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Knowprompt: Knowledge-aware prompt-tuning with synergistic optimization for relation extraction
Recently, prompt-tuning has achieved promising results for specific few-shot classification
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …
tasks. The core idea of prompt-tuning is to insert text pieces (ie, templates) into the input and …
A deep learning pipeline for patient diagnosis prediction using electronic health records
Augmentation of disease diagnosis and decision-making in healthcare with machine
learning algorithms is gaining much impetus in recent years. In particular, in the current …
learning algorithms is gaining much impetus in recent years. In particular, in the current …
[HTML][HTML] Towards the Web of Embeddings: integrating multiple knowledge graph embedding spaces with FedCoder
Abstract The Semantic Web is distributed yet interoperable: Distributed since resources are
created and published by a variety of producers, tailored to their specific needs and …
created and published by a variety of producers, tailored to their specific needs and …
Wiki-based prompts for enhancing relation extraction using language models
Prompt-tuning and instruction-tuning of language models have exhibited significant results
in few-shot Natural Language Processing (NLP) tasks, such as Relation Extraction (RE) …
in few-shot Natural Language Processing (NLP) tasks, such as Relation Extraction (RE) …
Entity alignment with adaptive margin learning knowledge graph embedding
L Shen, R He, S Huang - Data & Knowledge Engineering, 2022 - Elsevier
A large number of knowledge graphs have been constructed at present. However, there is
diversity and heterogeneity among different knowledge graphs. The relation and attribute of …
diversity and heterogeneity among different knowledge graphs. The relation and attribute of …
Towards automatic bias detection in knowledge graphs
With the recent surge in social applications relying on knowledge graphs, the need for
techniques to ensure fairness in KG based methods is becoming increasingly evident …
techniques to ensure fairness in KG based methods is becoming increasingly evident …
Revisiting text and knowledge graph joint embeddings: The amount of shared information matters!
Jointly learning embeddings from text and a Knowledge Graph benefits both word and
entity/relation embeddings by taking advantage of both large-scale unstructured content …
entity/relation embeddings by taking advantage of both large-scale unstructured content …
Entity prediction in knowledge graphs with joint embeddings
Abstract Knowledge Graphs (KGs) have become increasingly popular in the recent years.
However, as knowledge constantly grows and changes, it is inevitable to extend existing …
However, as knowledge constantly grows and changes, it is inevitable to extend existing …
[PDF][PDF] Linking knowledge graphs and images using embeddings
C Edwards - 2018 - cnedwards.com
Abstract Knowledge graphs and images offer contrasting yet complementary sources of
information. Images offer visuospatial data lacking in the structure of knowledge graphs. By …
information. Images offer visuospatial data lacking in the structure of knowledge graphs. By …
[HTML][HTML] Extending knowledge graph embeddings for data imputation
P Rosso - 2021 - folia.unifr.ch
English French With the advancement of Big Data and Natural Language Processing (NLP)
technologies, extensive research into Knowledge Graphs (KGs) has been conducted. In a …
technologies, extensive research into Knowledge Graphs (KGs) has been conducted. In a …