Unifying large language models and knowledge graphs: A roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves in the
field of natural language processing and artificial intelligence, due to their emergent ability …
field of natural language processing and artificial intelligence, due to their emergent ability …
Grapheval: A knowledge-graph based llm hallucination evaluation framework
H Sansford, N Richardson, HP Maretic… - arXiv preprint arXiv …, 2024 - arxiv.org
Methods to evaluate Large Language Model (LLM) responses and detect inconsistencies,
also known as hallucinations, with respect to the provided knowledge, are becoming …
also known as hallucinations, with respect to the provided knowledge, are becoming …
[PDF][PDF] Knowledge Graph Efficient Construction: Embedding Chain-of-Thought into LLMs
ABSTRACT Large Language Models (LLMs) are extensively utilized for extracting key
information from unstructured data to construct Knowledge Graph (KG) due to their …
information from unstructured data to construct Knowledge Graph (KG) due to their …
Large Model Agents: State-of-the-Art, Cooperation Paradigms, Security and Privacy, and Future Trends
Large Model (LM) agents, powered by large foundation models such as GPT-4 and DALL-E
2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM …
2, represent a significant step towards achieving Artificial General Intelligence (AGI). LM …
Scalable Learning of Latent Language Structure With Logical Offline Cycle Consistency
We introduce Logical Offline Cycle Consistency Optimization (LOCCO), a scalable, semi-
supervised method for training a neural semantic parser. Conceptually, LOCCO can be …
supervised method for training a neural semantic parser. Conceptually, LOCCO can be …
[PDF][PDF] From mission description to knowledge graph: Applying transformer-based models to map knowledge from publicly available satellite datasets
A Berquand, AV Ladeira - … of the 10th International Systems & …, 2022 - researchgate.net
Keeping up with the exponential number of space missions being designed and launched
worldwide is a tricky task. Adding to the difficulty, information is scattered across various …
worldwide is a tricky task. Adding to the difficulty, information is scattered across various …
Prompt Me One More Time: A Two-Step Knowledge Extraction Pipeline with Ontology-Based Verification
This study explores a method for extending real-world knowledge graphs (specifically,
Wikidata) by extracting triplets from texts with the aid of Large Language Models (LLMs). We …
Wikidata) by extracting triplets from texts with the aid of Large Language Models (LLMs). We …
[PDF][PDF] Knowledge-centric Prompt Composition for Knowledge Base Construction from Pre-trained Language Models.
Pretrained language models (PLMs), exemplified by the GPT family of models, have
exhibited remarkable proficiency across a spectrum of natural language processing tasks …
exhibited remarkable proficiency across a spectrum of natural language processing tasks …
Text-Conditioned Graph Generation Using Discrete Graph Variational Autoencoders
M Longland, D Liebowitz, K Moore… - … Conference on Data …, 2023 - Springer
Inspired by recent progress in text-conditioned image generation, we propose a model for
the problem of text-conditioned graph generation. We introduce the Vector Quantized Text to …
the problem of text-conditioned graph generation. We introduce the Vector Quantized Text to …
Language models for ontology engineering
Y He - 2024 - ora.ox.ac.uk
Ontology, originally a philosophical term, refers to the study of being and existence. The
concept was introduced to Artificial Intelligence (AI) as a knowledge-based system that can …
concept was introduced to Artificial Intelligence (AI) as a knowledge-based system that can …