Results 41 to 50 of about 9,552,944 (333)
KEPLER: A Unified Model for Knowledge Embedding and Pre-trained Language Representation [PDF]
Transactions of the Association for Computational Linguistics, 2019 Pre-trained language representation models (PLMs) cannot well capture factual knowledge from text. In contrast, knowledge embedding (KE) methods can effectively represent the relational facts in knowledge graphs (KGs) with informative entity embeddings ...
Xiaozhi Wang +5 more
semanticscholar +1 more source
Scientific Reports, 2023 The complexity of the cerebral cortex underlies its function and distinguishes us as humans. Here, we present a principled veridical data science methodology for quantitative histology that shifts focus from image-level investigations towards neuron ...
Andrija Štajduhar +4 more
doaj +1 more source
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence [PDF]
AAAI Conference on Artificial Intelligence, 2017 Knowledge graphs (KGs), which could provide essential relational information between entities, have been widely utilized in various knowledge-driven applications.
Ruobing Xie, Zhiyuan Liu, Maosong Sun
semanticscholar +1 more source
Towards a knowledge graph for pre-/probiotics and microbiota–gut–brain axis diseases
Scientific Reports, 2022 Scientific publications present biological relationships but are structured for human reading, making it difficult to use this resource for semantic integration and querying.
Ting Liu +4 more
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K-BERT: Enabling Language Representation with Knowledge Graph [PDF]
AAAI Conference on Artificial Intelligence, 2019 Pre-trained language representation models, such as BERT, capture a general language representation from large-scale corpora, but lack domain-specific knowledge. When reading a domain text, experts make inferences with relevant knowledge. For machines to
Weijie Liu +6 more
semanticscholar +1 more source
Commonsense knowledge representation and reasoning with fuzzy neural networks [PDF]
, 1996 This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning.
He, Fangpo, Kouzani, Abbas, Sammut, Karl
core +1 more source
Knowledge Representation with Ontologies: The Present and Future [PDF]
, 2004 Recently, we have seen an explosion of interest in ontologies as artifacts to represent human knowledge and as critical components in knowledge management, the semantic Web, business-to-business applications, and several other application areas ...
Brewster, Christopher +7 more
core +1 more source
The cat's cradle network [PDF]
, 2003 In this paper we will argue that the representation of context in knowledge management is appropriately served by the representation of the knowledge networks in an historicised form.
Gammack, J., Hobbs, V.J., Pigott, D.
core +2 more sources
Image-embodied Knowledge Representation Learning [PDF]
International Joint Conference on Artificial Intelligence, 2016 Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images.
Ruobing Xie +3 more
semanticscholar +1 more source
Efficient Smooth Tensor Train and Tensor Ring Completion for Image Classification Enhancement
IEEE AccessThis paper deals with studying the data completion problem for enhancing the image classification task under the pixel removal scenario. In some applications, it happens that a part of the pixels of a given image is lost due to several issues, such as ...
Salman Ahmadi-Asl +5 more
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