ABSTRACT
Learning the dependency relations among entities and the hierarchy formed by these relations by mapping entities into some order embedding space can effectively enable several important applications, including knowledge base completion and prerequisite relations prediction. Nevertheless, it is very challenging to learn a good order embedding due to the existence of partial ordering and missing relations in the observed data. Moreover, most application scenarios do not provide non-trivial negative dependency relation instances. We therefore propose a framework that performs dependency relation prediction by exploring both rich semantic and hierarchical structure information in the data. In particular, we propose several negative sampling strategies based on graph-specific centrality properties, which supplement the positive dependency relations with appropriate negative samples to effectively learn order embeddings. This research not only addresses the needs of automatically recovering missing dependency relations, but also unravels dependencies among entities using several real-world datasets, such as course dependency hierarchy involving course prerequisite relations, job hierarchy in organizations, and paper citation hierarchy. Extensive experiments are conducted on both synthetic and real-world datasets to demonstrate the prediction accuracy as well as to gain insights using the learned order embedding.
- Fareedah ALSaad, Assma Boughoula, Chase Geigle, Hari Sundaram, and ChengXiang Zhai. 2018. Mining MOOC Lecture Transcripts to Construct Concept Dependency Graphs. In EDM.Google Scholar
- Ben Athiwaratkun and Andrew Gordon Wilson. 2018. Hierarchical Density Order Embeddings. In ICLR.Google Scholar
- Antoine Bordes, Nicolas Usunier, Alberto Garcia-Duran, Jason Weston, and Oksana Yakhnenko. 2013. Translating embeddings for modeling multi-relational data. In NIPS. Google ScholarDigital Library
- Hongyun Cai, Vincent W. Zheng, and Kevin Chang. 2018. A comprehensive survey of graph embedding: problems, techniques and applications. TKDE (2018).Google Scholar
- Yetian Chen, José P González-Brenes, and Jin Tian. 2016. Joint Discovery of Skill Prerequisite Graphs and Student Models. In EDM.Google Scholar
- Xin Luna Dong, Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Kevin Murphy, Shaohua Sun, and Wei Zhang. 2014. From data fusion to knowledge fusion. Proceedings of the VLDB Endowment 7, 10 (2014), 881--892. Google ScholarDigital Library
- Alexander R. Fabbri, Irene Li, Prawat Trairatvorakul, Yijiao He, Wei Tai Ting, Robert Tung, Caitlin Westerfield, and Dragomir R. Radev. 2018. TutorialBank: A Manually-Collected Corpus for Prerequisite Chains, Survey Extraction and Resource Recommendation. In ACL.Google Scholar
- Jonathan Gordon, Linhong Zhu, Aram Galstyan, Prem Natarajan, and Gully Burns. 2016. Modeling concept dependencies in a scientific corpus. In ACL.Google Scholar
- Chen Liang, Jianbo Ye, Shuting Wang, Bart Pursel, and C. Lee Giles. 2018. Investigating active learning for concept prerequisite learning. EAAI (2018).Google Scholar
- Chen Liang, Jianbo Ye, ZhaohuiWu, Bart Pursel, and C. Lee Giles. 2017. Recovering Concept Prerequisite Relations from University Course Dependencies. In AAAI. Google ScholarDigital Library
- Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning entity and relation embeddings for knowledge graph completion. In AAAI. Google ScholarDigital Library
- Hanxiao Liu, Wanli Ma, Yiming Yang, and Jaime Carbonell. 2016. Learning concept graphs from online educational data. JAIR 55 (2016). Google ScholarDigital Library
- Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S. Corrado, and Jeff Dean. 2013. Distributed representations of words and phrases and their compositionality. In NIPS. Google ScholarDigital Library
- Jack Minker. 1982. On indefinite databases and the closed world assumption. In International Conference on Automated Deduction. Google ScholarDigital Library
- Maximilian Nickel, Kevin Murphy, Volker Tresp, and Evgeniy Gabrilovich. 2016. A review of relational machine learning for knowledge graphs. Proc. IEEE 104, 1 (2016), 11--33.Google ScholarCross Ref
- Maximilian Nickel, Lorenzo Rosasco, Tomaso A. Poggio, and others. 2016. Holographic Embeddings of Knowledge Graphs. In AAAI. Google ScholarDigital Library
- Maximilian Nickel, Volker Tresp, and Hans-Peter Kriegel. 2011. A Three-Way Model for Collective Learning on Multi-Relational Data. In ICML. Google ScholarDigital Library
- Richard Jayadi Oentaryo, Ee-Peng Lim, Xavier Jayaraj Siddarth Ashok, Philips Kokoh Prasetyo, Koon Han Ong, and Zi Quan Lau. 2018. Talent Flow Analytics in Online Professional Network. Data Science and Engineering 3 (2018).Google Scholar
- Liangming Pan, Chengjiang Li, Juanzi Li, and Jie Tang. 2017. Prerequisite relation learning for concepts in moocs. In ACL.Google Scholar
- Liangming Pan, Xiaochen Wang, Chengjiang Li, Juanzi Li, and Jie Tang. 2017. Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation. In IJCNLP.Google Scholar
- Jeffrey Pennington, Richard Socher, and Christopher D. Manning. 2014. GloVe: Global Vectors for Word Representation. In EMNLP.Google Scholar
- Bryan Perozzi, Rami Al-Rfou, and Steven Skiena. 2014. DeepWalk: Online Learning of Social Representations. In SIGKDD. Google ScholarDigital Library
- Jian Tang, Meng Qu, Mingzhe Wang, Ming Zhang, Jun Yan, and Qiaozhu Mei. 2015. LINE: Large-scale Information Network Embedding. In WWW. Google ScholarDigital Library
- Ivan Vendrov, Ryan Kiros, Sanja Fidler, and Raquel Urtasun. 2016. Orderembeddings of images and language. In ICLR.Google Scholar
- Luke Vilnis, Xiang Li, Shikhar Murty, and Andrew McCallum. 2018. Probabilistic Embedding of Knowledge Graphs with Box Lattice Measures. In ACL.Google Scholar
- Luke Vilnis and Andrew McCallum. 2015. Word representations via gaussian embedding. In ICLR.Google Scholar
- Quan Wang, Zhendong Mao, Bin Wang, and Li Guo. 2017. Knowledge Graph Embedding: A Survey of Approaches and Applications. TKDE 29, 12 (2017), 2724--2743.Google ScholarCross Ref
- Hsiang-Fu Yu, Mikhail Bilenko, and Chih-Jen Lin. 2017. Selection of negative samples for one-class matrix factorization. In SDM.Google Scholar
Index Terms
- One-Class Order Embedding for Dependency Relation Prediction
Recommendations
The clone relation of a binary relation
In a recent paper, De Baets etal. introduced the clone relation of a strict order relation. Two elements of a poset are said to be a pair of clones (or to be clones) if every other element that is greater (resp. smaller) than one of them is also greater ...
Contextual relation embedding and interpretable triplet capsule for inductive relation prediction
AbstractRelation prediction is a task for knowledge graph completion which is targeted at predicting missing relationships between entities. Most previous works focus on learning latent representations of entities and relations, resulting in ...
RHINE: Relation Structure-Aware Heterogeneous Information Network Embedding
Heterogeneous information network (HIN) embedding aims to learn the low-dimensional representations of nodes while preserving structures and semantics in HINs. Although most existing methods consider heterogeneous relations and achieve promising ...
Comments