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graph Attention Networks With Local Structure Awareness for knowledge graph Completion

IEEE ACCESS 2020年 8卷 224860-224870页

作者： Ji, Kexi Hui, Bei Luo, Guangchun Univ Elect Sci & Technol China Sch Informat & Software Engn Chengdu 610054 Peoples R China Trusted Cloud Comp & Big Data Key Lab Sichuan Pro Chengdu 610054 Peoples R China

graph neural networks have been proven to be very effective for representation learning of knowledge graphs. Recent methods such as SACN and CompGCN, have achieved the most advanced results in knowledge graph completion. However, previous efforts mostly rely on localized first-order approximations of spectral graph convolutions or first-order neighborhoods, ignoring the abundant local structures like cycles and stars. Therefore, the diverse semantic information beneath these structures is not well-captured, leaving opportunities for better knowledge representation which will finally help KGC. In this work, we propose LSA-GAT, a graph attention network with a novel neighborhood aggregation strategy for knowledge graph completion. The model can take special local structures into account, and derive a sophisticated representation covering both the semantic and structural information. Moreover, the LSA-GAT model is combined with a CNN-based decoder to form an encoder-decoder framework with a carefully designed training process. The experimental results show significant improvement of the proposed LSA-GAT compared to current state-of-the-art methods on FB15k-237 and WN18RR datasets.

关键词： knowledge graph embedding knowledge graph completion graph attention networks

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Relation classification via knowledge graph enhanced transformer encoder

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knowledge-BASED SYSTEMS 2020年 206卷 106321-106321页

作者： Huang, Wenti Mao, Yiyu Yang, Zhan Zhu, Lei Long, Jun Cent South Univ Sch Comp Sci & Engn Changsha 410083 Peoples R China Cent South Univ Network Resources Management & Trust Evaluat Key Changsha 410083 Peoples R China

Relation classification is an important task in natural language processing fields. The goal is to predict predefined relations for the marked nominal pairs in given sentences. State-of-the-art works usually focus on using deep neural networks as classifier to conduct the relation prediction. The rich semantic information of relationships in the triples of existing knowledge graph (KG) can be used as additional supervision for relation classification. However, these relationships were simply used as labels to specify the class of sentences in previous works, and their semantic information was completely ignored. In this paper, a novel approach is proposed for relation classification, which jointly uses information from textual sentences and knowledge graphs. To this end, we introduce a Transformer encoder to measure the semantic similarity between sentences and relation types. Besides, we connect the semantic information of marked nominals in sentences with that of the corresponding entities in knowledge graph to generate the semantic matching information between textual relations and KG relations. The matching information can provide additional supervision for relation classification. Since the words and entities are used interactively with each other in our work, we propose an embedding translating strategy to handle the semantic gap problem between word embeddings and entity embeddings. Experimental results on two widely used datasets, SemEval-2010 Task 8 and TACRED, show that our approach is able to efficiently use the semantic information from the knowledge graph to enhance the performance of the Transformer encoder for relation classification. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Relation classification knowledge graph embedding Transformer

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Automatic detection of COVID-19 vaccine misinformation with graph link prediction

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JOURNAL OF BIOMEDICAL INFORMATICS 2021年 124卷 103955-103955页

作者： Weinzierl, Maxwell A. Harabagiu, Sanda M. Univ Texas Dallas Human Language Technol Res Inst Dept Comp Sci Richardson TX 75080 USA

Enormous hope in the efficacy of vaccines became recently a successful reality in the fight against the COVID-19 pandemic. However, vaccine hesitancy, fueled by exposure to social media misinformation about COVID-19 vaccines became a major hurdle. Therefore, it is essential to automatically detect where misinformation about COVID-19 vaccines on social media is spread and what kind of misinformation is discussed, such that inoculation interventions can be delivered at the right time and in the right place, in addition to interventions designed to address vaccine hesitancy. This paper is addressing the first step in tackling hesitancy against COVID-19 vaccines, namely the automatic detection of known misinformation about the vaccines on Twitter, the social media platform that has the highest volume of conversations about COVID-19 and its vaccines. We present COVAXLIES, a new dataset of tweets judged relevant to several misinformation targets about COVID-19 vaccines on which a novel method of detecting misinformation was developed. Our method organizes COVAXLIES in a Misinformation knowledge graph as it casts misinformation detection as a graph link prediction problem. The misinformation detection method detailed in this paper takes advantage of the link scoring functions provided by several knowledge embedding methods. The experimental results demonstrate the superiority of this method when compared with classification-based methods, widely used currently.

关键词： Natural Language Processing Machine learning COVID-19 vaccine misinformation Social Media knowledge graph embedding

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An explainable recommendation framework based on an improved knowledge graph attention network with massive volumes of side information

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knowledge-BASED SYSTEMS 2022年 239卷 107970-107970页

作者： Shimizu, Ryotaro Matsutani, Megumi Goto, Masayuki Waseda Univ Grad Sch Creat Sci & Engn 3-4-1 OhkuboShinjuku Ku Tokyo 1698555 Japan ZOZO Res Kioi Tower 22F1-3 KioichoChiyoda ku Tokyo 1020094 Japan Waseda Univ Sch Creat Sci & Engn 3-4-1 OhkuboShinjuku Ku Tokyo 1698555 Japan

In recent years, explainable recommendation has been a topic of active study. This is because the branch of the machine learning field related to methodologies is enabling human understanding of the reasons for the outputs of recommender systems. The realization of explainable recommendation is widely expected to increase both user satisfaction and the demand for explainable recommendation systems. Explainable recommendation utilizes a wealth of side information (such as sellers, brands, user ages and genders, and bookmark information, among others) to expound the decision-making reasoning applied by recommendation models. In explainable recommendation, although learning side information containing numerous variables leads to rich interpretability, learning too many variables presents a challenge because decreases the amount of learning that a given computational resource can perform, and the accuracy of the recommendation model may be degraded. However, numerous and diverse variables are included in the side information stored by the actual companies operating massive real-world services. Hence, to realize practical applications of this valuable information, it is necessary to resolve problems such as computational cost. In this study, we propose a new framework for explainable recommendation based on an improved knowledge graph attention network model, which utilizes the side information of items and realizes high recommendation accuracy. The proposed framework enables direct interpretation by visualizing the reasons for the recommendations provided. Experimental results show that the proposed framework reduced computational time requirements by approximately 80%, while maintaining recommendation accuracy by enabling the model to learn the probabilistically given edges included in the graph structure. Moreover, the results show that the proposed framework exhibited richer interpretability than the conventional model. Finally, a multifaceted analysis suggests that

关键词： Explainable artificial intelligence Explainable recommendation Model-intrinsic approach knowledge graph attention network knowledge graph embedding

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Towards knowledge Enhanced Language Model for Machine Reading Comprehension

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IEEE ACCESS 2020年 8卷 224837-224851页

作者： Gong, Peizhu Liu, Jin Yang, Yihe He, Huihua Shanghai Maritime Univ Coll Informat Engn Shanghai 201306 Peoples R China Shanghai Normal Univ Coll Educ Shanghai 200234 Peoples R China

Machine reading comprehension is a crucial and challenging task in natural language processing (NLP). Recently, knowledge graph (KG) embedding has gained massive attention as it can effectively provide side information for downstream tasks. However, most previous knowledge-based models do not take into account the structural characteristics of the triples in KGs, and only convert them into vector representations for direct accumulation, leading to deficiencies in knowledge extraction and knowledge fusion. In order to alleviate this problem, we propose a novel deep model KCF-NET, which incorporates knowledge graph representations with context as the basis for predicting answers by leveraging capsule network to encode the intrinsic spatial relationship in triples of KG. In KCF-NET, we fine-tune BERT, a highly performance contextual language representation model, to capture complex linguistic phenomena. Besides, a novel fusion structure based on multi-head attention mechanism is designed to balance the weight of knowledge and context. To evaluate the knowledge expression and reading comprehension ability of our model, we conducted extensive experiments on multiple public datasets such as WN11, FB13, SemEval-2010 Task 8 and SQuAD. Experimental results show that KCF-NET achieves state-of-the-art results in both link prediction and MRC tasks with negligible parameter increase compared to BERT-Base, and gets competitive results in triple classification task with significantly reduced model size.

关键词： Task analysis Semantics knowledge engineering knowledge based systems Bit error rate Encoding Syntactics Machine reading comprehension knowledge graph embedding BERT capsule network

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Constructing negative samples via entity prediction for multi-task knowledge representation learning

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knowledge-BASED SYSTEMS 2023年 281卷

作者： Chen, Guihai Wu, Jianshe Luo, Wenyun Ding, Jingyi Xidian Univ Key Lab Intelligent Percept & Image Understanding Int Res Ctr Intelligent Percept & Computat Minist EducSch Artificial Intelligence Xian 710071 Peoples R China

knowledge representation learning (KRL) aims at embedding the entities and relations in knowledge graphs (KGs) into vectors by learning. In recent years, several multi-task learning (MTL) frameworks were proposed to KRL and achieved promising results. Nevertheless, most of these models only share the initial embeddings of entities and relations among sub-tasks. We construct hard negative samples for relation prediction (RP) task and triple classification (TC) task by using the similarity matrix from the entity prediction (EP) task. We also share the features in hidden layers of EP task to TC. The proposed framework is applicable to CNN based models for KRL. Experimental results on WN18RR and FB15k-237 datasets show that the proposed framework promotes the performance of EP, RP and TC tasks evidently comparing with the single task models and obtains better results in RP and TC than the state-of-the-art MTL-based models/frameworks. Code of NS-KRL is available online.1

关键词： Negative samples knowledge graph embedding knowledge representation learning Information sharing Multi-task learning

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Incorporating multi-perspective information into reinforcement learning to address multi-hop knowledge graph question answering

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EXPERT SYSTEMS WITH APPLICATIONS 2024年第PartC期255卷

作者： Gong, Chuanyang Wei, Zhihua Wang, Rui Zhu, Ping Chen, Jing Zhang, Hongyun Miao, Duoqian Tongji Univ Dept Comp Sci & Technol Shanghai Peoples R China iFlytek Res Shanghai Peoples R China

knowledge graph question answering (KGQA) aims to answer natural language questions from structured knowledge graphs (KGs). Traditional KQGA methods are usually limited to single-hop queries and cannot handle complex questions involving multi-hop reasoning well. To overcome this issue, multi-hop KGQA based on reinforcement learning (RL) has been proposed. However, multi-hop KGQA based on RL still faces some challenges. Firstly, due to the insufficient availability of latent environmental information during the reasoning process, the agent finds it challenging to make coherent and correct decisions. Secondly, the agent only receives rewards from the environment upon reaching the answer entity during the exploration, leading to slow or even obstructed learning. To address these shortcomings, we construct multi-perspective information based on the state of the environment, and integrate multi-perspective information with RL framework, thereby creating the Multi-Perspective Information Fusion Reasoning Network (MPIFRN). MPIFRN achieves the goal via three steps. (1) We construct three different views of information, i.e., expectation embedding, instructionguided embedding, and path-aware embedding. These environmental cues provide more reliable support for decision-making. (2) We still adopt the method of mapping entities and relations into the knowledge graph embedding space to answer multi-hop questions. At each step of reasoning, we use a scoring function to measure the plausibility of each "triple" in the embedding space. (3) Furthermore, we employ the asynchronous advantage actor-critic (A3C) algorithm to guide the agent in selecting the most promising entities and to expand the reasoning paths in parallel by updating policy and value network parameters, thereby facilitating multi-hop knowledge graph question answering. We conduct extensive experiments on KGQA benchmark datasets, providing substantial evidence to demonstrate the effectiveness of our approach.

关键词： Multi-perspective information fusion knowledge graph embedding Multi-hop knowledge graph question answering Reinforcement learning Reasoning interpretability

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A novel Data-Driven framework based on BIM and knowledge graph for automatic model auditing and Quantity Take-off

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ADVANCED ENGINEERING INFORMATICS 2022年第0期54卷

作者： Liu, Hao Cheng, Jack C. P. Gan, Vincent J. L. Zhou, Shanjing Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong 999077 Peoples R China Natl Univ Singapore Dept Built Environm Singapore 117566 Singapore Imperial Coll London Ctr Syst Engn & Innovat Dept Civil & Environm Engn London SW7 2AZ England

Model auditing is a critical step before conducting Building Information Modeling (BIM)-based Quantity Take-off (QTO) because these models may contain various human errors and mistakes, leading to insufficient semantic information and inconsistent modeling style in BIM models. The traditional object-oriented approach has diffi-culties in representing unstructured BIM data (e.g., interrelationships), while rule-based methods involve tremendous human efforts to develop rule sets, lacking flexibility for different requirements. Therefore, this study aims to establish a novel data-driven framework based on BIM and knowledge graph (KG) to represent un-structured BIM data for automatic inferences of auditing results of BIM model mistakes. It starts by establishing a BIM-KG data model via identifying required information for auditing purposes. Subsequently, BIM data is automatically transformed into the BIM-KG representations, the embeddings of which are trained using a knowledge graph embedding model. Automatic mechanisms are then developed to utilize the computable em-beddings to effectively identify mistake BIM elements. The framework is validated using illustrative examples and the results show that 100% mistake elements can be identified successfully without human intervention.

关键词： Building information modeling Quantity take -off BIM information quality BIM model auditing knowledge graph embedding

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TeReKG: A temporal collaborative knowledge graph framework for software team recommendation

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knowledge-BASED SYSTEMS 2024年 289卷

作者： Ruenin, Pisol Choetkiertikul, Morakot Supratak, Akara Tuarob, Suppawong Mahidol Univ Fac Informat & Commun Technol Nakhon Pathom Thailand

Successful software development requires a cohesive team with the right mix of technical skills and the ability to collaborate effectively. However, forming a software team that can execute tasks with precision and efficiency requires a deep understanding of each member's competence, experience, and cooperation history. Previously, automated software team selection has evaluated technical skills, cohesion, and cooperation history. However, the previous method had some limitations. Particularly, local features directly calculated from team members were subjective to the researchers' views, and the method ignored the temporal aspect of open -source software development. To overcome these limitations, this paper proposes a knowledge-graph software team recommendation framework called TeReKG. This framework encapsulates temporal collaboration patterns and uses a temporal knowledge graph to encode software collaboration history, technical abilities, task dependencies, and project structure. TeReKG was against state -of -the -art team recommendation algorithms using three popular open -source software projects: Moodle, Apache, and Atlassian. The evaluation results show that TeReKG outperforms the state -of -the -art baselines in both single-role and team recommendation tasks. These findings demonstrate that knowledge graph embedding can be effectively utilized in automated recommendation tasks in software engineering. Additionally, this highlights the potential for knowledge graphs to capture global information that can benefit various software development applications, including impact prediction of software repositories, code clone detection, and source code retrieval.

关键词： Software team recommendation knowledge graph embedding graph representation learning Collaborative software development

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Relation-aware collaborative autoencoder for personalized multiple facet selection

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knowledge-BASED SYSTEMS 2022年 246卷 1页

作者： Chantamunee, Siripinyo Wong, Kok Wai Fung, Chun Che Murdoch Univ Discipline Informat Technol Perth Australia Walailak Univ Sch Engn & Technol Nakhon Si Thammarat Thailand Walailak Univ Informat Innovat Ctr Excellence Nakhon Si Thammarat Thailand

Collaborative-based personalization has been one of the most successful techniques used in building personalization for recommender systems and facet selection. The technique predicts users' interests based on the preferences of similar people or items. The prediction is usually made on one single group of users or items/facets. However, multiple facet selection creates a different challenge where the prediction needs to be based on the similarity among different groups of users and facets. In conventional collaborative approach, user-facet representation is created from the concatenation of user preferences on each facet. This creates a spared representation which affects the accuracy of the personalized model. It is essential to develop a more suitable representation that effectively represents the collaborative preferences given across multiple facets and a predictive model to estimate the possible preferences across those groups. Multiple facets appear to be correlated to each other and this can be useful for associating the existing preferences. None of the previous works has addressed the issue due to the association of facet relationships. Hence, this paper aims to examine the effectiveness of a new approach that utilizes multiple-facet relationships to associate the collaborative interests across different facets. This study proposes a new collaborative-based personalization model for multiple facet selection, called Relation-aware Collaborative Autoencoder (RCAE) Model. A new embedding methodology was introduced for incorporating multiple facet relationships into user-facet interaction. Evaluations based on four real-world datasets demonstrated that the proposed model utilizing facet relationships has achieved significant improvement over the conventional collaborative approach. (C)& nbsp;2022 Elsevier B.V. All rights reserved.

关键词： Multiple facets Personalized facet selection Collaborative-based personalization Autoencoder Deep neural network knowledge graph embedding

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