conceptnet

ldtoolkit/conceptnet-lite

Python library to work with ConceptNet offline without the need for PostgreSQL

D2KLab/ZeSTE

Explainable Zero-Shot Topic Extraction

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.

adityaSomak/PSLQA

An implementation of Probabilistic Soft Logic Engine using Python/Gurobi

causenet-org/CIKM-20

CauseNet: Towards a Causality Graph Extracted from the Web

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).

tae898/conceptnet5

Code for building ConceptNet from raw data.

Aunsiels/CSK

Code for generating Quasimodo, a commonsense knowledge base.

Heidelberg-NLP/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.

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

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.

Heidelberg-NLP/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.

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.

alexeyev/RuConceptNet

/ru/ConceptNet5.7 Python wrapper

zgornel/ConceptnetNumberbatch.jl

Julia API for ConceptNetNumberbatch

bi-graph/KGdatasets

Public datasets for graph embedding

play0137/Chinese_ConceptNet

Refined and expanded version of Chinese ConceptNet

dhanyagowrish/Winograd-Schema-Challenge

An SVM based approach to solve the Winograd Schema Challenge

IBM/tpf-conceptnet-datasource

ConceptNet datasource for the linked data fragments server (Server.js)

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.

Shriansh2002/conceptnet-finder

ConceptNet 🚀🚀 is a powerful semantic network that represents general knowledge in a machine-readable format.

FawziElNaggar/Generate-Questions-and-Answering-using-NLP

Generate question with different types from any kind of text data and get answers for it.

lukas-a-olson/conceptnet-exploration

play0137/Generate_coherent_text

Generate coherent and understandable text in Chinese