lingzhiwisemed

lingzhiwisemed's Stars

• HKUST-KnowComp/LMPNN

  Logical Message Passing Networks with One-hop Inference in Atomic Formulas (ICLR 2023)

  Language:Python153

• ThilinaRajapakse/simpletransformers

  Transformers for Information Retrieval, Text Classification, NER, QA, Language Modelling, Language Generation, T5, Multi-Modal, and Conversational AI

  Language:Python4.1k723

• fo40225/tensorflow-windows-wheel

  Tensorflow prebuilt binary for Windows

  Language:Python3.7k1.5k

• keras-team/keras-contrib

  Keras community contributions

  Language:Python1.6k652

• percent4/keras_bert_sequence_labeling

  本项目采用Keras和Keras-bert实现中文序列标注，对BERT进行微调，并在多个命名实体识别数据集上进行测试。

  Language:Python4715

• WILAB-HIT/Resources

  Language:Batchfile9039

• sanyabt/np-kg

  NP-KG: Knowledge Graph Framework for Natural Product-Drug Interactions

  Language:Jupyter Notebook10

• HisAnteater2/I-RGCN_DGL

  DGL Implementation of the I-RGCN Model, introduced in "Few-shot Link prediction via Graph Neural Networks for COVID-19 Drug-Repurposing."

  Language:Jupyter Notebook5

• jhshen95/LASS

  Code repo for COLING 2022 paper "Joint Language Semantic and Structure Embedding for Knowledge Graph Completion"

  Language:Python531

• ronanmmurphy/Knowledge-Graph-Embeddings-to-Implement-Explainability

  Knowledge Graph Embeddings (KGE) to implement Explainable Artificial Intelligence. As AI develops users must know how algorithms make their decisions, especially for hazardous tasks such as driverless cars. Knowledge graphs are an inherently understandable form of text-based data created as an interconnected network of information. These can be converted into KGE by transforming the unqiue entites in the graph to vector representations. With these, predictions were made for missing/incorrect links in the network and further explainations were made by plotting the clusters of the data. Knowledge graphs and their embedded models were researched and four of these KGE were created and tested by their ability to rank the correct links from a Covid-19 dataset. This dataset was extracted from research papers about the virus to retrieve information quicker. The model which was most accurate was used to implement knowledge graph completion and explainability of the dataset using visual and textual interpretations. A 29,000-word thesis was written to describe the work done through the researching, testing and interpreting of this project.

  Language:Jupyter Notebook111

• thuiar/OKD-Reading-List

  Papers for Open Knowledge Discovery

  Language:TeX11924

• mattzheng/DouBanRecommend

  基于豆瓣图书的推荐、知识图谱与知识引擎简单构建neo4j

  Language:Python26755

• jiangsirspider/knowledge_reccomend

  基于知识图谱的医生推荐系统

  Language:Python627

• qiu997018209/KnowledgeGraph

  知识图谱车音工作项目

  Language:Python250175

• OpenKG-ORG/OpenRichpedia

  东南大学多模态知识图谱-OpenRichpedia工程文件

  284

• lasigeBioTM/K-BiOnt

  Biomedical Relation Extraction with Knowledge Graph-based Recommendations

  Language:Python85

• explainablerecsys/recsys2022

  RecSys 2022 Tutorial Hands on Explainable Recommender Systems with Knowledge Graphs

  Language:Python494

• Zhankun-Xiong/Recommendation-system-based-on-knowledge-graph-embedding

  Recommendation system based on knowledge graph embedding

  Language:Python15849

• Randool/pyRecommender

  基于知识图谱的推荐系统

  Language:Python17543

• GavinHacker/recsys_core

  [推荐系统] Based on the scoring data set, the recommendation system is built with FM and LR as the core(基于评分数据集，构建以FM和LR为核心的推荐系统).

  Language:Jupyter Notebook30380

• hlf20010508/KGE_NFM

  基于知识图谱和推荐系统的药物靶标相互作用预测框架

  Language:Python142

• recommenders-team/recommenders

  Best Practices on Recommendation Systems

  Language:Python19.6k3.1k

• lemonhu/stock-knowledge-graph

  利用网络上公开的数据构建一个小型的证券知识图谱/知识库

  Language:Python2k596

• jm199504/Financial-Knowledge-Graphs

  小型金融知识图谱构建流程（neo4j / python / cypher / KG）

  Language:Jupyter Notebook2.8k530

• wenxianxian/demvae

  Dispersed Exponential Family Mixture VAE

  Language:OpenEdge ABL272

• mjc92/CopyNet

  An implementation of "Incorporating copying mechanism in sequence-to-sequence learning"

  Language:Python15345

• neunms/Reinforcement-learning-on-graphs-A-survey

  This collection of papers can be used to summarize research about graph reinforcement learning for the convenience of researchers.

  15922

• JohnSnowLabs/spark-nlp-workshop

  Public runnable examples of using John Snow Labs' NLP for Apache Spark.

  Language:Jupyter Notebook1k605

• DeepGraphLearning/RNNLogic

  Language:C++12925

• FedML-AI/FedML

  FEDML - The unified and scalable ML library for large-scale distributed training, model serving, and federated learning. FEDML Launch, a cross-cloud scheduler, further enables running any AI jobs on any GPU cloud or on-premise cluster. Built on this library, TensorOpera AI (https://TensorOpera.ai) is your generative AI platform at scale.

  Language:Python4.2k789