Question: How is the author's work different from that of Das et al.?
Evidence: Extensive work exists on knowledge graph embeddings dating back to Nickel, Tresp, and Kriegel ( BIBREF1 ) who first proposed Rescal based on a matrix factorization approach. Bordes et al. ( BIBREF2 ) advanced this line of work by proposing the first translational model TransE which seeks to relate the head and tail entity embeddings by modeling the relation as a translational vector. This culminated in a long series of new knowledge graph embeddings all based on the translational principle with various refinements BIBREF9 , BIBREF10 , BIBREF4 , BIBREF3 , BIBREF5 , BIBREF11 , BIBREF12 , BIBREF13 , BIBREF14 , BIBREF15 , BIBREF16 . Some recently proposed models like ManiFoldE BIBREF17 attempt to learn knowledge graph embeddings as a manifold while embeddings like HolE BIBREF1 derive inspiration from associative memories. Furthermore, with the success of neural models, models based on convolutional neural networks have been proposed like BIBREF6 , BIBREF18 to learn knowledge graph embeddings. Other models in this class of models include ConvKB BIBREF19 and KBGAN BIBREF20 . There has been some work on incorporating additional information like entity types, relation paths etc. to learn knowledge graph representations. Palumbo et al. ( BIBREF21 ) use node2vec to learn embeddings of entities and items in a knowledge graph. A notable class of methods called “path-ranking” based models directly model paths between entities as features. Examples include Path Ranking Algorithm (PRA) BIBREF22 , PTransE BIBREF10 and models based on recurrent neural networks BIBREF23 , BIBREF24 . Besides, Das et al. ( BIBREF25 ) propose a reinforcement learning method that addresses practical task of answering questions where the relation is known, but only one entity. Hartford et al. ( BIBREF26 ) model interactions across two or more sets of objects using a parameter-sharing scheme. While most of the above models except for the recurrent-neural net abased models above are shallow our model Dolores differs from all of these works and especially that of Palumbo et al. ( BIBREF21 ) in that we learn deep contextualized knowledge graph representations of entities and relations using a deep neural sequential model. The work that is closest to our work is that of Das et al. ( BIBREF24 ) who directly use an RNN-based architecture to model paths to predict missing links. We distinguish our work from this in the following key ways: (a) First, unlike Das et al. ( BIBREF24 ), our focus is not on path reasoning but on learning rich knowledge graph embeddings useful for a variety of predictive tasks. Moreover while Das et al. ( BIBREF24 ) need to use paths generated from PRA that typically correlate with relations, our method has no such restriction and only uses paths generated by generic random walks greatly enhancing the scalability of our method. In fact, we incorporate Dolores embeddings to improve the performance of the model proposed by Das et al. ( BIBREF24 ). (b) Second, and most importantly we learn knowledge graph embeddings at multiple layers each potentially capturing different levels of abstraction. (c) Finally, while we are inspired by the work of Peters et al. ( BIBREF8 ) in learning deep word representations, we build on their ideas by drawing connections between knowledge graphs and language modeling BIBREF8 . In particular, we propose methods to use random walks over knowledge graphs in conjunction with the machinery of deep neural language modeling to learn powerful deep contextualized knowledge graph embeddings that improve the state of the art on various knowledge graph tasks.
We distinguish our work from this in the following key ways: (a) First, unlike Das et al., our focus is not on path reasoning but on learning rich knowledge graph embeddings useful for a variety of predictive tasks. Moreover while Das et al. need to use paths generated from PRA that typically correlate with relations, our method has no such restriction and only uses paths generated by generic random walks greatly enhancing the scalability of our method. In fact, we incorporate DOLORES embeddings to improve the performance of the model proposed by Das et al. (b) Second, and most importantly we learn knowledge graph embeddings at multiple layers each potentially capturing different levels of abstraction. (c) Finally, while we are inspired by the work of Peters et al. in learning deep word representations, we build on their ideas by drawing connections between knowledge graphs and language modeling.
Answer: (a) The author's focus is not on path reasoning, but on learning rich knowledge graph embedding useful for various predictive tasks. In addition, Das et al. 's approach does not have this limitation, as they need to use paths that PRA generates that are usually relational. (b) The author learns multiple layers of knowledge graph embedding, each of which may capture different levels of abstraction. (c) Although the authors are inspired by Peters et al in learning deep word representations, the authors build their ideas by making a connection between knowledge graphs and language modeling