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2021 | OriginalPaper | Buchkapitel

Pattern-Aware and Noise-Resilient Embedding Models

verfasst von : Mojtaba Nayyeri, Sahar Vahdati, Emanuel Sallinger, Mirza Mohtashim Alam, Hamed Shariat Yazdi, Jens Lehmann

Erschienen in: Advances in Information Retrieval

Verlag: Springer International Publishing

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Knowledge Graph Embeddings (KGE) have become an important area of Information Retrieval (IR), in particular as they provide one of the state-of-the-art methods for Link Prediction. Recent work in the area of KGEs has shown the importance of relational patterns, i.e., logical formulas, to improve the learning process of KGE models significantly. In separate work, the role of noise in many knowledge discovery and IR settings has been studied, including the KGE setting. So far, very few papers have investigated the KGE setting considering both relational patterns and noise. Not considering both together can lead to problems in the performance of KGE models. We investigate the effect of noise in the presence of patterns. We show that by introducing a new loss function that is both pattern-aware and noise-resilient, significant performance issues can be solved. The proposed loss function is model-independent which could be applied in combination with different models. We provide an experimental evaluation both on synthetic and real-world cases.

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Titel: Pattern-Aware and Noise-Resilient Embedding Models
verfasst von: Mojtaba Nayyeri
Sahar Vahdati
Emanuel Sallinger
Mirza Mohtashim Alam
Hamed Shariat Yazdi
Jens Lehmann
Verlag: Springer International Publishing
Buch: Advances in Information Retrieval
Print ISBN: 978-3-030-72112-1

Electronic ISBN: 978-3-030-72113-8

Copyright-Jahr: 2021
DOI: https://doi.org/10.1007/978-3-030-72113-8_32

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