We are very pleased to announce that our group got a paper accepted for presentation at ECIR 2021. The ECIR conference is the premier European forum for the presentation of new research results in the broadly conceived area of Information Retrieval (IR), and has a strong focus on the active participation of early-career researchers. The General Chairs of ECIR 2021 invite researchers, academics, students, and industry leaders working in the field to join us online for a rich program featuring full-paper and poster presentations, system demonstrations, tutorials, workshops, an industry-oriented event, and great social events.
Here is the abstract and the link to the paper:
Pattern-Aware and Noise-Resilient Embedding Models
By
Mojtaba Nayyeri,
Sahar Vahdati,
Emanuel Sallinger,
Mirza Mohtashim Alam,
Hamed Shariat Yazdi and
Jens Lehmann.
Abstract
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.
