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OpenKG Chain:A Blockchain Infrastructure for Open knowledge Graphs

Data Intelligence 2021年第2期3卷 205-227页

作者： Huajun Chen Ning Hu Guilin Qi Haofen Wang Zhen Bi Jie Li Fan Yang College of Computer Science&AZFT Knowledge Engine Lab Zhejiang UniversityHangzhou 310058China Ontology Foundation Venture Drive#11-31Vision ExchangeSingapore 608526Singapore School of Computer Science and Engineering Southeast UniversityNanjing 211189China Intelligent Big Data Visualization Lab Tongji UniversityShanghai 200092China

The early concept of knowledge graph originates from the idea of the semantic Web,which aims at using structured graphs to model the knowledge of the world and record the relationships that exist between *** publishing knowledge bases as open data on the Web has gained significant *** China,Chinese Information Processing Society of China(CIPS)launched the OpenKG in 2015 to foster the development of Chinese Open knowledge *** existing open knowledge-based programs,OpenKG chain is envisioned as a blockchain-based open knowledge *** article introduces the first attempt at the implementation of sharing knowledge graphs on OpenKG chain,a blockchain-based trust *** have completed the test of the underlying blockchain platform,and the on-chain test of OpenKG’s data set and tool set sharing as well as fine-grained knowledge crowdsourcing at the triple *** have also proposed novel definitions:K-Point and OpenKG Token,which can be considered to be a measurement of knowledge value and user value.1,033 knowledge contributors have been involved in two months of testing on the blockchain,and the cumulative number of on-chain recordings triggered by real knowledge consumers has reached 550,000 with an average daily peak value of more than 10,*** the first time,we have tested and realized on-chain sharing of knowledge at entity/triple granularity *** present,all operations on the data sets and tool sets at ***,as well as the triplets at OpenBase,are recorded on the chain,and corresponding value will also be generated and assigned in a trusted *** this effort,OpenKG chain looks forward to providing a more credible and traceable knowledge-sharing platform for the knowledge graph community.

关键词： Open knowledge graph Blockchain Decentralized distributed network Decentralized knowledge graph

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MULTI-LEVEL PROTEIN STRUCTURE PRE-TRAINING WITH PROMPT LEARNING 11

MULTI-LEVEL PROTEIN STRUCTURE PRE-TRAINING WITH PROMPT LEARN...

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11th International Conference on Learning Representations, ICLR 2023

作者： Wang, Zeyuan Zhang, Qiang Yu, Haoran Hu, Shuangwei Jin, Xurui Gong, Zhichen Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China College of Chemical and Biological Engineering Zhejiang University China Vecx Biomedicines Inc. MindRank AI Ltd. University College London United Kingdom AZFT Joint Lab for Knowledge Engine China East China Sea Laboratory China

A protein can focus on different structure levels to implement its functions. Each structure has its own merit and driving forces in describing specific characteristics, and they cannot replace each other. Most existing function prediction methods take either the primary or the tertiary structure as input, unintentionally ignoring the other levels of protein structures. Considering protein sequences can determine multi-level structures, in this paper, we aim to realize the comprehensive potential of protein sequences for function prediction. Specifically, we propose a new prompt-guided multi-task pre-training and fine-tuning framework. Through the prompt-guided multi-task pre-training, we learn multiple prompt signals to steer the model, called PromptProtein, to focus on different levels of structures. We also design a prompt fine-tuning module to provide downstream tasks the on-demand flexibility of utilizing respective levels of structural information. Extensive experiments on function prediction and protein engineering show that PromptProtein outperforms state-of-the-art methods by large margins. To the best of our knowledge, this is the first prompt-based pre-trained protein model. © 2023 11th International Conference on Learning Representations, ICLR 2023. All rights reserved.

关键词： Proteins

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INSTRUCTPROTEIN: ALIGNING HUMAN AND PROTEIN LANGUAGE VIA knowledge INSTRUCTION

arXiv

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arXiv 2023年

作者： Wang, Zeyuan Zhang, Qiang Ding, Keyan Qin, Ming Zhuang, Xiang Li, Xiaotong Chen, Huajun College of Computer Science and Technology Zhejiang University China ZJU-Hangzhou Global Scientific and Technological Innovation Center China AZFT Joint Lab for Knowledge Engine China

Large Language Models (LLMs) have revolutionized the field of natural language processing, but they fall short in comprehending biological sequences such as proteins. To address this challenge, we propose InstructProtein, an innovative LLM that possesses bidirectional generation capabilities in both human and protein languages: (i) taking a protein sequence as input to predict its textual function description and (ii) using natural language to prompt protein sequence generation. To achieve this, we first pre-train an LLM on both protein and natural language corpora, enabling it to comprehend individual languages. Then supervised instruction tuning is employed to facilitate the alignment of these two distinct languages. Herein, we introduce a knowledge graph-based instruction generation framework to construct a high-quality instruction dataset, addressing annotation imbalance and instruction deficits in existing protein-text corpus. In particular, the instructions inherit the structural relations between proteins and function annotations in knowledge graphs, which empowers our model to engage in the causal modeling of protein functions, akin to the chain-of-thought processes in natural languages. Extensive experiments on bidirectional protein-text generation tasks show that InstructProtein outperforms state-of-the-art LLMs by large margins. Moreover, InstructProtein serves as a pioneering step towards text-based protein function prediction and sequence design, effectively bridging the gap between protein and human language understanding. Copyright © 2023, The Authors. All rights reserved.

关键词： Proteins

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Relational Message Passing for Fully Inductive knowledge Graph Completion 39

Relational Message Passing for Fully Inductive Knowledge Gra...

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39th IEEE International Conference on Data engineering, ICDE 2023

作者： Geng, Yuxia Chen, Jiaoyan Pan, Jeff Z. Chen, Mingyang Jiang, Song Zhang, Wen Chen, Huajun Zhejiang University College of Computer Science and Technology Hangzhou China Donghai Laboratory Zhoushan China The University of Manchester Department of Computer Science Manchester United Kingdom School of Informatics The University of Edinburgh Edinburgh United Kingdom Naie Pdu Huawei Technologies Co. Ltd. Xi'an China Zhejiang University Hangzhou China Zhejiang University Azft Joint Lab for Knowledge Engine Hangzhou China

ISBN: (纸本)9798350322279

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the KG's ontological schema. Extensive evaluation on multiple benchmarks has shown the effectiveness of RMPI's techniques and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes, data and some supplementary experiment results are available at https://***/zjukg/RMPI. © 2023 IEEE.

关键词： knowledge graph

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Prompt-Guided Injection of Conformation to Pre-trained Protein Model

arXiv

引用

arXiv 2022年

作者： Zhang, Qiang Wang, Zeyuan Han, Yuqiang Yu, Haoran Jin, Xurui Chen, Huajun College of Computer Science and Technology Zhejiang University China Hangzhou Innovation Center Zhejiang University China AZFT Knowledge Engine Lab China MindRank AI Ltd. China

Pre-trained protein models (PTPMs) represent a protein with one fixed embedding and thus are not capable for diverse tasks. For example, protein structures can shift, namely protein folding, between several conformations in various biological processes. To enable PTPMs to produce informative representations, we propose to learn interpretable, pluggable, and extensible protein prompts as a way of injecting task-related knowledge into PTPMs. In this regard, prior PTPM optimization with the masked language modeling task can be interpreted as learning a sequence prompt (Seq prompt) that enables PTPMs to capture the sequential dependency between amino acids. To incorporate conformational knowledge to PTPMs, we propose an interaction-conformation prompt (IC prompt) that is learned through back-propagation with the protein-protein interaction task. As an instantiation, we present a conformation-aware pre-trained protein model that learns both the sequence and interaction-conformation prompts in a multi-task setting. We conduct comprehensive experiments on nine protein datasets. Results show that using the Seq prompt does not hurt PTPMs’ performance on sequence-related tasks while incorporating the IC prompt significantly improves PTPMs’ performance on tasks where interaction conformational knowledge counts. Furthermore, the learned prompts can be combined and extended to deal with new protein tasks. Copyright © 2022, The Authors. All rights reserved.

关键词： Proteins

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knowledge-aware Zero-shot learning: Survey and perspective

arXiv

引用

arXiv 2021年

作者： Chen, Jiaoyan Geng, Yuxia Chen, Zhuo Horrocks, Ian Pan, Jeff Z. Chen, Huajun Department of Computer Science University of Oxford College of Computer Science AZFT Knowledge Engine Lab Zhejiang University School of Informatics The University of Edinburgh

Zero-shot learning (ZSL) which aims at predicting classes that have never appeared during the training using external knowledge (a.k.a. side information) has been widely investigated. In this paper we present a literature review towards ZSL in the perspective of external knowledge, where we categorize the external knowledge, review their methods and compare different external knowledge. With the literature review, we further discuss and outlook the role of symbolic knowledge in addressing ZSL and other machine learning sample shortage issues. Copyright © 2021, The Authors. All rights reserved.

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Relational Message Passing for Fully Inductive knowledge Graph Completion

Relational Message Passing for Fully Inductive Knowledge Gra...

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International Conference on Data engineering

作者： Yuxia Geng Jiaoyan Chen Jeff Z. Pan Mingyang Chen Song Jiang Wen Zhang Huajun Chen College of Computer Science and Technology Zhejiang University Hangzhou China Donghai Laboratory Zhoushan China Department of Computer Science The University of Manchester Manchester United Kingdom School of Informatics The University of Edinburgh Edinburgh United Kingdom NAIE PDU Huawei Technologies Co. Ltd. Xi’an China AZFT Joint Lab for Knowledge Engine Zhejiang University Hangzhou China

In knowledge graph completion (KGC), predicting triples involving emerging entities and/or relations, which are unseen when the KG embeddings are learned, has become a critical challenge. Subgraph reasoning with message passing is a promising and popular solution. Some recent methods have achieved good performance, but they (i) usually can only predict triples involving unseen entities alone, failing to address more realistic fully inductive situations with both unseen entities and unseen relations, and (ii) often conduct message passing over the entities with the relation patterns not fully utilized. In this study, we propose a new method named RMPI which uses a novel Relational Message Passing network for fully Inductive KGC. It passes messages directly between relations to make full use of the relation patterns for subgraph reasoning with new techniques on graph transformation, graph pruning, relation-aware neighborhood attention, addressing empty subgraphs, etc., and can utilize the relation semantics defined in the KG’s ontological schema. Extensive evaluation on multiple benchmarks has shown the effectiveness of RMPI’s techniques and its better performance compared with the existing methods that support fully inductive KGC. RMPI is also comparable to the state-of-the-art partially inductive KGC methods with very promising results achieved. Our codes, data and some supplementary experiment results are available at https://***/zjukg/RMPI.

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Neural symbolic reasoning with knowledge graphs:knowledge extraction,relational reasoning,and inconsistency checking

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Fundamental Research 2021年第5期1卷 565-573页

作者： Huajun Chen Shumin Deng Wen Zhang Zezhong Xu Juan Li Evgeny Kharlamov College of Computer Science Zhejiang UniversityHangzhou310027China Hangzhou Innovation Center Zhejiang UniversityHangzhou310000China AZFT Joint Lab for Knowledge Engine Hangzhou311121China Bosch Center for Artificial Intelligence Robert Bosch GmbHRenningen70465 StuttgartGermany SIRIUS Research Center University of OsloOsloN-0316Norway

knowledge graphs(KGs)express relationships between entity pairs,and many real-life problems can be formulated as knowledge graph reasoning(KGR).Conventional approaches to KGR have achieved promising performance but still have some *** the one hand,most KGR methods focus only on one phase of the KG lifecycle,such as KG completion or refinement,while ignoring reasoning over other stages,such as KG *** the other hand,traditional KGR methods,broadly categorized as symbolic and neural,are unable to balance both scalability and *** resolve these two problems,we take a more comprehensive perspective of KGR with regard to the whole KG lifecycle,including KG extraction,completion,and refinement,which correspond to three subtasks:knowledge extraction,relational reasoning,and inconsistency *** addition,we propose the implementation of KGR using a novel neural symbolic framework,with regard to both scalability and *** results demonstrate that our proposed methods outperform traditional neural symbolic models.

关键词： Neural symbolic reasoning knowledge graph knowledge extraction Relational reasoning Inconsistency checking

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Billion-scale Pre-trained e-commerce product knowledge graph model

arXiv

引用

arXiv 2021年

作者： Zhang, Wen Wong, Chi-Man Ye, Ganqiang Wen, Bo Zhang, Wei Chen, Huajun Zhejiang University Hangzhou China Alibaba Group University of Macao Alibaba Group Hangzhou China College of Computer Science Hangzhou Innovation Center Zhejiang University AZFT Joint Lab for Knowledge Engine

In recent years, knowledge graphs have been widely applied to organize data in a uniform way and enhance many tasks that require knowledge, for example, online shopping which has greatly facilitated people's life. As a backbone for online shopping platforms, we built a billion-scale e-commerce product knowledge graph for various item knowledge services such as item recommendation. However, such knowledge services usually include tedious data selection and model design for knowledge infusion, which might bring inappropriate results. Thus, to avoid this problem, we propose a Pre-trained knowledge Graph Model (PKGM) for our billion-scale e-commerce product knowledge graph, providing item knowledge services in a uniform way for embedding-based models without accessing triple data in the knowledge graph. Notably, PKGM could also complete knowledge graphs during servicing, thereby overcoming the common incompleteness issue in knowledge graphs. We test PKGM in three knowledge-related tasks including item classification, same item identification, and recommendation. Experimental results show PKGM successfully improves the performance of each task. Copyright © 2021, The Authors. All rights reserved.

关键词： Electronic commerce

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Zero-shot visual question answering using knowledge graph

arXiv

引用

arXiv 2021年

作者： Chen, Zhuo Chen, Jiaoyan Geng, Yuxia Pan, Jeff Z. Yuan, Zonggang Chen, Huajun College of Computer Science Hangzhou Innovation Center Zhejiang University AZFT Joint Lab for Knowledge Engine Department of Computer Science University of Oxford School of Informatics The University of Edinbur NAIE CTO Office Huawei Technologies Co. Ltd

Incorporating external knowledge to Visual Question Answering (VQA) has become a vital practical need. Existing methods mostly adopt pipeline approaches with different components for knowledge matching and extraction, feature learning, etc. However, such pipeline approaches suffer when some component does not perform well, which leads to error cascading and poor overall performance. Furthermore, the majority of existing approaches ignore the answer bias issue — many answers may have never appeared during training (i.e., unseen answers) in real-word application. To bridge these gaps, in this paper, we propose a Zero-shot VQA algorithm using knowledge graph and a mask-based learning mechanism for better incorporating external knowledge, and present new answer-based Zero-shot VQA splits for the F-VQA dataset. Experiments show that our method can achieve state-of-the-art performance in Zero-shot VQA with unseen answers, meanwhile dramatically augment existing end-to-end models on the normal F-VQA task. Copyright © 2021, The Authors. All rights reserved.

关键词： Pipelines

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