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This repository contains code for the RAGonite project, a flexible RAG pipeline developed by the NLP team at Fraunhofer IIS, Erlangen, Germany. RAGONITE supports conversational question answering o…
ARI (Abstract Reasoning Induction) is an innovative framework designed to enhance the temporal reasoning capabilities of Large Language Models (LLMs).
The repository for ACL 2024 paper "TimeBench: A Comprehensive Evaluation of Temporal Reasoning Abilities in Large Language Models"
The source code of "Local and Global: Temporal Question Answering via Information Fusion"
A GUI interface to convert Urban Knowledge into Database SQL, SQLAlchemy classes, and a FastAPI demo server.
ACL 2021: Question Answering over Temporal Knowledge Graphs
Source code and dataset for KDD 2020 paper "Understanding Negative Sampling in Graph Representation Learning"
State-of-the-Art Text Embeddings
Implementation of a post-hoc cleaning module for CLOCQ, that can help to apply CLOCQ on entity or relation linking tasks.
Code for our CIKM 2019 paper. As far as we know, CONVEX is the first unsupervised method for conversational question answering over knowledge graphs. A demo and our benchmark (and more) can be foun…
Project to prepare a n-triples wikidata dump for QA access.
Code for our SIGIR 2022 paper. CONVINSE is a framework for conversational question answering (ConvQA) over heterogeneous information sources.
[AAAI 2022] TempoQR: Temporal Question Reasoning over Knowledge Graphs
Official implementation for "Automatic Chain of Thought Prompting in Large Language Models" (stay tuned & more will be updated)
Code for our SIGIR 2023 paper. EXPLAIGNN provides a pipeline for conversational question answering (ConvQA) over heterogeneous sources, and code for iterative graph neural networks (GNNs). Such ite…
Code for our WSDM 2022 paper. CLOCQ is a framework which allows efficient access to knowledge bases (KB) for functionalities related to question answering (QA). CLOCQ can retrieve a set of relevant…
CBench, Benchmarking System for Question Answering Over Knowledge Graphs Systems.