Discriminative Gaifman Models
We present discriminative Gaifman models, a novel family of relational machine learning models. Gaifman models learn feature representations bottom up from representations of locally connected and bounded-size regions of knowledge bases (KBs). Considering local and bounded-size neighborhoods of knowledge bases renders logical inference and learning tractable, mitigates the problem of overfitting, and facilitates weight sharing. Gaifman models sample neighborhoods of knowledge bases so as to make the learned relational models more robust to missing objects and relations which is a common situation in open-world KBs. We present the core ideas of Gaifman models and apply them to large-scale relational learning problems. We also discuss the ways in which Gaifman models relate to some existing relational machine learning approaches.
The paper present Gaifman models that combined Gaifman’s locality theorem with relational machine learning models. It perform learning and inference within and across locally connected regions of relational structures.
The research problem of this paper is interesting and meaningful. The paper present a novel family of relational machine learning models. However, it would be better if some flaws of the paper can be modified. 1. In line 20, the paper write "We aim to advance the .. models which learn efficient and effective cs". What are the shortcomings of existing methods in terms of efficiency and representation? 2.In line 20, the paper write "...learn effective representations from these local neighborhoods". What is " effective representations"? Could you give an example? 3.In line 143, the paper give a theorem 2. Could you give the proof of the theorem? 4. In line 20, the paper write " We obtained ... categorical cross-entropy as loss function ".Why use it? Could you give a reasonable analysis or advantage analysis? 5. There are similarities with PTranE(Modeling Relation Paths for Representation Learning of Knowledge Bases). Could you compare with this method? In addition, TransE and TransR are typical Knowledge Graph embedding model. But ,at present, the best model of Knowledge Graph Embedding is TranSparse (Knowledge Graph Completion with Adaptive Sparse Transfer Matrix). Could you compare with this method in the experiment to illustrate effective representations?