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Knowledge Graphs

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KGAT: Knowledge Graph Attention Network for Recommendation

20 May 2019xiangwang1223/knowledge_graph_attention_network

To provide more accurate, diverse, and explainable recommendation, it is compulsory to go beyond modeling user-item interactions and take side information into account.

KNOWLEDGE GRAPHS RECOMMENDATION SYSTEMS

PaperRobot: Incremental Draft Generation of Scientific Ideas

ACL 2019 EagleW/PaperRobot

We present a PaperRobot who performs as an automatic research assistant by (1) conducting deep understanding of a large collection of human-written papers in a target domain and constructing comprehensive background knowledge graphs (KGs); (2) creating new ideas by predicting links from the background KGs, by combining graph attention and contextual text attention; (3) incrementally writing some key elements of a new paper based on memory-attention networks: from the input title along with predicted related entities to generate a paper abstract, from the abstract to generate conclusion and future work, and finally from future work to generate a title for a follow-on paper.

KNOWLEDGE GRAPHS PAPER GENERATION

COMET: Commonsense Transformers for Automatic Knowledge Graph Construction

ACL 2019 atcbosselut/comet-commonsense

We present the first comprehensive study on automatic knowledge base construction for two prevalent commonsense knowledge graphs: ATOMIC (Sap et al., 2019) and ConceptNet (Speer et al., 2017).

GRAPH CONSTRUCTION KNOWLEDGE GRAPHS

Position-aware Attention and Supervised Data Improve Slot Filling

EMNLP 2017 yuhaozhang/tacred-relation

The combination of better supervised data and a more appropriate high-capacity model enables much better relation extraction performance.

KNOWLEDGE BASE POPULATION KNOWLEDGE GRAPHS RELATION EXTRACTION SLOT FILLING

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

ACL 2019 deepakn97/relationPrediction

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Learning Attention-based Embeddings for Relation Prediction in Knowledge Graphs

Association of Computational Linguistics (ACl) 2019 2019 deepakn97/relationPrediction

The recent proliferation of knowledge graphs (KGs) coupled with incomplete or partial information, in the form of missing relations (links) between entities, has fueled a lot of research on knowledge base completion (also known as relation prediction).

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH COMPLETION LINK PREDICTION

KBGAN: Adversarial Learning for Knowledge Graph Embeddings

NAACL 2018 cai-lw/KBGAN

This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPH EMBEDDING KNOWLEDGE GRAPH EMBEDDINGS KNOWLEDGE GRAPHS LINK PREDICTION

Text Generation from Knowledge Graphs with Graph Transformers

NAACL 2019 rikdz/GraphWriter

Generating texts which express complex ideas spanning multiple sentences requires a structured representation of their content (document plan), but these representations are prohibitively expensive to manually produce.

KNOWLEDGE GRAPHS TEXT GENERATION