Tootfinder

Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
Osman Erman Gungor, Derak Paulsen, William Kang
https://arxiv.org/abs/2507.14376

Schemora: schema matching via multi-stage recommendation and metadata enrichment using off-the-shelf llms
Schema matching is essential for integrating heterogeneous data sources and enhancing dataset discovery, yet it remains a complex and resource-intensive problem. We introduce SCHEMORA, a schema matching framework that combines large language models with hybrid retrieval techniques in a prompt-based approach, enabling efficient identification of candidate matches without relying on labeled training data or exhaustive pairwise comparisons. By enriching schema metadata and leveraging both vector-b…

You did what? I made this:
https://chromewebstore.google.com/detail/simons-schema-analyser/hkphgihmimhbmmgcjeahdcpgahebpjnd?authuser=0&hl=en-GB

TASER: Table Agents for Schema-guided Extraction and Recommendation
Nicole Cho, Kirsty Fielding, William Watson, Sumitra Ganesh, Manuela Veloso
https://arxiv.org/abs/2508.13404 …

TASER: Table Agents for Schema-guided Extraction and Recommendation
Real-world financial documents report essential information about an entity's financial holdings that can span millions of different financial instrument types. Yet, these details are often buried in messy, multi-page, fragmented tables - for example, 99.4% of the tables in our dataset have no bounding boxes with the maximum number of rows amounting to 426 per table across 44 pages. To tackle these unique challenges from real-world tables, we present a continuously learning, agentic table extra…

📊 Significantly reduces project initialization time from hours to minutes while maintaining full type safety between frontend and backend layers
🤖 Integrates seamlessly with #AI code generation tools for automated schema and API route creation
https://…

Conceptual and Design Principles for a Self-Referential Algorithm Mimicking Neuronal Assembly Functions
Paolo Totaro, Alberto Mangiante
https://arxiv.org/abs/2507.14011

Conceptual and Design Principles for a Self-Referential Algorithm Mimicking Neuronal Assembly Functions
This article proposes a method to formalise models of cognitive processes grounded in experience, considering experience from the perspective of a living system and not from that of an observer of the living system. The perspective of a living system is defined by the need of the system to preserve the vital equilibria. The method is based on an algorithmic schema that we call Environment Generative Operator (EGO) and uses a self-referential language developed for this purpose which we call E-l…

On Website Technicals (2021-11) - Tech updates: more meters, reutils tweets upgrade, DB-based Event and Product schema.org. - https://www.earth.org.uk/note-on-site-technicals-54.html

On Website Technicals (2021-11)
Tech updates: more meters, reutils tweets upgrade, DB-based Event and Product schema.org.

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Shaoming Duan, Zirui Wang, Chuanyi Liu, Zhibin Zhu, Yuhao Zhang, Peiyi Han, Liang Yan, Zewu Penge
https://arxiv.org/abs/2508.12769

CRED-SQL: Enhancing Real-world Large Scale Database Text-to-SQL Parsing through Cluster Retrieval and Execution Description
Recent advances in large language models (LLMs) have significantly improved the accuracy of Text-to-SQL systems. However, a critical challenge remains: the semantic mismatch between natural language questions (NLQs) and their corresponding SQL queries. This issue is exacerbated in large-scale databases, where semantically similar attributes hinder schema linking and semantic drift during SQL generation, ultimately reducing model accuracy. To address these challenges, we introduce CRED-SQL, a fr…

LLMATCH: A Unified Schema Matching Framework with Large Language Models
Sha Wang, Yuchen Li, Hanhua Xiao, Bing Tian Dai, Roy Ka-Wei Lee, Yanfei Dong, Lambert Deng
https://arxiv.org/abs/2507.10897

LLMATCH: A Unified Schema Matching Framework with Large Language Models
Schema matching is a foundational task in enterprise data integration, aiming to align disparate data sources. While traditional methods handle simple one-to-one table mappings, they often struggle with complex multi-table schema matching in real-world applications. We present LLMatch, a unified and modular schema matching framework. LLMatch decomposes schema matching into three distinct stages: schema preparation, table-candidate selection, and column-level alignment, enabling component-level …

A Comparative Study of Delta Parquet, Iceberg, and Hudi for Automotive Data Engineering Use Cases
Dinesh Eswararaj, Ajay Babu Nellipudi, Vandana Kollati
https://arxiv.org/abs/2508.13396

A Comparative Study of Delta Parquet, Iceberg, and Hudi for Automotive Data Engineering Use Cases
The automotive industry generates vast amounts of data from sensors, telemetry, diagnostics, and real-time operations. Efficient data engineering is critical to handle challenges of latency, scalability, and consistency. Modern data lakehouse formats Delta Parquet, Apache Iceberg, and Apache Hudi offer features such as ACID transactions, schema enforcement, and real-time ingestion, combining the strengths of data lakes and warehouses to support complex use cases. This study presents a comparati…

Maya-Tupi graphs: a generalization of split graphs
J\'ulio Ara\'ujo, C\'esar Hern\'andez-Cruz, Cl\'audia Linhares
https://arxiv.org/abs/2508.13424 https://…

Maya-Tupi graphs: a generalization of split graphs
We define the family of Maya-Tupi graphs as those graphs that admit a partition $(A,B)$ of their vertex sets such that $A$ induces a complete multipartite graph where each part has size at most two, and $B$ induces a graph where every connected component is $K_1$ or $K_2$. The family of Maya-Tupi graphs is self complementary, generalizes split graphs, falls into the sparse-dense partitioning schema and is characterized by finitely many forbidden induced subgraphs. Unfortunately, our computation…

Bootify
#SpringBoot

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
John Alderete, Macarious Kin Fung Hui, Aanchan Mohan
https://arxiv.org/abs/2508.13060

Evaluating ASR robustness to spontaneous speech errors: A study of WhisperX using a Speech Error Database
The Simon Fraser University Speech Error Database (SFUSED) is a public data collection developed for linguistic and psycholinguistic research. Here we demonstrate how its design and annotations can be used to test and evaluate speech recognition models. The database comprises systematically annotated speech errors from spontaneous English speech, with each error tagged for intended and actual error productions. The annotation schema incorporates multiple classificatory dimensions that are of so…

Human-AI Schema Discovery and Application for Creative Problem Solving
Sitong Wang
https://arxiv.org/abs/2508.05045 https://arxiv.org/pdf/2508.05045…

Human-AI Schema Discovery and Application for Creative Problem Solving
Humans often rely on underlying structural patterns-schemas-to create, whether by writing stories, designing software, or composing music. Schemas help organize ideas and guide exploration, but they are often difficult to discover and apply, especially in complex or unfamiliar domains. My Ph.D. research develops a framework for human-AI schema discovery and application to support creative problem solving. I design systems that support users in sensemaking over examples to abstract schemas, and …

Well this has been fun!
Experimenting building a chrome extension for detecting schema markup on a webpage.

Diese Habsburger, ey. Seit Jahrhunderten nur Ärger. Der nächste will dann vermutlich auch das Schloss zurück ...

oh, i almost forgot:
- #PHP is great
- #JSON schema validation is great: https://github.com/opis/json-schema

Return to monk

A Schema.org Mapping for Brazilian Legal Norms: Toward Interoperable Legal Graphs and Open Government Data
Hudson de Martim
https://arxiv.org/abs/2508.00827 https://

A Schema.org Mapping for Brazilian Legal Norms: Toward Interoperable Legal Graphs and Open Government Data
Open Government Data (OGD) initiatives aim to enhance transparency and public participation by making government data openly accessible. However, structuring legal norms for machine readability remains a critical challenge for advancing Legal Tech applications such as Legal Knowledge Graphs (LKGs). Focusing on the Normas.leg.br portal initiative by the Brazilian National Congress, we propose a unified mapping of Brazilian legislation to the schema.org/Legislation vocabulary via JSON-LD and Link…

PIMBS: Efficient Body Schema Learning for Musculoskeletal Humanoids with Physics-Informed Neural Networks
Kento Kawaharazuka, Takahiro Hattori, Keita Yoneda, Kei Okada
https://arxiv.org/abs/2506.20343

PIMBS: Efficient Body Schema Learning for Musculoskeletal Humanoids with Physics-Informed Neural Networks
Musculoskeletal humanoids are robots that closely mimic the human musculoskeletal system, offering various advantages such as variable stiffness control, redundancy, and flexibility. However, their body structure is complex, and muscle paths often significantly deviate from geometric models. To address this, numerous studies have been conducted to learn body schema, particularly the relationships among joint angles, muscle tension, and muscle length. These studies typically rely solely on data …

AI-assisted JSON Schema Creation and Mapping
Felix Neubauer, J\"urgen Pleiss, Benjamin Uekermann
https://arxiv.org/abs/2508.05192 https://arxiv.org/pd…

AI-assisted JSON Schema Creation and Mapping
Model-Driven Engineering (MDE) places models at the core of system and data engineering processes. In the context of research data, these models are typically expressed as schemas that define the structure and semantics of datasets. However, many domains still lack standardized models, and creating them remains a significant barrier, especially for non-experts. We present a hybrid approach that combines large language models (LLMs) with deterministic techniques to enable JSON Schema creation, m…

Android: #Obtanium (instead of #FDroid app) is really awesome for managing app updates.
However, this method has an issue when the naming schema of the #apk files change on the server side, and you're using a…

XML Prompting as Grammar-Constrained Interaction: Fixed-Point Semantics, Convergence Guarantees, and Human-AI Protocols
Faruk Alpay, Taylan Alpay
https://arxiv.org/abs/2509.08182

XML Prompting as Grammar-Constrained Interaction: Fixed-Point Semantics, Convergence Guarantees, and Human-AI Protocols
Structured prompting with XML tags has emerged as an effective way to steer large language models (LLMs) toward parseable, schema-adherent outputs in real-world systems. We develop a logic-first treatment of XML prompting that unifies (i) grammar-constrained decoding, (ii) fixed-point semantics over lattices of hierarchical prompts, and (iii) convergent human-AI interaction loops. We formalize a complete lattice of XML trees under a refinement order and prove that monotone prompt-to-prompt oper…

Disentangling the schema turn: Restoring the information base to conceptual modelling
Chris Partridge, Andrew Mitchell, Sergio de Cesare, Oscar Xiberta Soto
https://arxiv.org/abs/2509.01617

Disentangling the schema turn: Restoring the information base to conceptual modelling
If one looks at contemporary mainstream development practices for conceptual modelling in computer science, these so clearly focus on a conceptual schema completely separated from its information base that the conceptual schema is often just called the conceptual model. These schema-centric practices are crystallized in almost every database textbook. We call this strong, almost universal, bias towards conceptual schemas the schema turn. The focus of this paper is on disentangling this turn wit…

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Xueli Pan, Victor de Boer, Jacco van Ossenbruggen
https://arxiv.org/abs/2508.10467 …

FIRESPARQL: A LLM-based Framework for SPARQL Query Generation over Scholarly Knowledge Graphs
Question answering over Scholarly Knowledge Graphs (SKGs) remains a challenging task due to the complexity of scholarly content and the intricate structure of these graphs. Large Language Model (LLM) approaches could be used to translate natural language questions (NLQs) into SPARQL queries; however, these LLM-based approaches struggle with SPARQL query generation due to limited exposure to SKG-specific content and the underlying schema. We identified two main types of errors in the LLM-generat…

An Agentic Toolkit for Adaptive Information Extraction from Regulatory Documents
Gaye Colakoglu, G\"urkan Solmaz, Jonathan F\"urst
https://arxiv.org/abs/2509.11773 htt…

An Agentic Toolkit for Adaptive Information Extraction from Regulatory Documents
Declaration of Performance (DoP) documents, mandated by EU regulation, certify the performance of construction products. While some of their content is standardized, DoPs vary widely in layout, language, schema, and format, posing challenges for automated key-value pair extraction (KVP) and question answering (QA). Existing static or LLM-only IE pipelines often hallucinate and fail to adapt to this structural diversity. Our domain-specific, stateful agentic system addresses these challenges thr…

Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems
Jie Zeng, Yukiko I. Nakano
https://arxiv.org/abs/2508.20635 https://

Schema-Guided Response Generation using Multi-Frame Dialogue State for Motivational Interviewing Systems
The primary goal of Motivational Interviewing (MI) is to help clients build their own motivation for behavioral change. To support this in dialogue systems, it is essential to guide large language models (LLMs) to generate counselor responses aligned with MI principles. By employing a schema-guided approach, this study proposes a method for updating multi-frame dialogue states and a strategy decision mechanism that dynamically determines the response focus in a manner grounded in MI principles.…

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
Alexander Nikitas Dimopoulos, Joseph Grasso
https://arxiv.org/abs/2508.00822

Cross-Dataset Semantic Segmentation Performance Analysis: Unifying NIST Point Cloud City Datasets for 3D Deep Learning
This study analyzes semantic segmentation performance across heterogeneously labeled point-cloud datasets relevant to public safety applications, including pre-incident planning systems derived from lidar scans. Using NIST's Point Cloud City dataset (Enfield and Memphis collections), we investigate challenges in unifying differently labeled 3D data. Our methodology employs a graded schema with the KPConv architecture, evaluating performance through IoU metrics on safety-relevant features. Resul…

Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools
Prerna Agarwal, Himanshu Gupta, Soujanya Soni, Rohith Vallam, Renuka Sindhgatta, Sameep Mehta
https://arxiv.org/abs/2509.11626

Automated Creation and Enrichment Framework for Improved Invocation of Enterprise APIs as Tools
Recent advancements in Large Language Models (LLMs) has lead to the development of agents capable of complex reasoning and interaction with external tools. In enterprise contexts, the effective use of such tools that are often enabled by application programming interfaces (APIs), is hindered by poor documentation, complex input or output schema, and large number of operations. These challenges make tool selection difficult and reduce the accuracy of payload formation by up to 25%. We propose AC…

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Junaid Ahmed Khan, Andrea Bartolini
https://arxiv.org/abs/2507.06107

A Unified Ontology for Scalable Knowledge Graph-Driven Operational Data Analytics in High-Performance Computing Systems
Modern high-performance computing (HPC) systems generate massive volumes of heterogeneous telemetry data from millions of sensors monitoring compute, memory, power, cooling, and storage subsystems. As HPC infrastructures scale to support increasingly complex workloads-including generative AI-the need for efficient, reliable, and interoperable telemetry analysis becomes critical. Operational Data Analytics (ODA) has emerged to address these demands; however, the reliance on schema-less storage s…

🛠️ Tools include database queries, schema inspection, route listing, #Artisan commands, and log reading capabilities
📋 Support for multiple packages: #Laravel Framework (10.x, 11.x, 12.x), #Livewire

BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
Alejandro Posadas-Nava, Alejandro Carrasco, Richard Linares
https://arxiv.org/abs/2508.09606

BEAVR: Bimanual, multi-Embodiment, Accessible, Virtual Reality Teleoperation System for Robots
\textbf{BEAVR} is an open-source, bimanual, multi-embodiment Virtual Reality (VR) teleoperation system for robots, designed to unify real-time control, data recording, and policy learning across heterogeneous robotic platforms. BEAVR enables real-time, dexterous teleoperation using commodity VR hardware, supports modular integration with robots ranging from 7-DoF manipulators to full-body humanoids, and records synchronized multi-modal demonstrations directly in the LeRobot dataset schema. Our …

Template-Based Schema Matching of Multi-Layout Tenancy Schedules:A Comparative Study of a Template-Based Hybrid Matcher and the ALITE Full Disjunction Model
Tim Uilkema, Yao Ma, Seyed Sahand Mohammadi Ziabari, Joep van Vliet
https://arxiv.org/abs/2507.02020

Template-Based Schema Matching of Multi-Layout Tenancy Schedules:A Comparative Study of a Template-Based Hybrid Matcher and the ALITE Full Disjunction Model
The lack of standardized tabular formats for tenancy schedules across real estate firms creates significant inefficiencies in data integration. Existing automated integration methods, such as Full Disjunction (FD)-based models like ALITE, prioritize completeness but result in schema bloat, sparse attributes and limited business usability. We propose a novel hybrid, template-based schema matcher that aligns multi-layout tenancy schedules to a predefined target schema. The matcher combines schema…

Schema sorted finally!
#11ty
Went for a modular set of includes in the end.

from my link log —
Row polymorphic programming.
https://www.stranger.systems/posts/by-slug/row-polymorphic-programming.html
saved 2025-07-14

Row Polymorphic Programming
Sometimes, especially when dealing with business logic, we have to deal with data that comes in from the real world, or elsewhere, naturally very messily typed, leading to nasty type signatures and messes of macros or code generation to build data structures from a schema. Row polymorphism can help us by abstracting over the fields contained in a record type in the type signature, letting us define records based on data, concatenate records together, and define functions that are generic across…

The Open mulTiwavelength Transient Event Repository (OTTER): Infrastructure Release and Tidal Disruption Event Catalog
Noah Franz, Kate D Alexander, Sebastian Gomez, Collin T Christy, Tanmoy Laskar, Sjoert van Velzen, Nicholas Earl, Suvi Gezari, Mitchell Karmen, Raffaella Margutti, Jeniveve Pearson, V. Ashley Villar, Ann I Zabludoff
https://

The Open mulTiwavelength Transient Event Repository (OTTER): Infrastructure Release and Tidal Disruption Event Catalog
Multiwavelength analyses of astrophysical transients are essential for understanding the physics of these events. To make such analyses more efficient and effective, we present the Open mulTiwavelength Transient Event Repository (OTTER), a publicly available catalog of published transient event metadata and photometry. Unlike previous efforts, our data schema is optimized for the storage of multiwavelength photometric datasets spanning the entire electromagnetic spectrum. Open source software, …

In PostgreSQL, managing schema changes without experiencing downtime can be quite challenging. At this year’s Berlin Buzzwords, Gülçin Yıldırım Jelínek explored locking mechanisms in PostgreSQL, specifically focusing on table-level locks acquired through Data Definition Language (DDL) operations, and discussed various tools and techniques that can help minimise the impact of locking.
Watch the full session:

Evaluating NL2SQL via SQL2NL
Mohammadtaher Safarzadeh, Afshin Oroojlooyjadid, Dan Roth
https://arxiv.org/abs/2509.04657 https://arxiv.org/pdf/2509.04657

Evaluating NL2SQL via SQL2NL
Robust evaluation in the presence of linguistic variation is key to understanding the generalization capabilities of Natural Language to SQL (NL2SQL) models, yet existing benchmarks rarely address this factor in a systematic or controlled manner. We propose a novel schema-aligned paraphrasing framework that leverages SQL-to-NL (SQL2NL) to automatically generate semantically equivalent, lexically diverse queries while maintaining alignment with the original schema and intent. This enables the fi…

🤖 Automate database management tasks - let AI handle queries, table creation & indexing
📊 Generate context-aware application code & tests with real-time database schema understanding
🔄 Dynamic reloading support - update tools without redeploying your application

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Likhitha Annapurna Kavuri, Akshay Mhatre, Akarsh K Nair, Deepti Gupta
https://arxiv.org/abs/2506.16458

SecureFed: A Two-Phase Framework for Detecting Malicious Clients in Federated Learning
Federated Learning (FL) protects data privacy while providing a decentralized method for training models. However, because of the distributed schema, it is susceptible to adversarial clients that could alter results or sabotage model performance. This study presents SecureFed, a two-phase FL framework for identifying and reducing the impact of such attackers. Phase 1 involves collecting model updates from participating clients and applying a dimensionality reduction approach to identify outlier…

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Eden Wu, Dishita G Turakhia, Guande Wu, Christos Koutras, Sarah Keegan, Wenke Liu, Beata Szeitz, David Fenyo, Cl\'audio T. Silva, Juliana Freire
https://arxiv.org/abs/2507.16117

BDIViz: An Interactive Visualization System for Biomedical Schema Matching with LLM-Powered Validation
Biomedical data harmonization is essential for enabling exploratory analyses and meta-studies, but the process of schema matching - identifying semantic correspondences between elements of disparate datasets (schemas) - remains a labor-intensive and error-prone task. Even state-of-the-art automated methods often yield low accuracy when applied to biomedical schemas due to the large number of attributes and nuanced semantic differences between them. We present BDIViz, a novel visual analytics sy…

Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
KMA Solaiman, Bharat Bhargava
https://arxiv.org/abs/2506.20070 https://…

Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
Existing multi-media retrieval models either rely on creating a common subspace with modality-specific representation models or require schema mapping among modalities to measure similarities among multi-media data. Our goal is to avoid the annotation overhead incurred from considering retrieval as a supervised classification task and re-use the pretrained encoders in large language models and vision tasks. We propose "FemmIR", a framework to retrieve multimodal results relevant to information …

The KG-ER Conceptual Schema Language
Enrico Franconi, Beno\^it Groz, Jan Hidders, Nina Pardal, S{\l}awek Staworko, Jan Van den Bussche, Piotr Wieczorek
https://arxiv.org/abs/2508.02548

The KG-ER Conceptual Schema Language
We propose KG-ER, a conceptual schema language for knowledge graphs that describes the structure of knowledge graphs independently of their representation (relational databases, property graphs, RDF) while helping to capture the semantics of the information stored in a knowledge graph.

Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables
Vishakh Padmakumar, Joseph Chee Chang, Kyle Lo, Doug Downey, Aakanksha Naik
https://arxiv.org/abs/2507.19521

Setting The Table with Intent: Intent-aware Schema Generation and Editing for Literature Review Tables
The increasing volume of academic literature makes it essential for researchers to organize, compare, and contrast collections of documents. Large language models (LLMs) can support this process by generating schemas defining shared aspects along which to compare papers. However, progress on schema generation has been slow due to: (i) ambiguity in reference-based evaluations, and (ii) lack of editing/refinement methods. Our work is the first to address both issues. First, we present an approach…

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms
Zurabi Kobaladze, Anna Arnania, Tamar Sanikidze
https://arxiv.org/abs/2508.00013

From Provable Correctness to Probabilistic Generation: A Comparative Review of Program Synthesis Paradigms
Program synthesis--the automated generation of executable code from high-level specifications--has been a central goal of computer science for over fifty years. This thesis provides a comparative literature review of the main paradigms that have shaped the field, tracing its evolution from formal logic based methods to recent advances using large scale neural models. We examine five key approaches: logic based (deductive) synthesis, inductive (example based) synthesis, sketch/schema based synth…

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
Vanessa Figueiredo
https://arxiv.org/abs/2508.21204 https://arxiv.org/…

Fuzzy, Symbolic, and Contextual: Enhancing LLM Instruction via Cognitive Scaffolding
We study how architectural inductive biases influence the cognitive behavior of large language models (LLMs) in instructional dialogue. We introduce a symbolic scaffolding mechanism paired with a short-term memory schema designed to promote adaptive, structured reasoning in Socratic tutoring. Using controlled ablation across five system variants, we evaluate model outputs via expert-designed rubrics covering scaffolding, responsiveness, symbolic reasoning, and conversational memory. We present …

MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction
Zhongqiu Li, Shiquan Wang, Ruiyu Fang, Mengjiao Bao, Zhenhe Wu, Shuangyong Song, Yongxiang Li, Zhongjiang He
https://arxiv.org/abs/2509.09082

MR-UIE: Multi-Perspective Reasoning with Reinforcement Learning for Universal Information Extraction
Large language models (LLMs) demonstrate robust capabilities across diverse research domains. However, their performance in universal information extraction (UIE) remains insufficient, especially when tackling structured output scenarios that involve complex schema descriptions and require multi-step reasoning. While existing approaches enhance the performance of LLMs through in-context learning and instruction tuning, significant limitations nonetheless persist. To enhance the model's generali…

Improving Knowledge Graph Understanding with Contextual Views
Antrea Christou, Cogan Shimizu
https://arxiv.org/abs/2508.02413 https://arxiv.org/pdf/2508.02…

Improving Knowledge Graph Understanding with Contextual Views
Navigating, visualizing, and discovery in graph data is frequently a difficult prospect. This is especially true for knowledge graphs (KGs), due to high number of possible labeled connections to other data. However, KGs are frequently equipped with an ontology as a schema. That is, it informs how the relationships between data may be constrained. This additional information can be leveraged to improve how (knowledge) graph data can be navigated, visualized, or otherwise utilized in a discover…

Illuminating Patterns of Divergence: DataDios SmartDiff for Large-Scale Data Difference Analysis
Aryan Poduri, Yashwant Tailor
https://arxiv.org/abs/2509.00293 https://

Illuminating Patterns of Divergence: DataDios SmartDiff for Large-Scale Data Difference Analysis
Data engineering workflows require reliable differencing across files, databases, and query outputs, yet existing tools falter under schema drift, heterogeneous types, and limited explainability. SmartDiff is a unified system that combines schema-aware mapping, type-specific comparators, and parallel execution. It aligns evolving schemas, compares structured and semi-structured data (strings, numbers, dates, JSON/XML), and clusters results with labels that explain how and why differences occur.…

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Urchade Zaratiana, Gil Pasternak, Oliver Boyd, George Hurn-Maloney, Ash Lewis
https://arxiv.org/abs/2507.18546

GLiNER2: An Efficient Multi-Task Information Extraction System with Schema-Driven Interface
Information extraction (IE) is fundamental to numerous NLP applications, yet existing solutions often require specialized models for different tasks or rely on computationally expensive large language models. We present GLiNER2, a unified framework that enhances the original GLiNER architecture to support named entity recognition, text classification, and hierarchical structured data extraction within a single efficient model. Built pretrained transformer encoder architecture, GLiNER2 maintains…

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
Julia Bartlewski, Christoph Broschinski, Gernot Deinzer, Cornelia Lang, Dirk Pieper, Bianca Schweighofer, Colin Sippl, Lisa-Marie Stein, Alexander Wagner, Silke Weisheit
https://arxiv.org/abs/2506.18517

Cost for research -- how cost data of research can be included in open metadata to be reused and evaluated
The openCost project aims to enhance transparency in research funding by making publication-related costs publicly accessible, following FAIR principles. It introduces a metadata schema for cost data, allowing aggregation and analysis across institutions. The project promotes open access and cost-efficient models, benefiting academic institutions, funders, and policymakers.

SQL-Exchange: Transforming SQL Queries Across Domains
Mohammadreza Daviran, Brian Lin, Davood Rafiei
https://arxiv.org/abs/2508.07087 https://arxiv.org/pdf…

SQL-Exchange: Transforming SQL Queries Across Domains
We introduce SQL-Exchange, a framework for mapping SQL queries across different database schemas by preserving the source query structure while adapting domain-specific elements to align with the target schema. We investigate the conditions under which such mappings are feasible and beneficial, and examine their impact on enhancing the in-context learning performance of text-to-SQL systems as a downstream task. Our comprehensive evaluation across multiple model families and benchmark datasets--…

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
Yifu Liu, Yin Zhu, Yingqi Gao, Zhiling Luo, Xiaoxia Li, Xiaorong Shi, Yuntao Hong, Jinyang Gao, Yu Li, Bolin Ding, Jingren Zhou
https://arxiv.org/abs/2507.04701

XiYan-SQL: A Novel Multi-Generator Framework For Text-to-SQL
To leverage the advantages of LLM in addressing challenges in the Text-to-SQL task, we present XiYan-SQL, an innovative framework effectively generating and utilizing multiple SQL candidates. It consists of three components: 1) a Schema Filter module filtering and obtaining multiple relevant schemas; 2) a multi-generator ensemble approach generating multiple highquality and diverse SQL queries; 3) a selection model with a candidate reorganization strategy implemented to obtain the optimal SQL q…

🔧 Strict-Mode Function Calling
Beta API now supports strict-mode for function calling, ensuring the output always complies with the defined JSON schema.
🔗 Anthropic API Compatibility
The API is now compatible with Anthropic API, allowing seamless integration with Claude Code.
💰 Pricing Adjustment (Effective Sept. 5, 2025, 16:00 UTC)
Input tokens: $ 0.07/million (cache hit) / $ 0.56/million (cache miss)
Output tokens: $ 1.68/million
The off-peak discount e…

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Yushi Sun, Kai Sun, Yifan Ethan Xu, Xiao Yang, Xin Luna Dong, Nan Tang, Lei Chen
https://arxiv.org/abs/2509.04716

KERAG: Knowledge-Enhanced Retrieval-Augmented Generation for Advanced Question Answering
Retrieval-Augmented Generation (RAG) mitigates hallucination in Large Language Models (LLMs) by incorporating external data, with Knowledge Graphs (KGs) offering crucial information for question answering. Traditional Knowledge Graph Question Answering (KGQA) methods rely on semantic parsing, which typically retrieves knowledge strictly necessary for answer generation, thus often suffer from low coverage due to rigid schema requirements and semantic ambiguity. We present KERAG, a novel KG-based…

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Meriem Mastouri, Emna Ksontini, Wael Kessentini
https://arxiv.org/abs/2507.16044

Making REST APIs Agent-Ready: From OpenAPI to Model Context Protocol Servers for Tool-Augmented LLMs
Large Language Models (LLMs) are evolving from passive text generators into active agents that invoke external tools. To support this shift, scalable protocols for tool integration are essential. The Model Context Protocol (MCP), introduced by Anthropic in 2024, offers a schema-driven standard for dynamic tool discovery and invocation. Yet, building MCP servers remains manual and repetitive, requiring developers to write glue code, handle authentication, and configure schemas by hand-replicatin…

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Chirag Seth, Utkarsh Singh
https://arxiv.org/abs/2508.04623 https://

Lightweight Transformers for Zero-Shot and Fine-Tuned Text-to-SQL Generation Using Spider
Text-to-SQL translation enables non-expert users to query relational databases using natural language, with applications in education and business intelligence. This study evaluates three lightweight transformer models - T5-Small, BART-Small, and GPT-2 - on the Spider dataset, focusing on low-resource settings. We developed a reusable, model-agnostic pipeline that tailors schema formatting to each model's architecture, training them across 1000 to 5000 iterations and evaluating on 1000 test sam…

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
Timoteo Kelly, Abdulkadir Korkmaz, Samuel Mallet, Connor Souders, Sadra Aliakbarpour, Praveen Rao
https://arxiv.org/abs/2506.19268

HARPT: A Corpus for Analyzing Consumers' Trust and Privacy Concerns in Mobile Health Apps
We present HARPT, a large-scale annotated corpus of mobile health app store reviews aimed at advancing research in user privacy and trust. The dataset comprises over 480,000 user reviews labeled into seven categories that capture critical aspects of trust in applications, trust in providers and privacy concerns. Creating HARPT required addressing multiple complexities, such as defining a nuanced label schema, isolating relevant content from large volumes of noisy data, and designing an annotati…

Data-Aware Socratic Query Refinement in Database Systems
Ruiyuan Zhang, Chrysanthi Kosyfaki, Xiaofang Zhou
https://arxiv.org/abs/2508.05061 https://arxiv.o…

Data-Aware Socratic Query Refinement in Database Systems
In this paper, we propose Data-Aware Socratic Guidance (DASG), a dialogue-based query enhancement framework that embeds \linebreak interactive clarification as a first-class operator within database systems to resolve ambiguity in natural language queries. DASG treats dialogue as an optimization decision, asking clarifying questions only when the expected execution cost reduction exceeds the interaction overhead. The system quantifies ambiguity through linguistic fuzziness, schema grounding con…

Replaced article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Enhancing Text2Cypher with Schema Filtering
Makbule Gulcin Ozsoy
https://

Crosslisted article(s) found for cs.DB. https://arxiv.org/list/cs.DB/new
[1/1]:
- Robust Detection of Synthetic Tabular Data under Schema Variability
G. Charbel N. Kindji (MALT), Elisa Fromont (MALT), Lina Maria Rojas-Barahona, Tanguy Urvoy

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler
https://arxiv.org/abs/2509.00581 https://

SQL-of-Thought: Multi-agentic Text-to-SQL with Guided Error Correction
Converting natural language queries into SQL queries is a crucial challenge in both industry and academia, aiming to increase access to databases and large-scale applications. This work examines how in-context learning and chain-of-thought can be utilized to develop a robust solution for text-to-SQL systems. We propose SQL-of-Thought: a multi-agent framework that decomposes the Text2SQL task into schema linking, subproblem identification, query plan generation, SQL generation, and a guided corr…

Evaluating Structured Decoding for Text-to-Table Generation: Evidence from Three Datasets
Julian Oestreich, Lydia M\"uller
https://arxiv.org/abs/2508.15910 https://

Evaluating Structured Decoding for Text-to-Table Generation: Evidence from Three Datasets
We present a comprehensive evaluation of structured decoding for text-to-table generation with large language models (LLMs). While previous work has primarily focused on unconstrained generation of tables, the impact of enforcing structural constraints during generation remains underexplored. We systematically compare schema-guided (structured) decoding to standard one-shot prompting across three diverse benchmarks - E2E, Rotowire, and Livesum - using open-source LLMs of up to 32B parameters, a…

AI-Driven Generation of Data Contracts in Modern Data Engineering Systems
Harshraj Bhoite
https://arxiv.org/abs/2507.21056 https://arxiv.org/pdf/2507.21056…

AI-Driven Generation of Data Contracts in Modern Data Engineering Systems
Data contracts formalize agreements between data producers and consumers regarding schema, semantics, and quality expectations. As data pipelines grow in complexity, manual authoring and maintenance of contracts becomes error-prone and labor-intensive. We present an AI-driven framework for automatic data contract generation using large language models (LLMs). Our system leverages parameter-efficient fine-tuning methods, including LoRA and PEFT, to adapt LLMs to structured data domains. The mode…