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Attention-Based Capsule Networks with Dynamic Routing for Relation Extraction
[article]
2018
arXiv
pre-print
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A capsule is a group of neurons, whose activity vector represents the instantiation parameters of a specific type of entity. In this paper, we explore the capsule networks used for relation extraction in a multi-instance multi-label learning framework and propose a novel neural approach based on capsule networks with attention mechanisms. We evaluate our method with different benchmarks, and it is demonstrated that our method improves the precision of the predicted relations. Particularly, we
arXiv:1812.11321v1
fatcat:q3okntkh5bbzlno346wffilxfq
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... ow that capsule networks improve multiple entity pairs relation extraction.
Transfer Learning for Relation Extraction via Relation-Gated Adversarial Learning
[article]
2019
arXiv
pre-print
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., 2017; Zhang et al., 2018b; Zeng et al., 2018; Qin et al., 2018; Zhang et al., 2019) . ...
(Zhang et al., 2018a) proposed an adversarial nets-based partial domain adaptation method to identify the source samples in instance level. ...
arXiv:1908.08507v1
fatcat:3dicu6q6kvfxrex3pfr5rezvkm
ProteinKG65: A Knowledge Graph for Protein Science
[article]
2022
arXiv
pre-print
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Existing data-centric methods for protein science generally cannot sufficiently capture and leverage biology knowledge, which may be crucial for many protein tasks. To facilitate research in this field, we create ProteinKG65, a knowledge graph for protein science. Using gene ontology and Uniprot knowledge base as a basis, we transform and integrate various kinds of knowledge with aligned descriptions and protein sequences respectively to GO terms and protein entities. ProteinKG65 is mainly
arXiv:2207.10080v1
fatcat:ajav76nlkng6hjnbg5r6wvnpf4
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... ated to providing a specialized protein knowledge graph, bringing the knowledge of Gene Ontology to protein function and structure prediction. The current version contains about 614,099 entities, 5,620,437 triples (including 5,510,437 protein-go triplets and 110,000 GO-GO triplets). We also illustrate the potential applications of ProteinKG65 with a prototype. We hope our released knowledge graph can help promote studies in AI for science.
Logic-guided Semantic Representation Learning for Zero-Shot Relation Classification
[article]
2020
arXiv
pre-print
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., 2015; Zhang et al., 2018; Zhang et al., 2019a; Yu et al., 2020; Wang et al., 2020) , and some joint models like (Zheng et al., 2017; Ye et al., 2020) . ...
Inspired by (Zhang et al., 2019b) , we apply an simple but effective embeddingbased method to incorporate symbolic rules into semantic space and generate E rl . ...
arXiv:2010.16068v1
fatcat:32mls4yobvdr5lzyy3wd4o2ojq
Retrosplenial cortex and its role in spatial cognition
[article]
2017
bioRxiv
pre-print
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Retrosplenial cortex (RSC) is a region within the posterior neocortical system, heavily interconnected with an array of brain networks, both cortical and subcortical, that is engaged by a myriad of cognitive tasks. Although there is no consensus as to its precise function, evidence from both human and animal studies clearly points to a role in spatial cognition. However, the spatial processing impairments that follow RSC damage are not straightforward to characterise, leading to difficulties in
doi:10.1101/190801
fatcat:zhdtzdiljrbvxl5nklq4kr5mgm
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... defining the exact nature of its role. In the present article we review this literature and classify the types of ideas that have been put forward into three broad, somewhat overlapping classes: (i) Learning of landmark location, stability and permanence; (ii) Integration between spatial reference frames, and (iii) Consolidation and retrieval of spatial knowledge ('schemas'). We evaluate these models and suggest ways to test them, before briefly discussing whether the spatial function may be a subset of a more general function in episodic memory.
PRGC: Potential Relation and Global Correspondence Based Joint Relational Triple Extraction
[article]
2021
arXiv
pre-print
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Joint extraction of entities and relations from unstructured texts is a crucial task in information extraction. Recent methods achieve considerable performance but still suffer from some inherent limitations, such as redundancy of relation prediction, poor generalization of span-based extraction and inefficiency. In this paper, we decompose this task into three subtasks, Relation Judgement, Entity Extraction and Subject-object Alignment from a novel perspective and then propose a joint
arXiv:2106.09895v1
fatcat:5uqfov5zdzhqbbmea4ja5yiupq
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... l triple extraction framework based on Potential Relation and Global Correspondence (PRGC). Specifically, we design a component to predict potential relations, which constrains the following entity extraction to the predicted relation subset rather than all relations; then a relation-specific sequence tagging component is applied to handle the overlapping problem between subjects and objects; finally, a global correspondence component is designed to align the subject and object into a triple with low-complexity. Extensive experiments show that PRGC achieves state-of-the-art performance on public benchmarks with higher efficiency and delivers consistent performance gain on complex scenarios of overlapping triples.
Kformer: Knowledge Injection in Transformer Feed-Forward Layers
[article]
2022
arXiv
pre-print
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Knowledge-Enhanced Model have developed a diverse set of techniques for knowledge integration on different knowledge sources. However, most previous work neglect the language model's own ability and simply concatenate external knowledge at the input. Recent work proposed that Feed Forward Network (FFN) in pre-trained language model can be seen as an memory that stored factual knowledge. In this work, we explore the FFN in Transformer and propose a novel knowledge fusion model, namely Kformer,
arXiv:2201.05742v1
fatcat:jsofnqtlfngxpdqx4jrzelm5eq
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... ich incorporates external knowledge through the feed-forward layer in Transformer. We empirically find that simply injecting knowledge into FFN can enhance the pre-trained language model's ability and facilitate current knowledge fusion methods. Our results on two benchmarks in the commonsense reasoning (i.e., SocialIQA) and medical question answering (i.e., MedQA-USMLE) domains demonstrate that Kformer can utilize external knowledge deeply and achieves absolute improvements in these tasks.
Conceptualized Representation Learning for Chinese Biomedical Text Mining
[article]
2020
arXiv
pre-print
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Biomedical text mining is becoming increasingly important as the number of biomedical documents and web data rapidly grows. Recently, word representation models such as BERT has gained popularity among researchers. However, it is difficult to estimate their performance on datasets containing biomedical texts as the word distributions of general and biomedical corpora are quite different. Moreover, the medical domain has long-tail concepts and terminologies that are difficult to be learned via
arXiv:2008.10813v1
fatcat:dqt4id37gjdalcqpxkvemkb7tm
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... nguage models. For the Chinese biomedical text, it is more difficult due to its complex structure and the variety of phrase combinations. In this paper, we investigate how the recently introduced pre-trained language model BERT can be adapted for Chinese biomedical corpora and propose a novel conceptualized representation learning approach. We also release a new Chinese Biomedical Language Understanding Evaluation benchmark (ChineseBLUE). We examine the effectiveness of Chinese pre-trained models: BERT, BERT-wwm, RoBERTa, and our approach. Experimental results on the benchmark show that our approach could bring significant gain. We release the pre-trained model on GitHub: https://github.com/alibaba-research/ChineseBLUE.
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
[article]
2019
arXiv
pre-print
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., 2016; Zhang et al., 2018a) . ...
Relation structure (relational knowledge) has been studied and is quite effective for KG completion (Zhang et al., 2018b) . ...
arXiv:1903.01306v1
fatcat:jz3yzkhkhfcungwzkws4sytjke
Environment symmetry drives a multidirectional code in rat retrosplenial cortex
[article]
2021
bioRxiv
pre-print
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Author contributions: Ningyu Zhang: Conceptualization, Methodology, Software, Investigation, Formal analysis, Data curation, Visualization, Writing -Original Draft, Review & Editing; Roddy Grieves: Conceptualization ...
doi:10.1101/2021.08.22.457261
fatcat:mwg4u5j2engo5a44r3zjspev4y
LaKo: Knowledge-driven Visual Question Answering via Late Knowledge-to-Text Injection
[article]
2022
arXiv
pre-print
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Visual question answering (VQA) often requires an understanding of visual concepts and language semantics, which relies on external knowledge. Most existing methods exploit pre-trained language models or/and unstructured text, but the knowledge in these resources are often incomplete and noisy. Some methods prefer to use knowledge graphs (KGs) which often have intensive structured knowledge, but the research is still quite preliminary. In this paper, we propose LaKo, a knowledge-driven VQA
arXiv:2207.12888v1
fatcat:ru2vqlszxvb65laeokebt76itu
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... d via Late Knowledge-to-text Injection. To effectively incorporate an external KG, we transfer triples into text and propose a late injection mechanism. Finally we address VQA as a text generation task with an effective encoder-decoder paradigm. In the evaluation with OKVQA datasets, our method achieves state-of-the-art results.
Bridging Text and Knowledge with Multi-Prototype Embedding for Few-Shot Relational Triple Extraction
[article]
2020
arXiv
pre-print
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Current supervised relational triple extraction approaches require huge amounts of labeled data and thus suffer from poor performance in few-shot settings. However, people can grasp new knowledge by learning a few instances. To this end, we take the first step to study the few-shot relational triple extraction, which has not been well understood. Unlike previous single-task few-shot problems, relational triple extraction is more challenging as the entities and relations have implicit
arXiv:2010.16059v1
fatcat:ioxbuwd2qzgoxgux7fxmyfrpjy
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... s. In this paper, We propose a novel multi-prototype embedding network model to jointly extract the composition of relational triples, namely, entity pairs and corresponding relations. To be specific, we design a hybrid prototypical learning mechanism that bridges text and knowledge concerning both entities and relations. Thus, implicit correlations between entities and relations are injected. Additionally, we propose a prototype-aware regularization to learn more representative prototypes. Experimental results demonstrate that the proposed method can improve the performance of the few-shot triple extraction.
Context-aware Deep Model for Entity Recommendation in Search Engine at Alibaba
[article]
2019
arXiv
pre-print
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ACKNOWLEDGMENTS We would like to thank colleagues of our team -Xiangzhi Wang, Yulin Wang, Liang Dong, Kangping Yin, Zhenxin Ma, Yongjin Wang, Qiteng Yang, Wei Shen, Liansheng Sun, Kui Xiong, Weixing Zhang ...
arXiv:1909.04493v1
fatcat:eg7pou6l55ajpddguwp4n4lngi
Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective
[article]
2022
arXiv
pre-print
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Preliminary on Low-resource KE 2.1 Knowledge Extraction Knowledge Extraction (KE) [Zhang et al., 2020a] aims to extract structural information from unstructured texts, such as Named Entity Recognition ...
Meta Learning Qu et al., 2020] ; Shen et al., 2021] Transfer Learning [Huang et al., 2018; ; [Zhang et al., 2020b; Xue et al., 2020] ; [Soares et al., 2019; Wang et al., 2021b] Prompt Learning ...
arXiv:2202.08063v1
fatcat:2q64tx2mzne53gt24adi6ymj7a
Reasoning Through Memorization: Nearest Neighbor Knowledge Graph Embeddings
[article]
2022
arXiv
pre-print
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., 2015; Zhang et al., 2019a; Zhang et al., 2020; Zhang et al., 2021a; Qi et al., 2021; Zhang et al., 2021b] . ...
objective of translation-based methods is that the translated head entity should be close to the tail entity in real space [Bordes et al., 2013], complex space [Sun et al., 2019], or quaternion space [Zhang ...
arXiv:2201.05575v1
fatcat:umhtd3wp75h2doegoe7q3yd3ve
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