Han Yu;Hongming Cai;Shengtung Tsai;Mengyao Li;Pan Hu;Jiaoyan Chen;Bingqing Shen
{"title":"利用实体信息对事件知识图进行鲁棒预测","authors":"Han Yu;Hongming Cai;Shengtung Tsai;Mengyao Li;Pan Hu;Jiaoyan Chen;Bingqing Shen","doi":"10.1109/TETC.2025.3534243","DOIUrl":null,"url":null,"abstract":"Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterprise systems, IoT systems, etc. Sequence-based and graph-based learning models have been applied to this task. However, when learning data is limited, especially in a multiple-participant-involved enterprise environment, the performance of such models falls short of expectations as they heavily rely on large-scale training data. To take full advantage of given data, in this article we propose a new type of knowledge graph (KG) that models not just events but also entities participating in the events, and we design a collaborative event prediction model exploiting such KGs. Our model identifies semantically similar vertices as collaborators to resolve unknown events, applies gated graph neural networks to extract event-wise sequential features, and exploits a heterogeneous attention network to cope with entity-wise influence in event sequences. To verify the effectiveness of our approach, we designed multiple-choice narrative cloze tasks with inadequate knowledge. Our experimental evaluation with three datasets generated from well-known corpora shows our method can successfully defend against such incompleteness of data and outperforms the state-of-the-art approaches for event prediction.","PeriodicalId":13156,"journal":{"name":"IEEE Transactions on Emerging Topics in Computing","volume":"13 3","pages":"890-901"},"PeriodicalIF":5.4000,"publicationDate":"2025-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Exploiting Entity Information for Robust Prediction Over Event Knowledge Graphs\",\"authors\":\"Han Yu;Hongming Cai;Shengtung Tsai;Mengyao Li;Pan Hu;Jiaoyan Chen;Bingqing Shen\",\"doi\":\"10.1109/TETC.2025.3534243\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterprise systems, IoT systems, etc. Sequence-based and graph-based learning models have been applied to this task. However, when learning data is limited, especially in a multiple-participant-involved enterprise environment, the performance of such models falls short of expectations as they heavily rely on large-scale training data. To take full advantage of given data, in this article we propose a new type of knowledge graph (KG) that models not just events but also entities participating in the events, and we design a collaborative event prediction model exploiting such KGs. Our model identifies semantically similar vertices as collaborators to resolve unknown events, applies gated graph neural networks to extract event-wise sequential features, and exploits a heterogeneous attention network to cope with entity-wise influence in event sequences. To verify the effectiveness of our approach, we designed multiple-choice narrative cloze tasks with inadequate knowledge. Our experimental evaluation with three datasets generated from well-known corpora shows our method can successfully defend against such incompleteness of data and outperforms the state-of-the-art approaches for event prediction.\",\"PeriodicalId\":13156,\"journal\":{\"name\":\"IEEE Transactions on Emerging Topics in Computing\",\"volume\":\"13 3\",\"pages\":\"890-901\"},\"PeriodicalIF\":5.4000,\"publicationDate\":\"2025-01-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Emerging Topics in Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10869308/\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Emerging Topics in Computing","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10869308/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Exploiting Entity Information for Robust Prediction Over Event Knowledge Graphs
Script event prediction is the task of predicting the subsequent event given a sequence of events that already took place. It benefits task planning and process scheduling for event-centric systems including enterprise systems, IoT systems, etc. Sequence-based and graph-based learning models have been applied to this task. However, when learning data is limited, especially in a multiple-participant-involved enterprise environment, the performance of such models falls short of expectations as they heavily rely on large-scale training data. To take full advantage of given data, in this article we propose a new type of knowledge graph (KG) that models not just events but also entities participating in the events, and we design a collaborative event prediction model exploiting such KGs. Our model identifies semantically similar vertices as collaborators to resolve unknown events, applies gated graph neural networks to extract event-wise sequential features, and exploits a heterogeneous attention network to cope with entity-wise influence in event sequences. To verify the effectiveness of our approach, we designed multiple-choice narrative cloze tasks with inadequate knowledge. Our experimental evaluation with three datasets generated from well-known corpora shows our method can successfully defend against such incompleteness of data and outperforms the state-of-the-art approaches for event prediction.
期刊介绍:
IEEE Transactions on Emerging Topics in Computing publishes papers on emerging aspects of computer science, computing technology, and computing applications not currently covered by other IEEE Computer Society Transactions. Some examples of emerging topics in computing include: IT for Green, Synthetic and organic computing structures and systems, Advanced analytics, Social/occupational computing, Location-based/client computer systems, Morphic computer design, Electronic game systems, & Health-care IT.