RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG

• URL: http://arxiv.org/abs/2412.17690v3
• Date: Wed, 25 Dec 2024 15:05:04 GMT
• Title: RAGONITE: Iterative Retrieval on Induced Databases and Verbalized RDF for Conversational QA over KGs with RAG
• Authors: Rishiraj Saha Roy, Chris Hinze, Joel Schlotthauer, Farzad Naderi, Viktor Hangya, Andreas Foltyn, Luzian Hahn, Fabian Kuech,
• Abstract summary: SPARQL is brittle for complex intents and conversational questions. We propose a novel two-pronged system where we fuse: (i) SPARQL results over a database automatically derived from the knowledge graph, and (ii) text-search results over verbalizations of KG facts. Our pipeline supports iterative retrieval: when the results of any branch are found to be unsatisfactory, the system can automatically opt for further rounds. We demonstrate the superiority of our proposed system over several baselines on a knowledge graph of BMW automobiles.
• Score: 6.4032082023113475
• License:
• Abstract: Conversational question answering (ConvQA) is a convenient means of searching over RDF knowledge graphs (KGs), where a prevalent approach is to translate natural language questions to SPARQL queries. However, SPARQL has certain shortcomings: (i) it is brittle for complex intents and conversational questions, and (ii) it is not suitable for more abstract needs. Instead, we propose a novel two-pronged system where we fuse: (i) SQL-query results over a database automatically derived from the KG, and (ii) text-search results over verbalizations of KG facts. Our pipeline supports iterative retrieval: when the results of any branch are found to be unsatisfactory, the system can automatically opt for further rounds. We put everything together in a retrieval augmented generation (RAG) setup, where an LLM generates a coherent response from accumulated search results. We demonstrate the superiority of our proposed system over several baselines on a knowledge graph of BMW automobiles.

Related papers

• Ontology-Guided, Hybrid Prompt Learning for Generalization in Knowledge Graph Question Answering [6.232269207752904]
  We present OntoSCPrompt, a novel Large Language Model (LLM)-based KGQA approach with a two-stage architecture. OntoSCPrompt first generates a SPARQL query structure (including SPARQL keywords such as SELECT, ASK, WHERE and placeholders for missing tokens) and then fills them with KG-specific information. We present several task-specific decoding strategies to ensure the correctness and executability of generated SPARQL queries in both stages.
  arXiv  Detail & Related papers  (2025-02-06T11:47:58Z)
• Can we Retrieve Everything All at Once? ARM: An Alignment-Oriented LLM-based Retrieval Method [48.14236175156835]
  ARM aims to better align the question with the organization of the data collection by exploring relationships among data objects. It outperforms standard RAG with query decomposition by up to 5.2 pt in execution accuracy and agentic RAG (ReAct) by up to 15.9 pt. It achieves up to 5.5 pt and 19.3 pt higher F1 match scores compared to these approaches.
  arXiv  Detail & Related papers  (2025-01-30T18:07:19Z)
• Evidence Contextualization and Counterfactual Attribution for Conversational QA over Heterogeneous Data with RAG Systems [4.143039012104666]
  Retrieval Augmented Generation (RAG) works as a backbone for interacting with an enterprise's own data via Conversational Question Answering (ConvQA) In this work, we demonstrate RAGONITE, a RAG system that remedies the above concerns by: (i) contextualizing evidence with source metadata and surrounding text; and (ii) computing counterfactual attribution.
  arXiv  Detail & Related papers  (2024-12-13T21:28:17Z)
• Effective Instruction Parsing Plugin for Complex Logical Query Answering on Knowledge Graphs [51.33342412699939]
  Knowledge Graph Query Embedding (KGQE) aims to embed First-Order Logic (FOL) queries in a low-dimensional KG space for complex reasoning over incomplete KGs. Recent studies integrate various external information (such as entity types and relation context) to better capture the logical semantics of FOL queries. We propose an effective Query Instruction Parsing (QIPP) that captures latent query patterns from code-like query instructions.
  arXiv  Detail & Related papers  (2024-10-27T03:18:52Z)
• LLM-based SPARQL Query Generation from Natural Language over Federated Knowledge Graphs [0.0]
  We introduce a Retrieval-Augmented Generation (RAG) system for translating user questions into accurate SPARQL queries over bioinformatics knowledge graphs (KGs) To enhance accuracy and reduce hallucinations in query generation, our system utilise metadata from the KGs, including query examples and schema information, and incorporates a validation step to correct generated queries. The system is available online at chat.expasy.org.
  arXiv  Detail & Related papers  (2024-10-08T14:09:12Z)
• Integrating SPARQL and LLMs for Question Answering over Scholarly Data Sources [0.0]
  This paper describes a methodology that combines SPARQL queries, divide and conquer algorithms, and a pre-trained extractive question answering model. It starts with SPARQL queries to gather data, then applies divide and conquer to manage various question types and sources, and uses the model to handle personal author questions. The approach, evaluated with Exact Match and F-score metrics, shows promise for improving QA accuracy and efficiency in scholarly contexts.
  arXiv  Detail & Related papers  (2024-09-11T14:50:28Z)
• Leveraging LLMs in Scholarly Knowledge Graph Question Answering [7.951847862547378]
  KGQA answers natural language questions by leveraging a large language model (LLM) Our system achieves an F1 score of 99.0% on SciQA - one of the Scholarly Knowledge Graph Question Answering challenge benchmarks.
  arXiv  Detail & Related papers  (2023-11-16T12:13:49Z)
• Decomposing Complex Queries for Tip-of-the-tongue Retrieval [72.07449449115167]
  Complex queries describe content elements (e.g., book characters or events), information beyond the document text. This retrieval setting, called tip of the tongue (TOT), is especially challenging for models reliant on lexical and semantic overlap between query and document text. We introduce a simple yet effective framework for handling such complex queries by decomposing the query into individual clues, routing those as sub-queries to specialized retrievers, and ensembling the results.
  arXiv  Detail & Related papers  (2023-05-24T11:43:40Z)
• UniKGQA: Unified Retrieval and Reasoning for Solving Multi-hop Question Answering Over Knowledge Graph [89.98762327725112]
  Multi-hop Question Answering over Knowledge Graph(KGQA) aims to find the answer entities that are multiple hops away from the topic entities mentioned in a natural language question. We propose UniKGQA, a novel approach for multi-hop KGQA task, by unifying retrieval and reasoning in both model architecture and parameter learning.
  arXiv  Detail & Related papers  (2022-12-02T04:08:09Z)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Open-Retrieval Conversational Question Answering [62.11228261293487]
  We introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers. We build an end-to-end system for ORConvQA, featuring a retriever, a reranker, and a reader that are all based on Transformers.
  arXiv  Detail & Related papers  (2020-05-22T19:39:50Z)

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.