neural network with external knowledge (TeKo),
in order to take full advantage of both structural and textual information
within text-rich networks. Specifically, we first present a flexible
heterogeneous semantic network that incorporates high-quality entities and
interactions among documents and entities. We then introduce two types of
external knowledge, that is, structured triplets and unstructured entity
description, to gain a deeper insight into textual semantics. We further design
a reciprocal convolutional mechanism for the constructed heterogeneous semantic
network, enabling network structure and textual semantics to collaboratively
enhance each other and learn high-level network representations. Extensive
experimental results on four public text-rich networks as well as a large-scale
e-commerce searching dataset illustrate the superior performance of TeKo over
state-of-the-art baselines.
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