Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning

• URL: http://arxiv.org/abs/2304.02711v2
• Date: Fri, 22 Dec 2023 22:01:58 GMT
• Title: Structured prompt interrogation and recursive extraction of semantics (SPIRES): A method for populating knowledge bases using zero-shot learning
• Authors: J. Harry Caufield, Harshad Hegde, Vincent Emonet, Nomi L. Harris, Marcin P. Joachimiak, Nicolas Matentzoglu, HyeongSik Kim, Sierra A.T. Moxon, Justin T. Reese, Melissa A. Haendel, Peter N. Robinson, and Christopher J. Mungall
• Abstract summary: We present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES) SPIRES relies on the ability of Large Language Models (LLMs) to perform zero-shot learning (ZSL) and general-purpose vocabularies answering from flexible prompts and return information to a specified schema. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction (RE) methods, but has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any training data.
• Score: 1.3963666696384924
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Creating knowledge bases and ontologies is a time consuming task that relies on a manual curation. AI/NLP approaches can assist expert curators in populating these knowledge bases, but current approaches rely on extensive training data, and are not able to populate arbitrary complex nested knowledge schemas. Here we present Structured Prompt Interrogation and Recursive Extraction of Semantics (SPIRES), a Knowledge Extraction approach that relies on the ability of Large Language Models (LLMs) to perform zero-shot learning (ZSL) and general-purpose query answering from flexible prompts and return information conforming to a specified schema. Given a detailed, user-defined knowledge schema and an input text, SPIRES recursively performs prompt interrogation against GPT-3+ to obtain a set of responses matching the provided schema. SPIRES uses existing ontologies and vocabularies to provide identifiers for all matched elements. We present examples of use of SPIRES in different domains, including extraction of food recipes, multi-species cellular signaling pathways, disease treatments, multi-step drug mechanisms, and chemical to disease causation graphs. Current SPIRES accuracy is comparable to the mid-range of existing Relation Extraction (RE) methods, but has the advantage of easy customization, flexibility, and, crucially, the ability to perform new tasks in the absence of any training data. This method supports a general strategy of leveraging the language interpreting capabilities of LLMs to assemble knowledge bases, assisting manual knowledge curation and acquisition while supporting validation with publicly-available databases and ontologies external to the LLM. SPIRES is available as part of the open source OntoGPT package: https://github.com/ monarch-initiative/ontogpt.

Related papers

• DIVKNOWQA: Assessing the Reasoning Ability of LLMs via Open-Domain Question Answering over Knowledge Base and Text [73.68051228972024]
  Large Language Models (LLMs) have exhibited impressive generation capabilities, but they suffer from hallucinations when relying on their internal knowledge. Retrieval-augmented LLMs have emerged as a potential solution to ground LLMs in external knowledge.
  arXiv  Detail & Related papers  (2023-10-31T04:37:57Z)
• An In-Context Schema Understanding Method for Knowledge Base Question Answering [70.87993081445127]
  Large Language Models (LLMs) have shown strong capabilities in language understanding and can be used to solve this task. Existing methods bypass this challenge by initially employing LLMs to generate drafts of logic forms without schema-specific details. We propose a simple In-Context Understanding (ICSU) method that enables LLMs to directly understand schemas by leveraging in-context learning.
  arXiv  Detail & Related papers  (2023-10-22T04:19:17Z)
• Self-Knowledge Guided Retrieval Augmentation for Large Language Models [59.771098292611846]
  Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Retrieval-based methods can offer non-parametric world knowledge and improve the performance on tasks such as question answering. Self-Knowledge guided Retrieval augmentation (SKR) is a simple yet effective method which can let LLMs refer to the questions they have previously encountered.
  arXiv  Detail & Related papers  (2023-10-08T04:22:33Z)
• LLM Guided Inductive Inference for Solving Compositional Problems [1.6727879968475368]
  Large language models (LLMs) have demonstrated impressive performance in question-answering tasks. Existing methods decompose reasoning tasks through the use of modules invoked sequentially. We introduce a method, Recursion based LLM (REBEL), which handles open-world, deep reasoning tasks.
  arXiv  Detail & Related papers  (2023-09-20T23:44:16Z)
• Schema-aware Reference as Prompt Improves Data-Efficient Knowledge Graph Construction [57.854498238624366]
  We propose a retrieval-augmented approach, which retrieves schema-aware Reference As Prompt (RAP) for data-efficient knowledge graph construction. RAP can dynamically leverage schema and knowledge inherited from human-annotated and weak-supervised data as a prompt for each sample.
  arXiv  Detail & Related papers  (2022-10-19T16:40:28Z)
• Explaining Patterns in Data with Language Models via Interpretable Autoprompting [143.4162028260874]
  We introduce interpretable autoprompting (iPrompt), an algorithm that generates a natural-language string explaining the data. iPrompt can yield meaningful insights by accurately finding groundtruth dataset descriptions. Experiments with an fMRI dataset show the potential for iPrompt to aid in scientific discovery.
  arXiv  Detail & Related papers  (2022-10-04T18:32:14Z)
• BERTese: Learning to Speak to BERT [50.76152500085082]
  We propose a method for automatically rewriting queries into "BERTese", a paraphrase query that is directly optimized towards better knowledge extraction. We empirically show our approach outperforms competing baselines, obviating the need for complex pipelines.
  arXiv  Detail & Related papers  (2021-03-09T10:17:22Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.