Report
Compositional Vector Space Models for Knowledge Base Completion
| العنوان: | Compositional Vector Space Models for Knowledge Base Completion |
|---|---|
| المؤلفون: | Neelakantan, Arvind, Roth, Benjamin, McCallum, Andrew |
| سنة النشر: | 2015 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Statistics - Machine Learning |
| الوصف: | Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%. Comment: The 53rd Annual Meeting of the Association for Computational Linguistics and The 7th International Joint Conference of the Asian Federation of Natural Language Processing, 2015 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1504.06662 |
| رقم الانضمام: | edsarx.1504.06662 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |