Partial Knowledge In Embeddings
This work addresses the challenge of encoding incomplete information in knowledge representation systems, which is incremental as it builds on existing embedding techniques.
The paper tackles the problem of representing partial knowledge in embeddings, a key component for successful knowledge systems, by introducing ensembles and aggregate embeddings to address representational expressiveness tradeoffs.
Representing domain knowledge is crucial for any task. There has been a wide range of techniques developed to represent this knowledge, from older logic based approaches to the more recent deep learning based techniques (i.e. embeddings). In this paper, we discuss some of these methods, focusing on the representational expressiveness tradeoffs that are often made. In particular, we focus on the the ability of various techniques to encode `partial knowledge' - a key component of successful knowledge systems. We introduce and describe the concepts of `ensembles of embeddings' and `aggregate embeddings' and demonstrate how they allow for partial knowledge.