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Article
Engineering, Industrial
Bin Zhou et al.
Summary: This paper proposes a method of analyzing time series data based on an industrial knowledge graph, which effectively improves the knowledge processing efficiency of the manufacturing system by integrating temporal features and semantic information. By using deep learning models and graph embedding techniques, semantic information related to product quality prediction can be extracted from time series data and linked to the knowledge graph, describing the dynamic semantic information of the manufacturing environment.
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH
(2023)
Article
Computer Science, Information Systems
Zhao Li et al.
Summary: In this paper, a novel knowledge representation learning model, TransO, is proposed to efficiently incorporate rich ontology information and improve model performance with low complexity. Experimental results demonstrate its superiority over existing methods.
WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
(2023)
Article
Computer Science, Information Systems
Jinbo Liu et al.
Summary: Next-location prediction is a task of recommending the next POIs based on context-dependency such as sequential, temporal, and spatial dependencies. Existing methods fail to capture both temporal and location topology information, which motivated us to propose a GNN-based model that comprehensively represents dynamic preferences by converting POIs into a low-dimensional metric and integrating long-term and short-term user preferences. Our experimental results on real-world datasets demonstrate the effectiveness of our approach over state-of-the-art methods for next-location prediction.
INFORMATION SCIENCES
(2023)
Article
Computer Science, Hardware & Architecture
Yulu Qi et al.
Summary: This paper introduces a method of incorporating a knowledge graph into compound attack detection and constructs a cybersecurity knowledge graph based on known attacks. The cybersecurity knowledge graph can perform correlation analysis on real-time data to restore the attack process. The main contribution of this paper is the construction of the cybersecurity knowledge graph and the automatic discovery of compound attacks. Additionally, the paper proposes a multi-dimensional data association analysis algorithm based on a dynamic clustering mechanism and an attack chain complementation-pruning method based on optimal reaching path queries to address efficiency and data collection issues. Experimental results demonstrate that the proposed methods improve the accuracy and efficiency of attack chain mining.
COMPUTERS & ELECTRICAL ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yilin Zhang et al.
Summary: This study proposes a novel framework that leverages event structure knowledge to address the issues of ambiguous and unseen trigger words. By constructing an event background graph and dynamically matching appropriate event structure knowledge, it can effectively resolve polysemous triggers and identify unseen triggers.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Information Systems
Xin Bi et al.
Summary: This paper proposes a weakly supervised multi-hop knowledge graph question answering (KGQA) model to address the labeling cost issue of intermediate relations and the problem of incorrect reasoning paths. The proposed model incorporates both terminal and instant rewards in the reasoning process and utilizes intermediate supervision and an evaluation network to guide the reasoning path and improve the answering accuracy.
INFORMATION PROCESSING & MANAGEMENT
(2023)
Article
Computer Science, Artificial Intelligence
Uno Fang et al.
Summary: Graph convolution networks (GCN) have gained popularity for image clustering, but existing GCN-based techniques struggle with reasoning on cluster boundaries. To address this, we propose a supervised approach called CGL which generates influential and topological graph views for each class to reason inter-cluster relationships and intra-cluster boundaries. Our method treats each class as a fully connected graph and strategically generates directional graph views, resulting in improved transferability and outperforming state-of-the-art methods.
PATTERN RECOGNITION
(2023)
Article
Computer Science, Artificial Intelligence
Saiping Guan et al.
Summary: In addition to entity-centric knowledge organized as Knowledge Graph (KG), events are also an important form of knowledge that led to the emergence of event-centric knowledge representation forms like Event KG (EKG). EKG plays a crucial role in various downstream applications such as search, question-answering, recommendation, financial quantitative investments, and text generation. This paper provides a comprehensive survey of EKG from history, ontology, instance, and application perspectives, including its development processes, trends, and prospective directions for future research.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Yan Jia et al.
Summary: Predicting potential facts in the future is challenging due to the deep dependence between temporal association and semantic patterns of facts. Most existing methods fail to attach importance to impactful facts and events. Therefore, a novel model called ReTIN is proposed, which integrates real-time influence of historical facts based on hyperbolic geometry to effectively capture hierarchical relations among facts.
CAAI TRANSACTIONS ON INTELLIGENCE TECHNOLOGY
(2023)
Article
Computer Science, Artificial Intelligence
Shaoxiong Ji et al.
Summary: This survey provides a comprehensive review of knowledge graphs, covering topics such as knowledge graph representation learning, knowledge acquisition and completion, temporal knowledge graphs, and knowledge-aware applications. The study proposes a categorization and taxonomies on these topics, as well as explores emerging themes like metarelational learning, commonsense reasoning, and temporal knowledge graphs. Additionally, the research offers curated data sets and open-source libraries to facilitate future research in the field of knowledge graphs.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xin Bi et al.
Summary: Knowledge graphs enhance the quality of retrieving answers for natural language questions. UMRNet, an unrestricted multi-hop reasoning network, addresses the challenges of multi-hop KGQA by tackling the issues of delayed termination and mapping problems. With dynamic update strategy and non-delayed termination detection mechanism, UMRNet outperforms existing methods in terms of accuracy and efficiency.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Xiangyu Song et al.
Summary: This paper focuses on deep learning-based knowledge tracing models. By systematically investigating, comparing, and discussing different aspects of these models, researchers can be better assisted in this field. The findings of this study contribute significantly to the progress of online education, particularly in the context of the current global pandemic. Future research directions in the field of deep learning-based knowledge tracing are also discussed.
KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Anandakumar Haldorai et al.
Summary: People prioritize the overall quality of the urban ecosystem, with air pollution being a significant challenge to address. A neural network model based on canonical correlation analysis is proposed to evaluate and enhance the sustainability of urban environments.
NEURAL PROCESSING LETTERS
(2021)
Article
Computer Science, Artificial Intelligence
Xiaozhi Wang et al.
Summary: The paper introduces a unified model for Knowledge Embedding and Pre-trained Language Representation (KEPLER), which integrates entity descriptions into pre-trained language models to optimize knowledge embedding and language modeling objectives, achieving state-of-the-art performances in NLP tasks and KG link prediction.
TRANSACTIONS OF THE ASSOCIATION FOR COMPUTATIONAL LINGUISTICS
(2021)
Proceedings Paper
Computer Science, Information Systems
Dawei Cheng et al.
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
(2020)
Proceedings Paper
Computer Science, Information Systems
Zuoxi Yang
PROCEEDINGS OF THE 43RD INTERNATIONAL ACM SIGIR CONFERENCE ON RESEARCH AND DEVELOPMENT IN INFORMATION RETRIEVAL (SIGIR '20)
(2020)
Article
Acoustics
Kehai Chen et al.
IEEE-ACM TRANSACTIONS ON AUDIO SPEECH AND LANGUAGE PROCESSING
(2018)
Article
Computer Science, Artificial Intelligence
Marco Rospocher et al.
JOURNAL OF WEB SEMANTICS
(2016)
Article
Computer Science, Artificial Intelligence
Goran Glavas et al.
NATURAL LANGUAGE ENGINEERING
(2015)
Article
Computer Science, Artificial Intelligence
Goran Glavas et al.
EXPERT SYSTEMS WITH APPLICATIONS
(2014)
