# KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction
###### Abstract  
Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and CDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction. relation extraction; knowledge graph embedding; graph convolutional network; attention mechanism +
Footnote †: 2021: 11, 7734. [[https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734)]([https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734))  
Footnote †: 2021: 11, 7734. [[https://doi.org/10.3390/app1167734](https://doi.org/10.3390/app1167734)]([https://doi.org/10.3390/app1167734](https://doi.org/10.3390/app1167734))  
Footnote †: 2021: 11, 7734. [[https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734)]([https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734))  
Footnote †: 2021: 111, 7734. [[https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734)]([https://doi.org/10.3390/app11167734](https://doi.org/10.3390/app11167734))
the KG, all sentences that mention both entities \(e_{1}\) and \(e_{2}\) are regarded as the training instances of relation \(r\). Despite distant supervision paradigm can automatically collect training data for relation extractor, it often suffers from the wrong labeling problem [11]. A pair of entities that appear in a sentence may not express the relation which links them in the KG, they are just related to a same topic. As a result, the distant supervision will inevitably bring noise into the generated training dataset, which drops the performance of relation extraction. Most existing methods alleviate the negative impact of noise by utilizing multi-instance leaning. The multi-instance based relation extraction can be regarded as a bag-level classification task, which allows different sentences to have at most one shared label. Existing methods for distantly supervised relation extraction have made some progress. Nevertheless, they are still confronted with two challenges. (1) _How to design more effective sentence encoders to generate more expressive sentence-level features_. Previous works for relation extraction considered that all words in a sentence have equal contributions for predicting the relation between two marked entities. In fact, only a few words in a sentence are relevant for determining the relation expressed. Take the aforementioned sentence as an example, _\"[PERSON] co-founded Microsoft with his childhood friend [PERSON]\"_. Obviously, the word _\"co-founded\"_ is of great importance in predicting the relation _founder_ between _\"[PERSON]\"_ and _\"Microsoft\"_. However, the words _\"childhood friend\"_ have little relevance with that relation.