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Multi-feature fusion stock prediction based on knowledge graph

ELECTRONIC LIBRARY 2024年第3期42卷 455-482页

作者： Liu, Zhenghao Qian, Yuxing Lv, Wenlong Fang, Yanbin Liu, Shenglan Wuhan Univ Sch Informat Management Wuhan Peoples R China City Univ Hongkong Dept Informat Syst Hongkong Peoples R China Wuhan Univ Big Data Inst Wuhan Peoples R China Nanjing Univ Sch Journalism & Commun Nanjing Peoples R China

PurposeStock prices are subject to the influence of news and social media, and a discernible co-movement pattern exists among multiple stocks. Using a knowledge graph to represent news semantics and establish connections between stocks is deemed essential and ***/methodology/approachThis study presents a knowledge-driven framework for predicting stock prices. The framework integrates relevant stocks with the semantic and emotional characteristics of textual data. The authors construct a stock knowledge graph (SKG) to extract pertinent stock information and use a knowledge graph representation model to capture both the relevant stock features and the semantic features of news articles. Additionally, the authors consider the emotional characteristics of news and investor comments, drawing insights from behavioral finance theory. The authors examined the effectiveness of these features using the combined deep learning model CNN+LSTM+*** results demonstrate that the knowledge-driven combined feature model exhibits significantly improved predictive accuracy compared to single-feature ***/valueThe study highlights the value of the SKG in uncovering potential correlations among stocks. Moreover, the knowledge-driven multi-feature fusion stock forecasting model enhances the prediction of stock trends for well-known enterprises, providing valuable guidance for investor decision-making.

关键词： Stock prediction knowledge graph Relevant stocks knowledge graph embedding Sentiment analysis Muti-feature fusion

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Veni, Vidi, Vici: Solving the Myriad of Challenges before knowledge graph Learning 18

Veni, Vidi, Vici: Solving the Myriad of Challenges before Kn...

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18th IEEE International Conference on Semantic Computing (ICSC)

作者： Sardina, Jeffrey Costabello, Luca Gueret, Christophe Trinity Coll Dublin Sch Comp Sci & Stat Dublin Ireland Accenture Labs Dublin Ireland Accenture BioInnovat Labs Dublin Ireland

ISBN: (纸本)9798350385359

knowledge graphs (KGs) have become increasingly common for representing large-scale linked data. However, their immense size has required graph learning systems to assist humans in analysis, interpretation, and pattern detection. While there have been promising results for researcher- and clinician-empowerment through a variety of KG learning systems, we identify four key deficiencies in state-of-the-art graph learning that simultaneously limit KG learning performance and diminish the ability of humans to interface optimally with these learning systems. These deficiencies are: 1) lack of expert knowledge integration, 2) instability to node degree extremity in the KG, 3) lack of consideration for uncertainty and relevance while learning, and 4) lack of explainability. Furthermore, we characterise state-of-the-art attempts to solve each of these problems and note that each attempt has largely been isolated from attempts to solve the other problems. Through a formalisation of these problems and a review of the literature that addresses them, we adopt the position that not only are deficiencies in these four key areas holding back human-KG empowerment, but that the divide-and-conquer approach to solving these problems as individual units rather than a whole is a significant barrier to the interface between humans and KG learning systems. We propose that it is only through integrated, holistic solutions to the limitations of KG learning systems that human and KG learning co-empowerment will be efficiently affected. We finally present our "Veni, Vidi, Vici" framework that sets a roadmap for effectively and efficiently shifting to a holistic co-empowerment model in both the KG learning and the broader machine learning domain.

关键词： knowledge graphs knowledge graph embedding Relational Learning Neuro-Symbolic Learning GNNs

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AsyncET: Asynchronous Representation Learning for knowledge graph Entity Typing 24

AsyncET: Asynchronous Representation Learning for Knowledge ...

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30th ACM SIGKDD Conference on knowledge Discovery and Data Mining

作者： Wang, Yun-Cheng Ge, Xiou Wang, Bin Kuo, C. -C. Jay Univ Southern Calif Los Angeles CA 90007 USA Natl Univ Singapore Singapore Singapore

ISBN: (纸本)9798400704901

knowledge graph entity typing (KGET) aims to predict the missing entity types in knowledge graphs (KG). The relationship between entities and their corresponding types is often expressed using a single relation, hasType. However, hasType has a limited capability for modeling diverse entity-type relationships in the embedding space. In this paper, we first introduce multiple auxiliary relations to model the complex entity-type relationship. We propose an efficient and robust algorithm to group similar entity types together and assign a unique auxiliary relation to each group. Then, with the auxiliary relations, we propose an Asynchronous representation learning framework for KGET, named AsyncET, where entity and type embeddings are updated alternatively. Consequently, the quality of entity embeddings is gradually improved during training by infusing type information. In addition, entity types with different granularities and semantics can be properly modeled in the embedding space. Experimental results show that AsyncET can substantially improve the performance of embedding-based methods on the KGET task and has a significant advantage over state-of-the-art neural network-based methods in terms of model sizes and inference time.

关键词： knowledge graph embedding knowledge graph Entity Typing Representation Learning Taxonomy

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embedding knowledge graphs in Function Spaces 24

Embedding Knowledge Graphs in Function Spaces

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33rd ACM International Conference on Information and knowledge Management (CIKM)

作者： Teyou, Louis Mozart Kamdem Demir, Caglar Ngomo, Axel-Cyrille Ngonga Paderborn Univ Elect Informat & Math Paderborn North Rhine Wes Germany

ISBN: (纸本)9798400704369

We introduce a novel embedding method diverging from conventional approaches by operating within function spaces of finite dimension rather than finite vector space, thus departing significantly from standard knowledge graph embedding techniques. Initially employing polynomial functions to compute embeddings, we progress to more intricate representations using neural networks with varying layer complexities. We argue that employing functions for embedding computation enhances expressiveness and allows for more degrees of freedom, enabling operations such as composition, derivatives and primitive of entities representation. Additionally, we meticulously outline the step-by-step construction of our approach and provide code for reproducibility, thereby facilitating further exploration and application in the field.

关键词： Function space knowledge graph knowledge graph embedding Polynomial function Neural Networks

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KEEN: knowledge graph-Enabled Governance System for Biological Assets 1

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17th International Conference on knowledge Science, Engineering and Management (KSEM)

作者： Fang, Zhengkang Gai, Keke Yu, Jing Wei, Yihang Wei, Zhentao Chan, Weilin Beijing Inst Technol Sch Cyberspace Sci & Technol Beijing Peoples R China Beijing Muguo Technol Co Ltd Beijing Peoples R China Chinese Acad Sci Inst Informat Engn Beijing Peoples R China

ISBN: (数字)9789819754984

ISBN: (纸本)9789819754977;9789819754984

With the development of the biological industry and the need for improved productivity in agriculture and animal husbandry, there are higher demands for the governance of biological assets in the biological assets governance system. In order to harness existing knowledge and enhance the effectiveness and scalability of biological asset supervision, this research proposes a knowledge graph-Enabled govErNance system for biological assets (KEEN). The system leverages textual data and employs techniques such as entity recognition, relation extraction, and knowledge graph embedding to automatically generate a knowledge graph. This knowledge graph establishes relationships between biological behaviors and target identification, thereby expanding the capabilities of behavior recognition models to perform a wider range of biological asset supervision tasks. By enhancing the scalability of biological asset identification models, better decision-making and management can be achieved. Our evaluations demonstrate that the proposed method exhibits superior performance in terms of accuracy, scalability, and maintenance in the supervision of biological assets.

关键词： knowledge graph knowledge graph embedding Biological assets

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Collective Entity Alignment for knowledge Fusion of Power Grid Dispatching knowledge graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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Diversity matters: Robustness of bias measurements in Wikidata 23

Diversity matters: Robustness of bias measurements in Wikida...

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15th ACM Web Science Conference (WebSci)

作者： Das, Paramita Karnam, Sai Keerthana Panda, Anirban Guda, Bhanu Prakash Reddy Sarkar, Soumya Mukherjee, Animesh IIT Kharagpur Dept CSE Kharagpur W Bengal India Carnegie Mellon Univ Pittsburgh PA 15213 USA Microsoft IDC Hyderabad Telangana India

ISBN: (纸本)9798400700897

With the widespread use of knowledge graphs (KG) in various automated AI systems and applications, it is very important to ensure that information retrieval algorithms leveraging them are free from societal biases. Previous works have depicted biases that persist in KGs, as well as employed several metrics for measuring the biases. However, such studies lack in the systematic exploration of the sensitivity of the bias measurements, through varying sources of data, or the embedding algorithms used. To address this research gap, in this work, we present a holistic analysis of bias measurement on the knowledge graph. First, we attempt to reveal data biases that surface in Wikidata for thirteen different demographics selected from seven continents. Next, we attempt to unfold the variance in the detection of biases by two different knowledge graph embedding algorithms - TransE and ComplEx. We conduct our extensive experiments on a large number of occupations sampled from the thirteen demographics with respect to the sensitive attribute, i.e., gender. Our results show that the inherent data bias that persists in KG can be altered by specific algorithm bias as incorporated by KG embedding learning algorithms. Further, we show that the choice of the state-of-the-art KG embedding algorithm has a strong impact on the ranking of biased occupations irrespective of gender. In particular, we find that the embedding algorithm ComplEx is more robust to the choice of demographics compared to TransE. Subsequently, we observe that the similarity of the biased occupations across demographics is minimal which reflects the socio-cultural differences around the globe. This is often overlooked by most of the coarse-grained approaches working at the aggregate level. We believe that this full-scale audit of the bias measurement pipeline will raise awareness among the community while deriving insights related to design choices of data and algorithms both and refrain itself from the popular do

关键词： societal biases knowledge graphs Wikidata data bias algorithm bias knowledge graph embedding

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Leveraging the Power of Echo State Network for Enhanced Temporal knowledge graph Reasoning

Leveraging the Power of Echo State Network for Enhanced Temp...

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International Joint Conference on Neural Networks (IJCNN)

作者： Sun, Yi Wang, Zhe Wang, Kewen Griffith Univ Sch Informat & Commun Technol Brisbane Qld Australia

ISBN: (纸本)9798350359329;9798350359312

Temporal knowledge graphs (TKG) reasoning has emerged as a powerful methodology for prediction in various domains. Unlike traditional knowledge graphs(KG), TKGs introduce a critical time dimension to the graph structure. Existing TKG reasoning methods expose two important limitations. The first is they are primarily designed to predict the immediate next time step. When applied to multi-step predictions, this leads to an iterative process that compromises prediction efficiency. The second limitation is their performance often depends on short-term memory and overlook long-term global information. To overcome these limitations, we introduce the Echo State Temporal knowledge graph Network (ESTN), an innovative approach leveraging the Echo State Networks (ESN) for TKG reasoning. Specifically, the ESTN contains a graph Embedder (GE) which integrates both short-term and long-term information within the TKG, enhancing the depth and accuracy of the data representation in graph latent space (embeddings). A Time Module (TM) is designed to facilitate direct prediction over multiple time steps, significantly improved the prediction efficiency. Extensive experiments demonstrate that our method not only show robust performance, but also offers an alternative approach to TKG reasoning, emphasizing how the distinct features of ESN can be effectively harnessed in TKG reasoning tasks.

关键词： Temporal knowledge graph Echo State Network knowledge graph embedding

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embedding and Predicting Software Security Entity Relationships: A knowledge graph Based Approach 26th

Embedding and Predicting Software Security Entity Relationsh...

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26th International Conference on Neural Information Processing (ICONIP) of the Asia-Pacific-Neural-Network-Society (APNNS)

作者： Xiao, Hongbo Xing, Zhenchang Li, Xiaohong Guo, Hao Tianjin Univ Coll Intelligence & Comp Tianjin Key Lab Adv Networking TANK Tianjin 300350 Peoples R China Australian Natl Univ Res Sch Comp Sci Canberra ACT 2600 Australia

ISBN: (纸本)9783030367183;9783030367176

Software security knowledge involves heterogeneous security concepts (e.g., software weaknesses and attack patterns) and security instances (e.g., the vulnerabilities of a particular software product), which can be regarded as software security entities. Among software security entities, there are many within-type relationships as well as many across-type relationships. Predicting software security entity relationships helps to enrich software security knowledge (e.g., finding missing relationships among existing entities). Unfortunately, software security entities are currently documented in separate databases, such as Common Vulnerabilities and Exposures (CVE), Common Weakness Enumeration (CWE) and Common Attack Pattern Enumeration and Classification (CAPEC). This hyper-document representation cannot support effective reasoning of software entity relationships. In this paper, we propose to consolidate heterogeneous software security concepts and instances from separate databases into a coherent knowledge graph. We develop a knowledge graph embedding method which embeds the symbolic relational and descriptive information of software security entities into a continuous vector space. The resulting entity and relationship embeddings are predictive for software security entity relationships. Based on the Open World Assumption, we conduct extensive experiments to evaluate the effectiveness of our knowledge graph based approach for predicting various within-type and across-type relationships of software security entities.

关键词： Software security entity relationship knowledge graph embedding Link prediction

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A knowledge graph Question Answering Approach Based on graph Attention Networks and Relational Path Encoding 20th

A Knowledge Graph Question Answering Approach Based on Graph...

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20th International Conference on Intelligent Computing (ICIC)

作者： Cao, Shuxin Zhu, Xiaoxu Li, Peifeng Soochow Univ Suzhou 215026 Peoples R China

ISBN: (纸本)9789819756681;9789819756698

Most of the previous knowledge graph question answering (KGQA) methods focus on question parsing and ignore the knowledge graph information. In order to improve the utilization of knowledge graph structure information, based on the good performance of graph networks in the field of knowledge graph embedding, this paper proposes to utilize relational graph attention networks to pre-train the knowledge graph Q&A database. Entity and relation representations are obtained by fusing multi-hop information and the results are used to initialize the vector representation of nodes and edges in the graph. Inference is then performed using a multi-hop Q&A network based on relational paths. Since most current inference models do not consider path-level information, LSTM is introduced to encode paths in the model fusion process. In order to improve the reliability of inference, a multilayer attention mechanism is introduced to calculate the similarity between the paths and the original problem, so as to obtain the weight coefficients of the paths, which are fused into the relation and entity scores. Experiments demonstrate that the optimized Q&A inference method outperforms the original and baseline models on the MetaQA, WebQSP and CWQ datasets.

关键词： knowledge graph Q&A Second Keyword knowledge graph embedding graphical attention network

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