Query reformulation graph.
Rahman et al.
Query reformulation graph. Dive into the research topics of 'Query Reformulation for Descriptive Queries of Jargon Words Using a Knowledge Graph based on a Dictionary'. Abstract We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. Given a query issued by the Existing question answering and query reformulation datasets, such as MS-MARCO and HotpotQA, primarily focus on human-readable outputs, making them less directly applicable to knowledge graph retrieval, particularly in the context of Retrieval-Augmented Generation (RAG). This makes it In particular, we analyse either bug report contents or the results retrieved by them, employ graph-based term weighting, and then identify important keywords from them for query reformulation as follows: Nov 1, 2022 · Request PDF | Graph-based query reformulation system for descriptive queries of jargon words using definitions | In information retrieval, the lexical mismatch is a common problem where texts in The hybrid graph, created by consolidating the query community and query-flow graphs, takes into account the lexical similarity as well as the reformulation diversity to suggest queries. By constructing a graph that integrates information from multiple videos with visual features, we enhance the precision and comprehensiveness of query responses, while accommodating input video A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. , supergraphs) of the original query to facilitate the exploration of the results. Dec 10, 2010 · We annotate two large query-flow graphs with reformulation type information, and run several graph-characterization experiments on these graphs, extracting new insights about the relationships between the different query reformulation types. Given a query issued by the user, the system, apart from returning the result patterns, also proposes a number of specializations (i. Complete retrieval pipeline, from query reformulation to LLM-based filtering and final generation. Feb 4, 2025 · Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents. Semantic similarity is computed with the user query as a reference, which might be suboptimal: for instance, the user query will often be a question and the document containing the true answer will be in affirmative voice, so its Feb 4, 2025 · Efficient Graph-Based Reasoning: Optimizing graph-based workflows for large-scale, real-world applications. , method signatures) to our technique for automatic Download scientific diagram | The reformulated query is evidence of dissatisfaction even though the search results of initial query received some clicks. A sub-graph of query 6 from MEDLINE collection that corresponds to the query terms We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Oct 9, 2025 · In this paper, we propose ConvGQR, a new framework to reformulate conversational queries based on generative pre-trained language models (PLMs), one for query rewriting and another for generating potential answers. Aug 10, 2015 · Request PDF | Graph Query Reformulation with Diversity | We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Finally we study query recommendations based on short random walks on the query-flow graphs. Dec 13, 2024 · It leverages a knowledge graph to conduct query searches, incorporating relevant knowledge from the knowledge graph and user query patterns. Then, we apply an absorbing random walk on the query-reformulation graph and model the document utility with the transition probability from initial query to the satisfactory document. You should have notions from this other cookbook first! Reminder: Retrieval-Augmented-Generation (RAG) is “using an LLM to answer a user query, but basing the answer on information retrieved from a knowledge Oct 19, 2018 · Query reformulation, including query recommendation and query auto-completion, is a popular add-on feature of search engines, which provide related and helpful reformulations of a keyword query. In this paper we attempt to solve the information-overload problem in graph databases by proposing an exploratory approach where the user starts with a query to the graph database, and the system assists her by showing various reformu-lations of the query originally issued. •Transforming users’ descriptive queries into appropriate jargon queries. Abstract. Figure | describes our proposed method for query expansion that uses graph matching between DBpedia and UMLS ontologies as well as degree centrality. The most expressive fragment of RDF for which Reformulation-based quey answering exists Oct 2, 2024 · Previous research has explored various approaches, such as utilizing Bayesian perspectives and graph-based approaches to mine query-query correlations leveraging query-document click-through data, which lay the groundwork for query rewriting using customer interaction data. 564 We anno-tate two large query-flow graphs with reformulation type information, and run several graph-characterization experiments on these graphs, extracting new insights about the relationships between the different query reformulation types. This technique can improve user interactions, save time in technical domains, and optimize the performance. Graph Query Reformulation with Diversity Davide Mottin, University of Trento Francesco Bonchi, Yahoo Labs - Francesco Gullo, Yahoo Labs 1Graph Query R formulation w th Diversity – Davide Mottin, Francesco Bonchi, Francesco Gullo GitHub is where people build software. Jan 1, 2025 · Knowledge graph-based framework for query reformulation in ad-hoc retrieval. t. A sub-graph of query 6 from MEDLINE collection that corresponds to the query terms May 13, 2024 · A widely-investigated OMQA technique is FO-rewriting: every query asked on a KB is reformulated w. Query reformulation is the process of refining search queries to clarify the user's information needs, especially in cases of ambiguity, by offering alternative interpretations or categories for selection. 🔍 Query Reformulation System A C++-based Information Retrieval system that reformulates search queries using semantic closeness and graph-based expansion, powered by TF-IDF. 854 Kipf Krovetz, Viewing morphology as an inference process, с. Download Table | Examples of reformulations from publication: Query reformulation mining: Models, patterns, and applications | Understanding query reformulation patterns is a key task towards next Therefore, we can find the descriptive phrase in a query using the distances between nodes. The framework of our work consists of two steps: inference and selection, as illustrated in Fig. Aug 10, 2015 · We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. Agentic RAG: turbocharge your RAG with query reformulation and self-query! 🚀 Authored by: Aymeric Roucher This tutorial is advanced. Referring to knowledge graph embedding methods, we use the accuracy of intentions prediction to evaluate experimental results. e. Challenges •The number of reformulation is exponential •Quantify the interestingness of a reformulation •Finding query reformulations is NP-complete 8Graph Query Reformulation with Diversity –Davide Mottin, Francesco Bonchi, Francesco Gullo Our Approach Graph Query Reformulation with Diversity • Finds kmeaningfulspecializations First, we train a graph neural network to represent the relational properties between words and to infer a jargon word using compositional information of the descriptive query's words. Human-AI Synergy: Designing intuitive interfaces and workflows to empower humans to interact effectively with Agentic RAG systems. A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. [26] incorporates context-aware query reformulation into bug localization, using a graph-based approach to represent bug reports and employing PageRank to identify im-portant tokens for queries, demonstrating some improvements. Jul 17, 2024 · In this paper, we introduce GuideCQR, a framework that refines queries for CQR by leveraging key information from the initially retrieved documents. Dec 31, 2022 · Highlights•Novel graph-based QR system using a dictionary without any manually annotated data. Thus, transforming users' descriptive queries into appropriate Query reformulation (QR) is one of the techniques that overcome the lexical gaps by transforming users’ descriptive queries into appropriate jargon queries. Through query reformulation May 28, 2015 · In this paper, we first construct a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node. In this work, we introduce a novel knowledge graph-enhanced search engine aimed at facilitating knowledge exploration. Example 1. Mar 7, 2025 · To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. Relevant documents are highlighted in red. This paper aims at formulating a new form of query expansion by integrating and matching between two external resources: DBpedia and UMLS (Figure 1). We construct a graph representation of the query on the knowledge graph, transforming query reconstruction into graph-based inference. This paper builds a query-reformulation graph that consists of query nodes, satisfactory document nodes, and interruption node, and model the document utility with the transition probability from initial query to the satisfactory document, which significantly outperformed the state-of-the-art methods in recommending high utility queries. In particular, when searching for jargon, people tend to use descriptive queries, such as "a medical examination of the colon" rather than "colonoscopy," or they often use them interchangeably. The goal of pseudo-query refor-mulation is to, given a seed query q0 by a user, automatically navigate to a better query (Color figure online). May 25, 2019 · Query answering in RDF knowledge bases has traditionally been performed either through graph saturation, i. In particular, we analyse either bug report contents or the results retrieved by them, employ graph-based term weighting, and then identify important keywords from them for query reformulation as follows: Oct 26, 2021 · Request PDF | On Oct 26, 2021, Bosung Kim and others published Query Reformulation for Descriptive Queries of Jargon Words Using a Knowledge Graph based on a Dictionary | Find, read and cite all 北京大学软件工程研究中心 - 引用次数：422 次 Query reformulation (QR) is one of the techniques that overcome the lexical gaps by transforming users’ descriptive queries into appropriate jargon queries. It’s dual-channel architecture, which combines graph-based textual grounding with multimodal context encoding. Edges exist between nodes whose queries are simple reformulations of each other. query-specific information retrieval. Answering queries on RDF knowledge bases is a crucial data management task, usually performed through either graph saturation or query reformulation. Semantic Scholar extracted view of "Query specific graph-based query reformulation using UMLS for clinical information access" by Jainisha Sankhavara et al. In this paper, we propose a novel technique–ACER–for automatic query reformulation for concept location in the context of software change tasks. Query recommendation is an essential part of modern But vanilla RAG has limitations, most importantly these two: It performs only one retrieval step: if the results are bad, the generation in turn will be bad. 191 Kumaran, Effective and efficient user interaction for long queries, с. •Prediction of the corresponding jargon word using a graph from a dictionary. In this short paper, we optimize our recent state-of-the-art query reformulation technique for RDF graphs with RDFS ontologies [2], and we report on preliminary encouraging experiments showing performance improvement by up to two orders Fig. Recently, there have been a few works that leverage knowledge graph embeddings (KGE) for tasks related to query suggestion [15]–[17]. Together they form a unique fingerprint. Moreover, we propose a graph search model that finds the target node in real time using the relevance scores of neighborhood nodes. Jun 2, 2019 · This paper addresses the general query answering problem by reducing it, through a pre-query reformulation step, to that solved by the query reformulation technique of [13], and reports on experiments demonstrating the low cost of the reformulation algorithm. Due to the dropping prices of smartphones and the increasing coverage and bandwidth of mobile networks, a large percentage of search engine queries are issued from mobile devices. Additionally, to further enhance query utilization, we implement a learning-to-rank model that leverages key features such as class name match score and call graph score. Our experiments clearly show that our method achieves the state of the art on three benchmark KBQA datasets. Understanding query reformulation patterns is a key task towards next generation web search engines. AI generated definition based on: Designing the Search Experience, 2013 While query reformulation has been studied in contexts such as relational databases [18, 21], keyword search on structured data [34], or web search [9], to the best of our knowledge, this is the first work that focuses on query reformulation in graph databases. Our query-reformulation graph consistsofquerynodes,satisfactorydocumentsnodes,and an interruption node while session-flow graph consists of querynodes,documentsnodes,andfailurenodes. and studies for query reformulation. Fig. Aug 10, 2015 · We formalize the problem of finding a set of reformulations of the input query by maximizing a linear combination of coverage (of the original query's answer set) and diversity among the specializations. Mar 15, 2023 · Query reformulation (QR) is one of the techniques that overcome the lexical gaps by transforming users’ descriptive queries into appropriate jargon queries. This study proposes a novel graph-based QR system that uses a dictionary, where the model does not require manually annotated data. , adding all implicit This paper aims at formulating a new form of query expansion by integrating and matching between two external resources: DBpedia and UMLS (Figure 1). Nodes represent queries and associated retrieved results. Aug 10, 2015 · Read "Graph Query Reformulation with Diversity" on DeepDyve, the largest online rental service for scholarly research with thousands of academic publications available at your fingertips. Jun 24, 2024 · Query reformulation involves refining and clarifying user queries to enhance the accuracy and relevance of responses from AI systems like ChatGPT or Claude. Oct 7, 2025 · VideoRAG, and how it extends traditional RAG to multi-hour videos. Other than return the exact match of the query, users also want the search engine to help them do some knowledge exploration, i. Adding the Query Rewrite Node The Query Rewrite Node rewrites the original questions, using a prompt designed for query reformulation. Recommending High Utility Queries via Query-Reformulation Graph JianGuo Wang,1,2 Joshua Zhexue Huang,1,3and Dingming Wu3 Dec 4, 2024 · To address inaccuracies in queries, we introduce a user and conversational-based query reformulation approach, termed LLmiRQ+. The findings from our analyses provide potential implications for model design of task-based search engines. , modifying the query to look for the explicit triples entailing Oct 9, 2025 · Motivated by the observation that early incorporation of constraints into query graphs can more effectively prune the search space, we propose a modified staged query graph generation method with more flexible ways to generate query graphs. We propose a query reformulation method based on graph exploration to expand the knowledge domain of the query while ensuring relevance. Query reformulation as graph search. In contrast, our work focuses on leveraging an ex-ternal knowledge graph embedding. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects. - "Query Reformulation for Descriptive Queries of Jargon Words Using a Knowledge Graph based on a Dictionary" This paper proposes a novel utility query recommendation approach based on absorbing random walk on the session-flow graph, which can learn queries' utility by simultaneously modeling both users' reformulation behaviors and click behaviors. JD. . By combining both, ConvGQR can produce better search queries. Query recommendation is an essential part of modern In contrast, our work focuses on leveraging an ex-ternal knowledge graph embedding. If we can do that, then we can build systems able to understand and possibly predict user intent, providing the needed assistance at the right time, Second, although there has been intensive research on query reformulation, we provide a new insight into the variation of query reformulation strategies. We (1) first introduce a novel graph-based term weight –CodeRank– for identifying important terms from the source code, and then (2) apply that term weight and source document structures (e. The latter are analysed in relation with search episode size (Short, Medium and Long) and search stage (Start, Middle and End) from two different viewpoints (stream of query history and the We study a problem of graph-query reformulation enabling explorative query-driven discovery in graph databases. This guide explores key techniques, implementation strategies, and best practices for optimizing retrieval, refining responses, and enhancing AI-driven workflows. Our extensive experiments across 5 query sets with different query topics and 10 languages from 7 language families using 2 neural machine translators demonstrated the effectiveness of our proposed method in enhancing rag’s retrieval in com-parison with existing unsupervised query expanders. g. Dec 13, 2024 · Search engines are typically used to acquire desired knowledge. Query reformulation (QR) is a key factor in overcoming the problems faced by the lexical chasm in information retrieval (IR) systems. Feb 8, 2025 · In this paper, we propose a novel Knowledge Graph-Guided Retrieval Augmented Generation (KG 2 RAG) framework that utilizes knowledge graphs (KGs) to provide fact-level relationships between chunks, improving the diversity and coherence of the retrieved results. May 31, 2025 · A tutorial on building an advanced agentic RAG workflow that combines query routing, document grading, and query rewriting using LangGraph to create a robust, self-correcting retrieval system. from publication: Recommending High Nov 22, 2024 · A minimal example of an agentic RAG could improve the user query, e. In this paper, we present a new conversational query reformulation framework, ConvGQR, which integrates query rewriting and query expansion toward generating more effective search queries through a new knowledge infusion mechanism. , adding all implicit triples to the graph, or through query reformulation, i. 1. Jun 1, 2020 · This query reformulation approach is compared with baseline, pseudo relevance feedback based query expansion approach and state-of-the-art UMLS based query reformulation approaches. Query recommendation is an integral part of modern search engines that helps users find their information needs. Key results and ablation insights showing how graph reasoning and visual grounding boost comprehension. However, in this paper, we analyze and conclude that if query expansion is used judiciously, it can also lead to significant improvement in image retrieval. In query-reformulation graph, query 1 node is linkedtoquery2nodeifquery2isreformulatedbyanyuser forasamesearchintentafterformulatingquery1,whilein session-flowgraph,query1nodeislinkedtoquery2node We annotate two large query-flow graphs with reformulation type information, and run several graph-character-ization experiments on these graphs, extracting new insights about the relationships between the different query reformulation types. Query answering in RDF knowledge bases has traditionally been performed either through graph saturation, i. Rahman et al. Mar 15, 2023 · Query reformulation (QR) is one of the techniques that overcome the lexical gaps by transforming users’ descriptive queries into appropriate jargon queries. Using insights from large-scale search logs, our findings clearly show that task is an additional relevant search unit that helps better understanding user’s query reformulation patterns and predicting the next user’s query. - GitHub - asinghcsu/AgenticRAG-Survey: Agentic-RAG explores advanced Retrieval-Augmented Generation systems enhanced with AI LLM agents. , getting some related by not exactly matched results. The proposed method considers UMLS entities from a query with their related entities identified by UMLS and constructs a query-specific graph of biomedical entities for term selection. At last, we Mar 15, 2023 · In this study, we propose a new approach making use of a definition of jargon word from a dictionary, which is high-quality and easily accessible, to construct a knowledge graph for query reformulation. This paper explores the usage and impact of UMLS for entity-based query reformulation in biomedical document retrieval. Aug 1, 2020 · Biomedical document retrieval requires entity level processing instead of term level. Exploring hybrid approaches that combine graph-based reasoning with neural networks. com - Cited by 1,715 - Information Retrieval - Large Language Models Feb 1, 2017 · Query reformulation using query expansion has been successfully applied to text retrieval. Download scientific diagram | An example of the query-reformulation graph. We construct a new corpus of shopping search query log and create a query reformulation graph based on this dataset. 2. Mar 18, 2025 · Adaptive RAG systems leverage LangChain & LangGraph to improve accuracy and efficiency. the KB's ontology, so that its answers are computed by the relational evaluation of the query reformulation on the KB's database. 11 Kumaran, Reducing long queries using query quality predictors, с. Traditional query recommendation methods Oct 30, 2021 · Query reformulation (QR) is a key factor in overcoming the problems faced by the lexical chasm in information retrieval (IR) systems. The agent could then retrieve documents from a vector database based on the improved query, and generate a response. , modifying the query to look for the explicit triples entailing precisely what the orig-inal query asks for. r. Query Reformulation. Kim, Query reformulation for descriptive queries of jargon words using a knowledge graph based on a dictionary, с. by fixing typos, expanding it with synonyms, or even generating a new query based on the original one. from publication: Recommending High Utility Queries via Query-Reformulation Graph | Query recommendation is an essential Jun 25, 2020 · A novel graph-based approach for query reformulation using UMLS is described herein which queries are expanded using biomedical entities. dg15z5i xvm4g dohw nnz wcnyx dkov4cie x8 iieqee xhur5dcv rqqqz
Back to Top
