concept

Semantic Parsing

Also known as: SP, semantic parser

Facts (13)

Sources

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv Feb 16, 2025 4 facts

referencePeng Jiang and Xiaodong Cai published 'A survey of semantic parsing techniques' in Symmetry in 2024.

procedureIn a semantic parsing task using Sequential Neuro-Symbolic AI, the system follows these steps: (1) map a sequence of symbolic tokens to continuous embeddings using methods like word2vec or GloVe, (2) process these embeddings through a neural network to learn compositional patterns or transformations, and (3) decode the processed information back into a structured logical form, such as knowledge-graph triples.

claimSemantic parsing leverages neural networks to uncover latent patterns in symbolic inputs and generate interpretable symbolic conclusions.

referenceNatural language processing (NLP) technologies include retrieval-augmented generation (RAG), sequence-to-sequence models, semantic parsing, named entity recognition (NER), and relation extraction.

Enterprise AI Requires the Fusion of LLM and Knowledge Graph stardog.com Stardog Dec 4, 2024 3 facts

claimStardog provides a single platform capable of performing RAG, Graph RAG, and hallucination-free Semantic Parsing.

perspectiveStardog asserts that Semantic Parsing is a superior method for handling GenAI and user inputs compared to any variant of RAG (Retrieval-Augmented Generation), including Graph RAG.

claimStardog utilizes 'Safety RAG' (Semantic Parsing against complete knowledge) for enterprise use cases where hallucinations are unacceptable.

Neuro-Symbolic AI: Explainability, Challenges, and Future Trends arxiv.org arXiv Nov 7, 2024 2 facts

referenceKapanipathi et al. (2020) proposed a neural symbolic question answering (NSQA) system that relies on semantic parsing and reasoning.

procedureThe neural symbolic framework proposed by Kimura et al. (2021) for text-based games follows a multi-step process: (1) a semantic parser extracts basic propositional logic from text observations in the environment, converting natural language into logical expressions; (2) external knowledge bases like ConceptNet are used to understand word semantic categories and refine the extracted propositional logic; (3) the refined logic and lexical category information are combined via a First Order Logic (FOL) converter into logical facts representing game state conditions; (4) these logical facts are used as training input for a Logical Neural Network (LNN).

A survey on augmenting knowledge graphs (KGs) with large ... link.springer.com Springer Nov 4, 2024 2 facts

referenceBerant J and Liang P published 'Semantic parsing via paraphrasing' in 2014.

procedureSemantic parsing, entity linking, and relation extraction are techniques used to implement semantic layers by extracting and inferring critical concepts and relationships from data to feed into LLMs during processing.

KG-IRAG: A Knowledge Graph-Based Iterative Retrieval-Augmented ... arxiv.org arXiv Mar 18, 2025 1 fact

referenceApproaches to Knowledge Graph-based Question Answering (KBQA) are categorized into Information Retrieval (IR)-based methods and Semantic Parsing (SP)-based methods. IR-based methods directly retrieve information from knowledge graph databases and use the returned knowledge to generate answers, whereas SP-based methods generate logical forms for queries, which are then used for knowledge retrieval.

The construction and refined extraction techniques of knowledge ... nature.com Nature Feb 10, 2026 1 fact

procedureThe data processing pipeline for the framework involves: (1) converting communication logs into instruction chains with temporal tags and feedback records via semantic parsing, (2) analyzing equipment documents to match performance characteristics with operational scenarios, (3) transforming simulation data into decision trees annotated with probabilities and outcomes, and (4) restructuring theoretical literature into rule-explanation texts linked with historical event databases.