Query-Specific Knowledge Graphs for Complex Finance Topics

• URL: http://arxiv.org/abs/2211.04142v1
• Date: Tue, 8 Nov 2022 10:21:13 GMT
• Title: Query-Specific Knowledge Graphs for Complex Finance Topics
• Authors: Iain Mackie and Jeffrey Dalton
• Abstract summary: We focus on the CODEC dataset, where domain experts create challenging questions. We show that state-of-the-art ranking systems have headroom for improvement. We demonstrate that entity and document relevance are positively correlated.
• Score: 6.599344783327053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Across the financial domain, researchers answer complex questions by extensively "searching" for relevant information to generate long-form reports. This workshop paper discusses automating the construction of query-specific document and entity knowledge graphs (KGs) for complex research topics. We focus on the CODEC dataset, where domain experts (1) create challenging questions, (2) construct long natural language narratives, and (3) iteratively search and assess the relevance of documents and entities. For the construction of query-specific KGs, we show that state-of-the-art ranking systems have headroom for improvement, with specific failings due to a lack of context or explicit knowledge representation. We demonstrate that entity and document relevance are positively correlated, and that entity-based query feedback improves document ranking effectiveness. Furthermore, we construct query-specific KGs using retrieval and evaluate using CODEC's "ground-truth graphs", showing the precision and recall trade-offs. Lastly, we point to future work, including adaptive KG retrieval algorithms and GNN-based weighting methods, while highlighting key challenges such as high-quality data, information extraction recall, and the size and sparsity of complex topic graphs.

Related papers

• G-RAG: Knowledge Expansion in Material Science [0.0]
  Graph RAG integrates graph databases to enhance the retrieval process. We implement an agent-based parsing technique to achieve a more detailed representation of the documents.
  arXiv  Detail & Related papers  (2024-11-21T21:22:58Z)
• RiTeK: A Dataset for Large Language Models Complex Reasoning over Textual Knowledge Graphs [12.846097618151951]
  We develop a dataset for LLMs Complex Reasoning over Textual Knowledge Graphs (RiTeK) with a broad topological structure coverage. We synthesize realistic user queries that integrate diverse topological structures, annotated information, and complex textual descriptions. We introduce an enhanced Monte Carlo Tree Search (CTS) method, which automatically extracts relational path information from textual graphs for specific queries.
  arXiv  Detail & Related papers  (2024-10-17T19:33:37Z)
• Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval [49.42043077545341]
  We propose a knowledge-aware query expansion framework, augmenting LLMs with structured document relations from knowledge graph (KG) We leverage document texts as rich KG node representations and use document-based relation filtering for our Knowledge-Aware Retrieval (KAR)
  arXiv  Detail & Related papers  (2024-10-17T17:03:23Z)
• iText2KG: Incremental Knowledge Graphs Construction Using Large Language Models [0.7165255458140439]
  iText2KG is a method for incremental, topic-independent Knowledge Graph construction without post-processing. Our method demonstrates superior performance compared to baseline methods across three scenarios.
  arXiv  Detail & Related papers  (2024-09-05T06:49:14Z)
• HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction [1.0390583509657403]
  We introduce a novel approach based on a combination, called HybridRAG, of the Knowledge Graphs (KGs) based RAG techniques. We show that HybridRAG which retrieves context from both vector database and KG outperforms both traditional VectorRAG and GraphRAG individually. The proposed technique has applications beyond the financial domain.
  arXiv  Detail & Related papers  (2024-08-09T09:07:48Z)
• STaRK: Benchmarking LLM Retrieval on Textual and Relational Knowledge Bases [93.96463520716759]
  We develop STARK, a large-scale Semi-structure retrieval benchmark on Textual and Knowledge Bases. Our benchmark covers three domains: product search, academic paper search, and queries in precision medicine. We design a novel pipeline to synthesize realistic user queries that integrate diverse relational information and complex textual properties.
  arXiv  Detail & Related papers  (2024-04-19T22:54:54Z)
• Improving Retrieval in Theme-specific Applications using a Corpus Topical Taxonomy [52.426623750562335]
  We introduce ToTER (Topical taxonomy Enhanced Retrieval) framework. ToTER identifies the central topics of queries and documents with the guidance of the taxonomy, and exploits their topical relatedness to supplement missing contexts. As a plug-and-play framework, ToTER can be flexibly employed to enhance various PLM-based retrievers.
  arXiv  Detail & Related papers  (2024-03-07T02:34:54Z)
• 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)
• KG-ECO: Knowledge Graph Enhanced Entity Correction for Query Rewriting [15.243664083941287]
  In this work, we propose KG-ECO: Knowledge Graph enhanced Entity COrrection for query rewriting. To boost the model performance, we incorporate Knowledge Graph (KG) to provide entity structural information. Experimental results show that our approach yields a clear performance gain over two baselines.
  arXiv  Detail & Related papers  (2023-02-21T05:42:06Z)
• 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)
• Exposing Query Identification for Search Transparency [69.06545074617685]
  We explore the feasibility of approximate exposing query identification (EQI) as a retrieval task by reversing the role of queries and documents in two classes of search systems. We derive an evaluation metric to measure the quality of a ranking of exposing queries, as well as conducting an empirical analysis focusing on various practical aspects of approximate EQI.
  arXiv  Detail & Related papers  (2021-10-14T20:19:27Z)

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.