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检索条件"主题词=Structured Query Language"

共 1711 条记录，以下是31-40 订阅

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BIS: NL2SQL Service Evaluation Benchmark for Business Intelligence Scenarios 22nd

BIS: NL2SQL Service Evaluation Benchmark for Business Intell...

引用

22nd International Conference on Service Oriented Computing

作者： Caglayan, Bora Wang, Mingxue Kelleher, John D. Fei, Shen Tong, Gui Ding, Jiandong Zhang, Puchao Huawei Ireland Res Ctr Dublin Ireland Trinity Coll Dublin ADAPT Res Ctr Sch Comp Sci & Stat Dublin Ireland Huawei Technol Co Ltd Shenzhen Peoples R China

ISBN: (纸本)9789819608072;9789819608089

NL2SQL (Natural language to structured query language) transformation has seen wide adoption in Business Intelligence (BI) applications in recent years. However, existing NL2SQL benchmarks are not suitable for production BI scenarios, as they are not designed for common business intelligence questions. To address this gap, we have developed a new benchmark focused on typical NL questions in industrial BI scenarios. We discuss the challenges of constructing a BI -focused benchmark and the shortcomings of existing benchmarks. Additionally, we introduce question categories in our benchmark that reflect common BI inquiries. Lastly, we propose two novel semantic similarity evaluation metrics for assessing NL2SQL capabilities in BI applications and services.

关键词： structured query language

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Construction of query Classification System and query Classification Corpus for Chinese Intent Recognition 25th

Construction of Query Classification System and Query Classi...

引用

25th Workshop on Chinese Lexical Semantics, CLSW 2024

作者： Yu, Shan Liu, Hua Liu, Pengyuan College of Chinese Language and Culture Jinan University Guangzhou China College of Information Science Beijing Language and Culture University Beijing China

ISBN: (纸本)9789819635115

This paper starts from the need for intent recognition in human-machine dialogue. To train a more general and transferable Chinese intent recognition model, we propose a compromise query classification system suitable for task-oriented dialogue scenarios based on a multi-concept semantic representation framework. This system consists of 4 major categories and 27 subcategories, taking into account syntax, semantics, and pragmatics, and has high coverage. Based on this system, a multi-domain query type annotation corpus was constructed through manual annotation and automatic machine verification, comprising 88,420 queries from three different domains. Finally, in-domain and cross-domain query classification experiments were performed on Bert and Text-CNN models, which had been specifically trained using a query-type annotated corpus. The results indicate that the trained Bert and Text-CNN classification models achieved high accuracy in both in-domain and cross-domain datasets, demonstrating good transferability and generality. However, due to uneven data distribution, some cross-domain query classification tasks show lower accuracy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： structured query language

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Personalized query Engine Optimization for Link Traversal-Based query Processing over structured Decentralized Environments

Personalized Query Engine Optimization for Link Traversal-Ba...

引用

21st International Conference on The Semantic Web (ESWC)

作者： Eschauzier, Ruben Univ Ghent Imec Ghent Belgium

ISBN: (纸本)9783031789540;9783031789557

The scale of decentralization envisioned for the presently centralized web requires querying approaches that can query numerous small data sources instead of a few large ones. Link Traversal-based query Processing (LTQP) is a promising candidate for querying highly decentralized environments that executes queries with zero knowledge of the queried data and discovers data sources on the fly. However, as the engine does not know in advance what data will be queried, creating an optimized query plan before executing the query is challenging. Presently, LTQP is employed for client-side querying, where one engine instance services a single client. Despite this, current engines do not utilize client-specific engine query usage patterns to implement personalized query optimization algorithms. This paper will describe the proposed research approach for implementing personalized query optimization techniques, such as caching or learned query optimizers, for LTQP. The objective is to improve query optimization algorithms through the analysis of historical query engine usage, instead of depending on additional prior information. Personalized optimization will be based on existing work in SPARQL optimization literature and fundamental database theory, adapted to LTQP, and aimed at repeating their success in reducing query execution time. As a result, query engines will gain the capability to query large decentralized environments, enabling applications to function within this emerging decentralized web landscape.

关键词： structured query language

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query Performance Prediction: Theory, Techniques and Applications 25

Query Performance Prediction: Theory, Techniques and Applica...

引用

18th ACM International Conference on Web Search and Data Mining, WSDM 2025

作者： Arabzadeh, Negar Meng, Chuan Aliannejadi, Mohammad Bagheri, Ebrahim University of Waterloo Waterloo Canada University of Amsterdam Amsterdam Netherlands Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9798400713293

query performance prediction (QPP) is a key task in information retrieval (IR), focusing on estimating the retrieval quality of a given query without relying on human-labeled relevance judgments. Over the decades, QPP has gained increasing significance, with a surge in research activity in recent years. It has proven to benefit various aspects of retrieval, such as optimizing retrieval effectiveness by selecting the most appropriate ranking function for each query. Despite its critical role, there were only a few tutorials that cover the QPP techniques. The topic is even playing a more important role in the new era of pre-trained and large language models (LLMs), and the emerging fields of multi-agent intelligent systems and conversational search (CS). Moreover, while research in QPP has yielded promising outcomes, studies on its practical application and integration into real-world search engines remain limited. This tutorial has four main objectives. First, it aims to cover both the fundamentals and the latest advancements in QPP methods. Second, it broadens the scope of QPP beyond ad-hoc search to various search scenarios, e.g., CS and image search. Third, this tutorial provides a comprehensive review of QPP applications across various aspects of IR, providing insights on where and how to apply QPP in practice. Fourth, we equip participants with hands-on materials, enabling them to apply QPP implementation in practice. This tutorial seeks to benefit both researchers and practitioners in IR, encouraging further exploration and innovation in QPP. © 2025 Copyright held by the owner/author(s).

关键词： structured query language

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LLM driven Natural language to SQL for Utility Dashboards

LLM driven Natural Language to SQL for Utility Dashboards

引用

2025 IEEE International Students' Conference on Electrical, Electronics and Computer Science, SCEECS 2025

作者： Banerji, Jyotirmoy Amin, Abhiraj Rathor, Kapil Mukesh Patel School of Technology Management and Engineering Dept. Artificial Intelligence Mumbai India

ISBN: (纸本)9798331529833

Advancements in Natural language to structured query language (NL2SQL) systems are improving the accessibility of data stored in relational databases, making it easier for non-technical users to retrieve information using simple language queries. In this paper, we propose an NL2SQL system specifically designed for IoT-driven plant data applications, integrating Large language Models (LLMs) with a multi-step training and evaluation process. This approach involves developing a context table and accompanying tag datasheet to represent IoT-specific data attributes, fine-tuning a LLM to enhance SQL query generation, and evaluating model performance using the ChatGPT API, comparing generated queries against standard metrics from the Spider dataset. Our methodology aims to address the complexities inherent in NL2SQL tasks involving IoT datasets, where queries are often complex and context specific. This research contributes a scalable and adaptable NL2SQL approach which is suited for IoT data environments. © 2025 IEEE.

关键词： structured query language

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query Learning of Context-Deterministic and Congruential Context-Free languages over Infinite Alphabets 50th

Query Learning of Context-Deterministic and Congruential C...

引用

50th International Conference on Current Trends in Theory and Practice of Computer Science, SOFSEM 2025

作者： Numaya, Yutaro Kawasaki, Yoshito Yoshinaka, Ryo Shinohara, Ayumi Tohoku University Sendai Japan

ISBN: (纸本)9783031826962

This paper is concerned with learning some context-free languages (cfls) over infinite alphabets from membership and equivalence queries. Argyros and D’Antoni (2018) proposed a query learning algorithm for regular languages over infinite alphabets using deterministic symbolic automata. Our algorithms learn context-deterministic cfls and congruential cfls over infinite alphabets. We enhance the existing algorithms for learning those cfl classes over finite alphabets by adapting Argyros and D’Antoni’s technique. Our result shows that their technique can be extended to learn richer classes beyond regular languages. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： structured query language

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Compact query Rewritings for Ontology-Based query Answering 19th

Compact Query Rewritings for Ontology-Based Query Answering

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19th International Summer School on Reasoning Web, RW 2023

作者： Thomazo, Michaël Inria DI ENS ENS CNRS University PSL Paris France

ISBN: (纸本)9783031802829

Ontology-based query answering is an important reasoning problem at the core of logic-based data integration. Formally, the problem takes as input a database instance I, a set of existential rules R, and a Boolean conjunctive query q, and asks whether I,R⊧q. The query entailment problem thus defined is usually reduced towards a query evaluation problem, either through materialization of entailed atoms, or through query rewriting. In these lecture notes, we present a couple of query rewriting algorithms, and discuss an approach whose aim is to avoid a blow-up in the size of the rewritings in cases that are typically present in real-world, namely in the presence of (large) role and concepts hierarchies. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： structured query language

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LEAP: LLM-Powered End-to-End Automatic Library for Processing Social Science Queries on Unstructured Data 51st

LEAP: LLM-Powered End-to-End Automatic Library for Processin...

引用

51st International Conference on Very Large Data Bases, VLDB 2025

作者： Hu, Chuxuan Peters, Austin Kang, Daniel UIUC Urbana IL United States University of Chicago Chicago IL United States

Social scientists are increasingly interested in analyzing the semantic information (e.g., emotion) of unstructured data (e.g., Tweets), where the semantic information is not natively present. Performing this analysis in a cost-efficient manner requires using machine learning (ML) models to extract the semantic information and subsequently analyze the now structured data. However, this process remains challenging for domain experts. To demonstrate the challenges in social science analytics, we collect a dataset, QUIET-ML, of 120 real-world social science queries in natural language and their ground truth answers. Existing systems struggle with these queries since (1) they require selecting and applying ML models, and (2) more than a quarter of these queries are vague, making standard tools like natural language to SQL systems unsuited. To address these issues, we develop LEAP, an end-to-end library that answers social science queries in natural language with ML. LEAP filters vague queries to ensure that the answers are deterministic and selects from internally supported and user-defined ML functions to extend the unstructured data to structured tables with necessary annotations. LEAP further generates and executes code to respond to these natural language queries. LEAP achieves a 100% pass @ 3 and 92% pass @ 1 on QUIET-ML, with a $1.06 average end-to-end cost, of which code generation costs $0.02. © 2025, VLDB Endowment. All rights reserved.

关键词： structured query language

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In-Context Reinforcement Learning based Retrieval-Augmented Generation for Text-to-SQL 31

In-Context Reinforcement Learning based Retrieval-Augmented ...

引用

31st International Conference on Computational Linguistics, COLING 2025

作者： Toteja, Rishit Sarkar, Arindam Comar, Prakash Mandayam Amazon United States

ISBN: (纸本)9798891761964

Text-to-SQL simplifies database interactions by enabling non-experts to convert their natural language (NL) questions to structured query language (SQL) queries. With advancements in Large language Models (LLM), in-context learning (ICL) has emerged as a popular choice for building Text-to-SQL systems. Real world, industry-scale databases, often comprise thousands of tables and hundreds of columns, and makes passing the entire schema as context to an LLM infeasibly expensive. This requisites access to the correct database and the set of tables. Recently Retrieval Augmented Generation (RAG) based methods have been proposed for retrieving relevant subset of databases and tables for a given query. However, we observe that the existing methods of synthetic query generation can generate predominantly simple queries which might not be sufficiently representative of complex, real world queries, thus, negatively affecting the quality of the generated SQL. To address this, we propose an innovative in-context reinforcement learning (ICRL) based framework which refines the question generation process by enhancing the model's ability to produce intricate queries that practitioners may pose during inference. In contrast to the existing approaches, our framework ensures the generation of synthetic SQL queries which are diverse and complex. We demonstrate the effectiveness of our approach via multiple experiments comparing against the representative state-of-the-art models on public benchmark datasets and observe substantial improvements in performance and scalability. Our method achieves 15-20% higher recall in database/table retrieval task compared to the existing state-of-the-art models for schema identification and upto 2% higher execution accuracy for SQL generation. © 2025 Association for Computational Linguistics.

关键词： structured query language

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Gen-SQL: Efficient Text-to-SQL By Bridging Natural language Question And Database Schema With Pseudo-Schema 31

Gen-SQL: Efficient Text-to-SQL By Bridging Natural Language ...

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31st International Conference on Computational Linguistics, COLING 2025

作者： Shi, Jie Xu, Bo Liang, Jiaqing Xiao, Yanghua Chen, Jia Xie, Chenhao Wang, Peng Wang, Wei Shanghai Key Laboratory of Data Science School of Computer Science Fudan University China School of Computer Science and Technology Donghua University China School of Data Science Fudan University China SenseDeal Intelligent Technology Co. Ltd. China

ISBN: (纸本)9798891761964

With the prevalence of Large language Models (LLMs), recent studies have shifted paradigms and leveraged LLMs to tackle the challenging task of Text-to-SQL. Because of the complexity of real world databases, previous works adopt the retrieve-then-generate framework to retrieve relevant database schema and then to generate the SQL query. However, efficient embedding-based retriever suffers from lower retrieval accuracy, and more accurate LLM-based retriever is far more expensive to use, which hinders their applicability for broader applications. To overcome this issue, this paper proposes Gen-SQL, a novel generate-ground-regenerate framework, where we exploit prior knowledge from the LLM to enhance embedding-based retriever and reduce cost. Experiments on several datasets are conducted to demonstrate the effectiveness and scalability of our proposed method. We release our code and data at https://***/jieshi10/gensql. © 2025 Association for Computational Linguistics.

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