GitHub topics: conceptnet
JulesBelveze/concepcy
💫 SpaCy wrapper for ConceptNet 💫
Language: Python - Size: 951 KB - Last synced at: 10 days ago - Pushed at: over 1 year ago - Stars: 92 - Forks: 4
tae898/conceptnet5 Fork of commonsense/conceptnet5
Code for building ConceptNet from raw data.
Language: Dockerfile - Size: 29.4 MB - Last synced at: 1 day ago - Pushed at: over 1 year ago - Stars: 21 - Forks: 4
ldtoolkit/conceptnet-lite
Python library to work with ConceptNet offline without the need for PostgreSQL
Language: Python - Size: 317 KB - Last synced at: 4 days ago - Pushed at: 2 months ago - Stars: 70 - Forks: 5
zgornel/ConceptnetNumberbatch.jl
Julia API for ConceptNetNumberbatch
Language: Julia - Size: 104 KB - Last synced at: 29 days ago - Pushed at: over 1 year ago - Stars: 6 - Forks: 2
D2KLab/ZeSTE
Explainable Zero-Shot Topic Extraction
Language: JavaScript - Size: 4.28 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 60 - Forks: 16
play0137/Chinese_ConceptNet
Refined and expanded version of Chinese ConceptNet
Size: 31.9 MB - Last synced at: 9 months ago - Pushed at: 9 months ago - Stars: 5 - Forks: 0
FaridYusifli/Multiple_Knowledge_GraphDB-MKGDB
a large-scale graph database created as a combination of multiple taxonomy backbones extracted from 5 existing knowledge graphs, namely: ConceptNet, DBpedia, WebIsAGraph, WordNet and the Wikipedia category hierarchy
Language: Java - Size: 56.6 KB - Last synced at: 10 months ago - Pushed at: over 1 year ago - Stars: 11 - Forks: 2
IBM/tpf-conceptnet-datasource
ConceptNet datasource for the linked data fragments server (Server.js)
Language: JavaScript - Size: 229 KB - Last synced at: 2 months ago - Pushed at: over 1 year ago - Stars: 4 - Forks: 0
ECNU-ICA/ECNU-SenseMaker
📝 Source code for "ECNU-SenseMaker at SemEval-2020 Task 4: Leveraging Heterogeneous Knowledge Resources for Commonsense Validation and Explanation" (SemEval 2020).
Language: Python - Size: 5.74 MB - Last synced at: over 1 year ago - Pushed at: almost 2 years ago - Stars: 21 - Forks: 3
Shriansh2002/conceptnet-finder
ConceptNet 🚀🚀 is a powerful semantic network that represents general knowledge in a machine-readable format.
Language: JavaScript - Size: 458 KB - Last synced at: about 1 month ago - Pushed at: about 2 years ago - Stars: 0 - Forks: 0
Heidelberg-NLP/LMs4Implicit-Knowledge-Generation Fork of maria-becker/LMs4Implicit-Knowledge-Generation
Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key concepts and commonsense knowledge paths connecting them.
Language: Python - Size: 128 KB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 14 - Forks: 3
Heidelberg-NLP/CO-NNECT Fork of maria-becker/CO-NNECT
This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classification using Language Models), a relation classification system that we developed for classifying commonsense knowledge relations; and COMET, a target prediction system developed by Bosselut et al., 2019.
Language: Python - Size: 2.32 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 9 - Forks: 1
Heidelberg-NLP/CoCo-Ex
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.
Language: Python - Size: 22.2 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 48 - Forks: 7
maria-becker/LMs4Implicit-Knowledge-Generation
Code for equipping pretrained language models (BART, GPT-2, XLNet) with commonsense knowledge for generating implicit knowledge statements between two sentences, by (i) finetuning the models on corpora enriched with implicit information; and by (ii) constraining models with key concepts and commonsense knowledge paths connecting them.
Language: Python - Size: 128 KB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 6 - Forks: 4
maria-becker/CoCo-Ex
CoCo-Ex extracts meaningful concepts from natural language texts and maps them to conjunct concept nodes in ConceptNet, utilizing the maximum of relational information stored in the ConceptNet knowledge graph.
Language: Python - Size: 22.2 MB - Last synced at: almost 2 years ago - Pushed at: over 2 years ago - Stars: 10 - Forks: 2
causenet-org/CIKM-20
CauseNet: Towards a Causality Graph Extracted from the Web
Language: Jupyter Notebook - Size: 228 KB - Last synced at: about 2 years ago - Pushed at: about 4 years ago - Stars: 17 - Forks: 6
Aunsiels/CSK
Code for generating Quasimodo, a commonsense knowledge base.
Language: Python - Size: 8.32 MB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 17 - Forks: 3
bi-graph/KGdatasets
Public datasets for graph embedding
Size: 47 MB - Last synced at: about 2 years ago - Pushed at: about 3 years ago - Stars: 2 - Forks: 0
adityaSomak/PSLQA
An implementation of Probabilistic Soft Logic Engine using Python/Gurobi
Language: Python - Size: 563 KB - Last synced at: about 2 years ago - Pushed at: over 6 years ago - Stars: 52 - Forks: 5
dhanyagowrish/Winograd-Schema-Challenge
An SVM based approach to solve the Winograd Schema Challenge
Language: Jupyter Notebook - Size: 51.8 KB - Last synced at: about 2 years ago - Pushed at: over 4 years ago - Stars: 2 - Forks: 0
FawziElNaggar/Generate-Questions-and-Answering-using-NLP
Generate question with different types from any kind of text data and get answers for it.
Language: Jupyter Notebook - Size: 94.7 KB - Last synced at: about 2 years ago - Pushed at: over 3 years ago - Stars: 0 - Forks: 1
maria-becker/CO-NNECT
This repository contains our path generation framework Co-NNECT, in which we combine two models for establishing knowledge relations and paths between concepts from sentences, as a form of explicitation of implicit knowledge: COREC-LM (COmmonsense knowledge RElation Classification using Language Models), a relation classification system that we developed for classifying commonsense knowledge relations; and COMET, a target prediction system developed by Bosselut et al., 2019.
Language: Python - Size: 2.31 MB - Last synced at: almost 2 years ago - Pushed at: almost 4 years ago - Stars: 1 - Forks: 3
alexeyev/RuConceptNet
/ru/ConceptNet5.7 Python wrapper
Language: Python - Size: 4.11 MB - Last synced at: 15 days ago - Pushed at: over 4 years ago - Stars: 8 - Forks: 2
