Open AccessProceedings Article
Representing General Relational Knowledge in ConceptNet 5
Robert Speer,Catherine Havasi +1 more
- pp 3679-3686
TLDR
The latest iteration of ConceptNet 5 is presented, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.Abstract:
ConceptNet is a knowledge representation project, providing a large semantic graph that describes general human knowledge and how it is expressed in natural language. This paper presents the latest iteration, ConceptNet 5, including its fundamental design decisions, ways to use it, and evaluations of its coverage and accuracy.read more
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Knowledge vault: a web-scale approach to probabilistic knowledge fusion
Xin Dong,Evgeniy Gabrilovich,Geremy Heitz,Wilko Horn,Ni Lao,Kevin Murphy,Thomas Strohmann,Shaohua Sun,Wei Zhang +8 more
TL;DR: The Knowledge Vault is a Web-scale probabilistic knowledge base that combines extractions from Web content (obtained via analysis of text, tabular data, page structure, and human annotations) with prior knowledge derived from existing knowledge repositories that computes calibrated probabilities of fact correctness.
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ERNIE: Enhanced Language Representation with Informative Entities
TL;DR: This paper utilizes both large-scale textual corpora and KGs to train an enhanced language representation model (ERNIE) which can take full advantage of lexical, syntactic, and knowledge information simultaneously, and is comparable with the state-of-the-art model BERT on other common NLP tasks.
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Language Models as Knowledge Bases
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TL;DR: An in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models finds that BERT contains relational knowledge competitive with traditional NLP methods that have some access to oracle knowledge.
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Language Models as Knowledge Bases
Fabio Petroni,Tim Rocktäschel,Patrick S. H. Lewis,Anton Bakhtin,Yuxiang Wu,Alexander H. Miller,Sebastian Riedel +6 more
TL;DR: This article presented an in-depth analysis of the relational knowledge already present (without fine-tuning) in a wide range of state-of-the-art pretrained language models.
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Commonsense Knowledge Aware Conversation Generation with Graph Attention
TL;DR: This is the first attempt that uses large-scale commonsense knowledge in conversation generation, and unlike existing models that use knowledge triples (entities) separately and independently, this model treats each knowledge graph as a whole, which encodes more structured, connected semantic information in the graphs.
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