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检索条件"主题词=Knowledge Graph embedding"

共 566 条记录，以下是161-170 订阅

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Incorporating Uncertainty of Entities and Relations into Few-Shot Uncertain knowledge graph embedding 7th

Incorporating Uncertainty of Entities and Relations into Few...

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7th China Conference on knowledge graph and Semantic Computing (CCKS)

作者： Wang, Jingting Wu, Tianxing Zhang, Jiatao Southeast Univ Nanjing Peoples R China

ISBN: (纸本)9789811975950;9789811975967

In this paper, we study the problem of embedding few-shot uncertain knowledge graphs. Observing the existing embedding methods may discard the uncertainty information, or require sufficient training data for each relation, we propose a novel method by incorporating the inherent uncertainty of entities and relations (i.e. element-level uncertainty) into uncertain knowledge graph embedding. We introduce different metrics to quantify the uncertainty of different entities and relations. By employing a metric-based framework, our method can effectively capture both semantic and uncertainty information of entities and relations in the few-shot scenario. Experimental results show that our proposed method can learn better embeddings in terms of the higher accuracy in both confidence score prediction and tail entity prediction.

关键词： Uncertain knowledge graph knowledge graph embedding Few-shot learning

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Learning Attention-Based Translational knowledge graph embedding via Nonlinear Dynamic Mapping 25th

Learning Attention-Based Translational Knowledge Graph Embed...

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25th Pacific-Asia Conference on knowledge Discovery and Data Mining (PAKDD)

作者： Wang, Zhihao Xu, Honggang Li, Xin Deng, Yuxin East China Normal Univ Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China

ISBN: (纸本)9783030757687;9783030757670

knowledge graph embedding has become a promising method for knowledge graph completion. It aims to learn low-dimensional embeddings in continuous vector space for each entity and relation. It remains challenging to learn accurate embeddings for complex multi-relational facts. In this paper, we propose a new translation-based embedding method named ATransD-NL to address the following two observations. First, most existing translational methods do not consider contextual information that have been proved useful for improving performance of link prediction. Our method learns attention-based embeddings for each triplet taking into account influence of one-hop or potentially multi-hop neighbourhood entities. Second, we apply nonlinear dynamic projection of head and tail entities to relational space, to capture nonlinear correlations among entities and relations due to complex multi-relational facts. As an extension of TransD, our model only introduces one more extra parameter, giving a good tradeoff between model complexity and the state-of-the-art predictive accuracy. Compared with state-of-the-art translation-based methods and the neural-network based methods, experiment results show that our method delivers substantial improvements over baselines on the MeanRank metric of link prediction, e.g., an improvement of 35.6% over the attention-based graph embedding method KBGAT and an improvement of 64% over the translational method TransMS on WN18 database, with comparable performance on the Hits@10 metric.

关键词： knowledge graph embedding Translation-based methods Link prediction Attention mechanism

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A Closer Look at Probability Calibration of knowledge graph embedding 11

A Closer Look at Probability Calibration of Knowledge Graph ...

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11th International Joint Conference on knowledge graphs (IJCKG)

作者： Zhu, Ruiqi Wang, Fangrong Bundy, Alan Li, Xue Nuamah, Kwabena Xu, Lei Mauceri, Stefano Pan, Jeff Z. Univ Edinburgh Sch Informat Edinburgh Midlothian Scotland Huawei Ireland Res Ctr Dublin Ireland Huawei Ireland Res CSI Dublin Ireland

ISBN: (纸本)9781450399876

When the estimated probabilities do not match the relative frequencies, we say these estimated probabilities are uncalibrated [39], which may cause incorrect decision making, and is particularly undesired in high-stakes tasks [45]. knowledge graph embedding models are reported to produce uncalibrated probabilities [36], e.g., for all the triples predicted with probability 0.9, the percentage of them being truly correct triples is not 90%. In this article, we take a closer look at this problem. First, we confirmed the issue that typical KG embedding models are uncalibrated. Then, we show how off-the-shelf calibration techniques can be used to mitigate this issue, among which binning-based calibration produces more calibrated probabilities. We also investigated the possible reasons for the uncalibrated probabilities and found that the expit transform, the way used to convert embedding scores into probabilities, is ineffective in most cases.

关键词： knowledge graph embedding Probability Calibration

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Literal-Aware knowledge graph embedding for Welding Quality Monitoring: A Bosch Case 1

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22nd International Semantic Web Conference (ISWC)

作者： Tan, Zhipeng Zhou, Baifan Zheng, Zhuoxun Savkovic, Ognjen Huang, Ziqi Gonzalez, Irlan-Grangel Soylu, Ahmet Kharlamov, Evgeny Bosch Ctr AI Renningen Germany Rhein Westfal TH Aachen Aachen Germany Univ Oslo Dept Informat Oslo Norway Oslo Metropolitan Univ Dept Comp Sci Oslo Norway Free Univ Bozen Bolzano Dept Comp Sci Bolzano Italy

ISBN: (数字)9783031472435

ISBN: (纸本)9783031472428;9783031472435

Recently there has been a series of studies in knowledge graph embedding (KGE), which attempts to learn the embeddings of the entities and relations as numerical vectors and mathematical mappings via machine learning (ML). However, there has been limited research that applies KGE for industrial problems in manufacturing. This paper investigates whether and to what extent KGE can be used for an important problem: quality monitoring for welding in manufacturing industry, which is an impactful process accounting for production of millions of cars annually. The work is in line with Bosch research of data-driven solutions that intends to replace the traditional way of destroying cars, which is extremely costly and produces waste. The paper tackles two very challenging questions simultaneously: how large the welding spot diameter is;and to which car body the welded spot belongs to. The problem setting is difficult for traditional ML because there exist a high number of car bodies that should be assigned as class labels. We formulate the problem as link prediction, and experimented popular KGE methods on real industry data, with consideration of literals. Our results reveal both limitations and promising aspects of adapted KGE methods.

关键词： knowledge graph embedding welding quality monitoring literal embedding knowledge graph construction open dataset

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EMDKG: Improving Accuracy-Diversity Trade-Off in Recommendation with EM-based Model and knowledge graph embedding

EMDKG: Improving Accuracy-Diversity Trade-Off in Recommendat...

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IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (WI-IAT)

作者： Gan, Lu Nurbakova, Diana Laporte, Lea Calabretto, Sylvie Univ Lyon INSA Lyon Villeurbanne France

ISBN: (纸本)9781450391153

To maintain attractiveness and reduce redundancy of recommendation, the concept of diversity has been brought up in recommender systems (RS). Thus, advanced RS aim at achieving both better accuracy and diversity facing a trade-off issue between the two aspects. Recently, knowledge graphs embedding methods have been widely used in RS for achieving better accuracy provided with auxiliary information along with historical user-item interactions. However, little work has been done to investigate what effects of diversity it brings along with higher accuracy results and how to achieve the best accuracy-diversity trade-off under such circumstances. In this paper, we propose an EM-model capable of incorporating a generalized concept of diversity for a diversity-encoded knowledge graph embedding based recommendation. Our EM-model alternates between a general item diversity learning and knowledge graph embedding learning for user and item representation, which helps to achieve better results in terms of both accuracy and diversity compared to the state-of-art baselines on datasets MovieLens and Anime. Moreover, extensive experiments prove our model outperforms the baseline with existing diversification methods (MMR and DPP) achieving a better accuracy-diversity trade-off.

关键词： Recommender Systems knowledge graph embedding Diversity Accuracy Trade-off

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A Comprehensive Study on knowledge graph embedding over Relational Patterns Based on Rule Learning 22nd

A Comprehensive Study on Knowledge Graph Embedding over Rela...

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22nd International Semantic Web Conference (ISWC)

作者： Jin, Long Yao, Zhen Chen, Mingyang Chen, Huajun Zhang, Wen Zhejiang Univ Sch Software Technol Hangzhou Peoples R China Zhejiang Univ Coll Comp Sci & Technol Hangzhou Peoples R China Donghai Lab Hangzhou Peoples R China

ISBN: (纸本)9783031472398;9783031472404

knowledge graph embedding (KGE) has proven to be an effective approach to solving the knowledge graph Completion (KGC) task. Relational patterns which refer to relations with specific semantics exhibiting graph patterns are an important factor in the performance of KGE models. Though KGE models' capabilities are analyzed over different relational patterns in theory and a rough connection between better relational patterns modeling and better performance of KGC has been built, a comprehensive quantitative analysis on KGE models over relational patterns remains absent so it is uncertain how the theoretical support of KGE to a relational pattern contributes to the performance of triples associated to such a relational pattern. To address this challenge, we evaluate the performance of 7 KGE models over 4 common relational patterns on 2 benchmarks, then conduct an analysis in theory, entity frequency, and part-to-whole three aspects and get some counter-intuitive conclusions. Finally, we introduce a training-free method Scorebased Patterns Adaptation (SPA) to enhance KGE models' performance over various relational patterns. This approach is simple yet effective and can be applied to KGE models without additional training. Our experimental results demonstrate that our method generally enhances performance over specific relational patterns. Our source code is available from GitHub at https://***/zjukg/Comprehensive-Study-over- Relational- Patterns.

关键词： Relational patterns knowledge graph embedding Rule mining knowledge graph completion

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A Software Security Entity Relationships Prediction Framework Based on knowledge graph embedding Using Sentence-Bert 17th

A Software Security Entity Relationships Prediction Framewor...

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17th International Conference on Wireless Algorithms, Systems, and Applications (WASA)

作者： Wang, Yan Hou, Xiaowei Ma, Xiu Lv, Qiujian Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China

ISBN: (纸本)9783031192135;9783031192142

Recently, the need for complex cyber attack knowledge is increasing with the rising risk of software vulnerabilities and weaknesses on the internet. To spread knowledge and strengthen software security defense, researchers record software vulnerabilities, weaknesses, and attack patterns through software databases, including CVE, CWE, and CAPEC, etc. However, software security databases are time delayed and thus miss unobserved facts. Attackers can take advantage of this problem to execute an attack successfully. Therefore, the reasoning task of predicting software security entity relation is critical to supplementing software security data. This paper constructs a software security knowledge graph and proposes a knowledge graph representation learning method combining Sentence-Bert and GAT. The way can implement link prediction and classification tasks for knowledge graph completion. We finally designed a large number of experiments to evaluate the effectiveness of our model in knowledge graph completion and knowledge graph classification. The experimental results demonstrate that the proposed method can effectively improve the effectiveness of prediction.

关键词： Software security entity Entity relation prediction Sentence-bert knowledge graph embedding graph attention network

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Recommending Analogical APIs via knowledge graph embedding 2023

Recommending Analogical APIs via Knowledge Graph Embedding

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31st ACM Joint Meeting of the European Software Engineering Conference / Symposium on the Foundations-of-Software-Engineering (ESEC/FSE)

作者： Liu, Mingwei Yang, Yanjun Lou, Yiling Peng, Xin Zhou, Zhong Du, Xueying Yang, Tianyong Fudan Univ Shanghai Peoples R China Fudan Univ Sch Comp Sci Shanghai Peoples R China Fudan Univ Shanghai Key Lab Data Sci Shanghai Peoples R China

ISBN: (纸本)9798400703270

Library migration, which replaces the current library with a different one to retain the same software behavior, is common in software evolution. An essential part of this is finding an analogous API for the desired functionality. However, due to the multitude of libraries/APIs, manually finding such an API is time-consuming and error-prone. Researchers created automated analogical API recommendation techniques, notably documentation-based methods. Despite potential, these methods have limitations, e.g., incomplete semantic understanding in documentation and scalability issues. In this study, we present KGE4AR, a novel documentation-based approach using knowledge graph (KG) embedding for recommending analogical APIs during library migration. KGE4AR introduces a unified API KG to comprehensively represent documentation knowledge, capturing high-level semantics. It further embeds this unified API KG into vectors for efficient, scalable similarity calculation. We assess KGE4AR with 35,773 Java libraries in two scenarios, with and without target libraries. KGE4AR notably outperforms state-of-the-art techniques (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements), showcasing scalability with growing library counts.

关键词： API Migration knowledge graph knowledge graph embedding

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Two Problems in knowledge graph embedding: Non-Exclusive Relation Categories and Zero Gradients

Two Problems in Knowledge Graph Embedding: Non-Exclusive Rel...

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IEEE International Conference on Big Data (Big Data)

作者： Nur, Nasheen Park, Noseong Lee, Kookjin Kang, Hyunjoong Kwon, Soonhyeon Univ North Carolina Charlotte Charlotte NC 28223 USA George Mason Univ Fairfax VA 22030 USA Sandia Natl Labs Livermore CA USA Elect & Telecommun Res Inst Daejeon South Korea

ISBN: (纸本)9781728108582

knowledge graph embedding (KGE) learns latent vector representations of named entities (i.e., vertices) and relations (i.e., edge labels) of knowledge graphs. Herein, we address two problems in KGE. First, relations may belong to one or multiple categories, such as functional, symmetric, transitive, reflexive, and so forth;thus, relation categories are not exclusive. Some relation categories cause non-trivial challenges for KGE. Second, we found that zero gradients happen frequently in many translation based embedding methods such as TransE and its variations. To solve these problems, we propose i) converting a knowledge graph into a bipartite graph, although we do not physically convert the graph but rather use an equivalent trick;ii) using multiple vector representations for a relation;and iii) using a new hinge loss based on energy ratio (rather than energy gap) that does not cause zero gradients. We show that our method significantly improves the quality of embedding.

关键词： Relational Machine Learning knowledge graph knowledge graph embedding

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Low-Dimensional Hyperbolic knowledge graph embedding for Better Extrapolation to Under-Represented Data 21st

Low-Dimensional Hyperbolic Knowledge Graph Embedding for Bet...

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21st International Conference on The Semantic Web (ESWC)

作者： Zheng, Zhuoxun Zhou, Baifan Yang, Hui Tan, Zhipeng Waaler, Arild Kharlamov, Evgeny Soylu, Ahmet Bosch Ctr AI Renningen Germany Univ Oslo Oslo Norway Oslo Metropolitan Univ Oslo Norway Lab Interdisciplinaire Sci Numer Paris France

ISBN: (纸本)9783031606250;9783031606267

Past works have shown knowledge graph embedding (KGE) methods learn from facts in the form of triples and extrapolate to unseen triples. KGE in hyperbolic space can achieve impressive performance even in low-dimensional embedding space. However, existing work limitedly studied extrapolation to under-represented data, including under-represented entities and relations. To this end, we propose HolmE, a general form of KGE method on hyperbolic manifolds. HolmE addresses extrapolation to under-represented entities through a special treatment of the bias term, and extrapolation to under-represented relations by supporting strong composition. We provide empirical evidence that HolmE achieves promising performance in modelling unseen triples, under-represented entities, and under-represented relations. We prove that mainstream KGE methods either: (1) are special cases of HolmE and thus support strong composition;(2) do not support strong composition. The code and data are open-sourced at https://***/nsai-uio/HolmE-KGE.

关键词： knowledge graph embedding knowledge graph

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