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Knowledge Graph Embedding via Dynamic Mapping Matrix
TLDR
A more fine-grained model named TransD, which is an improvement of TransR/CTransR, which not only considers the diversity of relations, but also entities, which makes it can be applied on large scale graphs.
Extracting Relational Facts by an End-to-End Neural Model with Copy Mechanism
TLDR
This paper proposes an end-to-end model based on sequence- to-sequence learning with copy mechanism, which can jointly extract relational facts from sentences of any of these classes, including Normal, EntityPairOverlap and SingleEntiyOverlap.
Learning to Represent Knowledge Graphs with Gaussian Embedding
TLDR
The experimental results demonstrate that the KG2E method can effectively model the (un)certainties of entities and relations in a KG, and it significantly outperforms state-of-the-art methods (including TransH and TransR).
Distant Supervision for Relation Extraction with Sentence-Level Attention and Entity Descriptions
TLDR
This paper proposes a sentence-level attention model to select the valid instances, which makes full use of the supervision information from knowledge bases, and extracts entity descriptions from Freebase and Wikipedia pages to supplement background knowledge for the authors' task.
Knowledge Graph Completion with Adaptive Sparse Transfer Matrix
TLDR
Experimental results show that TranSparse outperforms Trans(E, H, R, and D) significantly, and achieves state-of-the-art performance on triplet classification and link prediction tasks.
Generating Natural Answers by Incorporating Copying and Retrieving Mechanisms in Sequence-to-Sequence Learning
TLDR
The empirical study on both synthetic and real-world datasets demonstrates the efficiency of COREQA, which is able to generate correct, coherent and natural answers for knowledge inquired questions.
Leveraging FrameNet to Improve Automatic Event Detection
TLDR
This paper proposes a global inference approach to detect events in FN and analyzes possible mappings from frames to event-types, and improves the performance of event detection and achieves a new state-of-the-art result.
Learning the Extraction Order of Multiple Relational Facts in a Sentence with Reinforcement Learning
TLDR
This paper argues that the extraction order is important in the multiple relation extraction task, and applies the reinforcement learning into a sequence-to-sequence model that could generate relational facts freely.
An End-to-End Model for Question Answering over Knowledge Base with Cross-Attention Combining Global Knowledge
TLDR
This work presents an end-to-end neural network model to represent the questions and their corresponding scores dynamically according to the various candidate answer aspects via cross-attention mechanism, and leverages the global knowledge inside the underlying KB, aiming at integrating the rich KB information into the representation of the answers.
CASIA@V2: A MLN-based Question Answering System over Linked Data
TLDR
A jointly learning framework using Markov Logic Network (MLN) for phrase detection, phrases mapping to seman- tic items and semantic items grouping for question answering system CASIA@V2 over Linked Data.
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