A Bilinear Ranking SVM for Knowledge Based Relation Prediction and Classification
| Autor: | Shengkang Yu, Xueyi Zhao, Fei Wu, Xi Li, Xuelong Li, Jingdong Wang, Yueting Zhuang, Zhongfei Zhang |
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| Rok vydání: | 2019 |
| Předmět: |
Descriptive knowledge
Information Systems and Management Knowledge representation and reasoning Relation (database) business.industry Computer science 02 engineering and technology 010501 environmental sciences Machine learning computer.software_genre 01 natural sciences Ranking (information retrieval) Support vector machine Knowledge-based systems Ranking SVM 0202 electrical engineering electronic engineering information engineering Question answering 020201 artificial intelligence & image processing Artificial intelligence business computer 0105 earth and related environmental sciences Information Systems |
| Zdroj: | IEEE Transactions on Big Data. 5:588-600 |
| ISSN: | 2372-2096 |
| Popis: | As an important and challenging problem, knowledge representation and inference are typically carried out in a knowledge embedding framework over a multi-relational knowledge graph, and thus have a wide range of applications such as semantic retrieval and question answering. In this paper, we propose a bilinear learning framework which performs cross-entity knowledge relation analysis in the continuous vector space (derived from knowledge embedding). In the framework, we effectively model the intrinsic correlations among different types of knowledge relations within a max-margin multi-relational ranking scheme, which jointly optimizes the tasks of entity embedding and cross-entity relation prediction in terms of multi-relational structures of the knowledge graph. Specifically, we devise a bilinear scoring function that aims to evaluate the confidence degree of semantic relation prediction for entity pairs through a multi-relational learning-to-rank pipeline. In essence, the pipeline formulates the problem of relation prediction for entity pairs as that of learning relation-specific ranking functions by max-margin optimization. Experimental results demonstrate the effectiveness of the proposed framework on two common benchmark datasets. |
| Databáze: | OpenAIRE |
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