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2023 workshop on Deep Learning for Knowledge graphs, DL4KG 2023

作者： Cadeddu, Andrea Chessa, Alessandro De Leo, Vincenzo Fenu, Gianni Motta, Enrico Osborne, Francesco Recupero, Diego Reforgiato Salatino, Angelo Secchi, Luca Linkalab s.r.l. Cagliari Italy Department of Mathematics and Computer Science University of Cagliari Cagliari Italy Knowledge Media Institute The Open University Milton Keynes United Kingdom Department of Business and Law University of Milano Bicocca Milan Italy

The use of transformer-based models like BERT for natural language processing has achieved remarkable performance across multiple domains. However, these models face challenges when dealing with very specialized domains, such as scientific literature. In this paper, we conduct a comprehensive analysis of knowledge injection strategies for transformers in the scientific domain, evaluating four distinct methods for injecting external knowledge into transformers. We assess these strategies in a single-label multi-class classification task involving scientific papers. For this, we develop a public benchmark based on 12k scientific papers from the AIDA knowledge graph, categorized into three fields. We utilize the Computer Science Ontology as our external knowledge source. Our findings indicate that most proposed knowledge injection techniques outperform the BERT baseline. © 2022 Copyright for this paper by its authors.

关键词： Knowledge graph

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Pre-Training and Prompting for Few-Shot Node Classification on Text-Attributed graphs 24

Pre-Training and Prompting for Few-Shot Node Classification ...

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30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

作者： Zhao, Huanjing Yang, Beining Cen, Yukuo Ren, Junyu Zhang, Chenhui Dong, Yuxiao Kharlamov, Evgeny Zhao, Shu Tang, Jie Tsinghua Univ Beijing Peoples R China Univ Edinburgh Edinburgh Midlothian Scotland Zhipu AI Beijing Peoples R China Bosch Ctr Artificial Intelligence Renningen Germany Anhui Univ Hefei Anhui Peoples R China

ISBN: (纸本)9798400704901

The text-attributed graph (TAG) is one kind of important real-world graph-structured data with each node associated with raw texts. For TAGs, traditional few-shot node classification methods directly conduct training on the pre-processed node features and do not consider the raw texts. The performance is highly dependent on the choice of the feature pre-processing method. In this paper, we propose P2TAG1, a framework designed for few-shot node classification on TAGs with graph pre-training and prompting. P2TAG first pre-trains the language model (LM) and graph neural network (GNN) on TAGs with self-supervised loss. To fully utilize the ability of language models, we adapt the masked language modeling objective for our framework. The pre-trained model is then used for the few-shot node classification with a mixed prompt method, which simultaneously considers both text and graph information. We conduct experiments on six real-world TAGs, including paper citation networks and product co-purchasing networks. Experimental results demonstrate that our proposed framework outperforms existing graph few-shot learning methods on these datasets with +18.98% similar to +35.98% improvements.

关键词： graph self-supervised learning text-attributed graphs few-shot node classification graph neural networks

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Energy Consumption Prediction for Manufacturing in Industrial IoT based on Heterogeneous GNN

Energy Consumption Prediction for Manufacturing in Industria...

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IEEE/CIC International Conference on Communications in China (ICCC)

作者： Su, Xuan Li, Mingyan Li, Qihao Chen, Chao Guo, Songtao Wang, Xu Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Univ Coll Comp Sci Chongqing 400044 Peoples R China Minist Educ China Key Lab Syst Control & Informat Proc Shanghai Peoples R China Jilin Univ Coll Commun Engn Changchun 130015 Peoples R China

ISBN: (纸本)9798350378412

Energy consumption prediction plays a crucial role in green manufacturing for industry, providing a theoretical foundation for energy-saving planning, workshop scheduling, and process optimization. However, owing to the processing complexity of mechanical products and the multiplicity of production information, previous methods can only achieve prediction for individual devices, which are quite restrictive to collaborative manufacturing planning. To deal with product-level energy management, this paper first proposes a heterogeneous manufacturing association graph (HetMG) which contains various manufacturing elements including product projects, decomposed orders and processing devices. Then, with HetMG, an energy consumption prediction method, HetEP, is introduced based on heterogeneous graph neural network (GNN), which incorporates Relational graph Convolutional Networks (R-GCNs), and Long Short-Term Memory (LSTM) to mine the spatial-temporal relation of heterogeneous elements during the manufacturing process. Moreover, graph Attention Network (GAT) is utilized in HetEP to adaptively learn the graph-structured data so that informative representations of each element can be obtained. The experimental results based on a manufacturing dataset from a generator factory demonstrate that the proposed HetEP yields superior prediction performance compared to the device-oriented prediction methods in terms of prediction accuracy.

关键词： enery consumption prediction green manufacturing heterogeneous graph deep learning network

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Story Point Level Classification by Text Level graph Neural Network 1

Story Point Level Classification by Text Level Graph Neural ...

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1st IEEE/ACM International workshop on natural language-based Software Engineering (NLBSE)

作者： Phan, Hung Jannesari, Ali Iowa State Univ Ames IA 50011 USA

ISBN: (纸本)9781450393430

Estimating the software projects' efforts developed by agile methods is important for project managers or technical leads. It provides a summary as a first view of how many hours and developers are required to complete the tasks. There are research works on automatic predicting the software efforts, including Term Frequency Inverse Document Frequency (TFIDF) as the traditional approach for this problem. graph Neural Network is a new approach that has been applied in natural language processing for text classification. The advantages of graph Neural Network are based on the ability to learn information via graph data structure, which has more representations such as the relationships between words compared to approaches of vectorizing sequence of words. In this paper, we show the potential and possible challenges of graph Neural Network text classification in story point level estimation. By the experiments, we show that the GNN Text Level Classification can achieve as high accuracy as about 80% for story points level classification, which is comparable to the traditional approach. We also analyze the GNN approach and point out several current disadvantages that the GNN approach can improve for this problem or other problems in software engineering.

关键词： graph neural network story point estimation term frequency

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KG-Rank: Enhancing Large language Models for Medical QA with Knowledge graphs and Ranking Techniques 23

KG-Rank: Enhancing Large Language Models for Medical QA with...

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23rd Meeting of the ACL Special Interest Group on Biomedical natural language processing, BioNLP 2024

作者： Yang, Rui Liu, Haoran Marrese-Taylor, Edison Zeng, Qingcheng Ke, Yu He Li, Wanxin Cheng, Lechao Chen, Qingyu Caverlee, James Matsuo, Yutaka Li, Irene Duke-NUS Medical School Singapore Texas A&M University United States The University of Tokyo Japan Northwestern University United States Singapore General Hospital Singapore Zhejiang University China Zhejiang Lab China Yale University United States National Institutes of Health United States

ISBN: (纸本)9798891761308

Large language models (LLMs) have demonstrated impressive generative capabilities with the potential to innovate in medicine. However, the application of LLMs in real clinical settings remains challenging due to the lack of factual consistency in the generated content. In this work, we develop an augmented LLM framework, KG-Rank, which leverages a medical knowledge graph (KG) along with ranking and re-ranking techniques, to improve the factuality of long-form question answering (QA) in the medical domain. Specifically, when receiving a question, KG-Rank automatically identifies medical entities within the question and retrieves the related triples from the medical KG to gather factual information. Subsequently, KG-Rank innovatively applies multiple ranking techniques to refine the ordering of these triples, providing more relevant and precise information for LLM inference. To the best of our knowledge, KG-Rank is the first application of KG combined with ranking models in medical QA specifically for generating long answers. Evaluation on four selected medical QA datasets demonstrates that KG-Rank achieves an improvement of over 18% in ROUGE-L score. Additionally, we extend KG-Rank to open domains, including law, business, music, and history, where it realizes a 14% improvement in ROUGE-L score, indicating the effectiveness and great potential of KG-Rank.. ©2024 Association for Computational Linguistics.

关键词： Knowledge graph

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Towards the first Named Entity Recognition of Inuktitut for an Improved Machine Translation 3

Towards the First Named Entity Recognition of Inuktitut for ...

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3rd workshop on natural language processing for Indigenous languages of the Americas, AmericasNLP 2023, co-located with ACL 2023

作者： Le, Ngoc Tan Kasdi, Ikram Sadat, Fatiha Université du Québec à Montréal Canada

ISBN: (纸本)9781959429913

Named Entity Recognition is a crucial step to ensure good quality performance of several natural language processing applications and tools, including machine translation and information retrieval. Moreover, it is considered as a fundamental module of many natural language Understanding tasks such as question-answering systems. This paper presents a first study on NER for an under-represented Indigenous Inuit language of Canada, Inuktitut, which lacks linguistic resources and large labeled data. Our proposed NER model for Inuktitut is built by transferring linguistic characteristics from English to Inuktitut, based on either rules or bilingual word embeddings. We provide an empirical study based on a comparison with the state of the art models and as well as intrinsic and extrinsic evaluations. In terms of Recall, Precision and F-score, the obtained results show the effectiveness of the proposed NER methods. Furthermore, it improved the performance of Inuktitut-English Neural Machine Translation. ©2023 Association for Computational Linguistics.

关键词： Neural machine translation

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Unleashing the Power of Emojis in Texts via Self-supervised graph Pre-Training

Unleashing the Power of Emojis in Texts via Self-supervised ...

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2024 Conference on Empirical methods in natural language processing, EMNLP 2024

作者： Zhang, Zhou Tan, Dongzeng Wang, Jiaan Chen, Yilong Xu, Jiarong Fudan University China Alibaba Group China

ISBN: (纸本)9798891761643

Emojis have gained immense popularity on social platforms, serving as a common means to supplement or replace text. However, existing data mining approaches generally either completely ignore or simply treat emojis as ordinary Unicode characters, which may limit the model's ability to grasp the rich semantic information in emojis and the interaction between emojis and texts. Thus, it is necessary to release the power of emojis in social media data mining. To this end, we first construct a heterogeneous graph consisting of three types of nodes, i.e., post, word and emoji nodes to improve the representation of different elements in posts. The edges are also well-defined to model how these three elements interact with each other. To facilitate the sharing of information among post, word and emoji nodes, we propose a graph pre-training framework for text and emoji co-modeling, which contains two graph pre-training tasks: node-level graph contrastive learning and edge-level link reconstruction learning. Extensive experiments on the Xiaohongshu and Twitter datasets with two types of downstream tasks demonstrate that our approach proves significant improvement over previous strong baseline methods. © 2024 Association for Computational Linguistics.

关键词： Contrastive Learning

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Hamiltonian deep neural network fostered sentiment analysis approach on product reviews

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SIGNAL IMAGE AND VIDEO processing 2024年第4期18卷 3483-3494页

作者： Ajmeera, Narahari Kamakshi, P. JNTUH Univ Coll Engn Dept Comp Sci & Engn Manthani Telangana India Kakatiya Inst Technol & Sci Dept Informat Technol Warangal Telangana India

In recent times, online shopping has become commonly used method for consumers to make purchases and engage in consumption with the rapid advancement of Internet technology. Enhancing user satisfaction is achievable through sentiment analysis (SA) of the enormous user evaluations found on e-commerce platforms. However, accurately predicting the sentiment orientations of these user reviews remains a challenge due to varying sequence lengths, text arrangements, and intricate logic. Nowadays, sentiment analysis is widely employed to assess customer feedback, which holds great significance in determining a product's success. In the past, people relied on word-of-mouth reviews to judge a product's quality. This practice of sentiment analysis is extensively applied in social media. natural language processing (NLP) plays a crucial role in deciphering sentiment, also referred to as opinion mining or emotion AI, as it encompasses the collective perception of customers. In this manuscript, a Hamiltonian Deep Neural Networks-based Sentiment Analysis on Product Recommendation System (HDNN-SCOA-SA-PR) is proposed. first, the data are gathered from Amazon Product Reviews dataset. Then the data are pre-processed utilizing adaptive self-guided filtering for space tokenization, Gensim lemmatization, and Snowball stemming. By using Structured Optimal graph-based Sparse Feature Extraction, the features are extracted. Extracted features are selected using Single Candidate Optimization Algorithm. Finally, the classification process is done using Hamiltonian deep neural network and classified sentiment analysis as positive, negative, neutral. The proposed HDNN-SCOA-SA-PR method is activated in Python, and the efficiency of the proposed method is analyzed with different metrics, such as accuracy, sensitivity, RoC, precision, error rate, F1-score,computation time. ROC is evaluated and compared to the existing methods, such as sentiment analysis based upon machine learning of online produc

关键词： Hamiltonian deep neural network Adaptive self-guided filtering Structured Optimal graph-based Sparse Feature Extraction Sentiment analysis Product reviews Single Candidate Optimization Algorithm

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BERTologyNavigator: Advanced Question Answering with BERT-based Semantics 1

BERTologyNavigator: Advanced Question Answering with BERT-ba...

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1st Scholarly QALD Challenge 2023 and 4th SeMantic Answer Type, Relation and Entity Prediction Tasks Challenge, Scholarly QALD 2023 and SemREC 2023

作者： Rajpal, Shreya Usbeck, Ricardo Universität Hamburg Hamburg Germany Vellore Institute of Technology Tamil Nadu Vellore India

The development and integration of knowledge graphs and language models has significance in artificial intelligence and natural language processing. In this study, we introduce the BERTologyNavigator- a two-phased system that combines relation extraction techniques and BERT embeddings to navigate the relationships within the DBLP Knowledge graph (KG). Our approach focuses on extracting one-hop relations and labelled candidate pairs in the first phases. This is followed by employing BERT's CLS embeddings and additional heuristics for relation selection in the second phase. Our system reaches an F1 score of 0.2175 on the DBLP QuAD Final test dataset for Scholarly QALD and 0.98 F1 score on the subset of the DBLP QuAD test dataset during the QA phase. © 2023 CEUR-WS. All rights reserved.

关键词： Knowledge graph

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CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Complex Knowledge graph Question Answering

CoTKR: Chain-of-Thought Enhanced Knowledge Rewriting for Com...

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2024 Conference on Empirical methods in natural language processing, EMNLP 2024

作者： Wu, Yike Huang, Yi Hu, Nan Hua, Yuncheng Qi, Guilin Chen, Jiaoyan Pan, Jeff Z. Southeast University Jiangsu Nanjing China Ministry of Education China China Mobile Research Institute Beijing China Monash University Melbourne Australia University of Manchester Manchester United Kingdom University of Edinburgh Edinburgh United Kingdom

ISBN: (纸本)9798891761643

Recent studies have explored the use of Large language Models (LLMs) with Retrieval Augmented Generation (RAG) for Knowledge graph Question Answering (KGQA). They typically require rewriting retrieved subgraphs into natural language formats comprehensible to LLMs. However, when tackling complex questions, the knowledge rewritten by existing methods may include irrelevant information, omit crucial details, or fail to align with the question's semantics. To address them, we propose a novel rewriting method CoTKR, Chain-of-Thought Enhanced Knowledge Rewriting, for generating reasoning traces and corresponding knowledge in an interleaved manner, thereby mitigating the limitations of single-step knowledge rewriting. Additionally, to bridge the preference gap between the knowledge rewriter and the question answering (QA) model, we propose a training strategy PAQAF, Preference Alignment from Question Answering Feedback, for leveraging feedback from the QA model to further optimize the knowledge rewriter. We conduct experiments using various LLMs across several KGQA benchmarks. Experimental results demonstrate that, compared with previous knowledge rewriting methods, CoTKR generates the most beneficial knowledge representation for QA models, which significantly improves the performance of LLMs in KGQA. © 2024 Association for Computational Linguistics.

关键词： Knowledge graph

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