14 papers
SemStruct: Contextualizing Semantic Embeddings with Structural Information for Schema Matching
Inwon Kang, Kavitha Srinivas, Nandana Mihindukulasooriya +4
Schema matching is a fundamental step in integrating heterogeneous data sources. While Pre-trained Language Models (PLMs) have revolutionized this task by capturing linguistic sema…
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
Liane Vogel, Kavitha Srinivas, Niharika D'Souza +3
Tabular foundation models aim to learn universal representations of tabular data that transfer across tasks and domains, enabling applications such as table retrieval, semantic sea…
Agentic Control Center for Data Product Optimization
Priyadarshini Tamilselvan, Gregory Bramble, Sola Shirai +3
Data products enable end users to gain greater insights about their data by providing supporting assets, such as example question-SQL pairs which can be answered using the data or…
Automatic Prompt Engineering with No Task Cues and No Tuning
Faisal Chowdhury, Nandana Mihindukulasooriya, Niharika S D'Souza +4
This paper presents a system for automatic prompt engineering that is much simpler in both design and application and yet as effective as the existing approaches. It requires no tu…
Bridging Business Intent and Data: A Benchmark for Automatic Relational Data Product Generation
Faisal Chowdhury, Sola Shirai, Sarthak Dash +2
A data product is designed to address a specific business need by transforming raw data into a curated, usable asset that delivers actionable insights. Despite practical advances i…
Evaluating Joinable Column Discovery Approaches for Context-Aware Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
Joinable Column Discovery is a critical challenge in automating enterprise data analysis. While existing approaches focus on syntactic overlap and semantic similarity, there remain…
DPDisc: From Factoid Questions to Data Product Requests for Open-World Data Product Discovery over Tables and Text
Liangliang Zhang, Nandana Mihindukulasooriya, Niharika S. D'Souza +4
Data products are reusable, self-contained assets designed for specific business use cases. Automating their discovery is of great industry interest, as it enables efficient data a…
StructText: A Synthetic Table-to-Text Approach for Benchmark Generation with Multi-Dimensional Evaluation
Satyananda Kashyap, Sola Shirai, Nandana Mihindukulasooriya +1
Extracting structured information from text, such as key-value pairs that could augment tabular data, is quite useful in many enterprise use cases. Although large language models (…
TOPJoin: A Context-Aware Multi-Criteria Approach for Joinable Column Search
Harsha Kokel, Aamod Khatiwada, Tejaswini Pedapati +4
One of the major challenges in enterprise data analysis is the task of finding joinable tables that are conceptually related and provide meaningful insights. Traditionally, joinabl…
Automatic Prompt Optimization for Knowledge Graph Construction: Insights from an Empirical Study
Nandana Mihindukulasooriya, Niharika S. D'Souza, Faisal Chowdhury +1
A KG represents a network of entities and illustrates relationships between them. KGs are used for various applications, including semantic search and discovery, reasoning, decisio…
Knowledge Base Construction for Knowledge-Augmented Text-to-SQL
Jinheon Baek, Horst Samulowitz, Oktie Hassanzadeh +4
Text-to-SQL aims to translate natural language queries into SQL statements, which is practical as it enables anyone to easily retrieve the desired information from databases. Recen…
Can Cross Encoders Produce Useful Sentence Embeddings?
Haritha Ananthakrishnan, Julian Dolby, Harsha Kokel +2
Cross encoders (CEs) are trained with sentence pairs to detect relatedness. As CEs require sentence pairs at inference, the prevailing view is that they can only be used as re-rank…
TabSketchFM: Sketch-based Tabular Representation Learning for Data Discovery over Data Lakes
Aamod Khatiwada, Harsha Kokel, Ibrahim Abdelaziz +7
Enterprises have a growing need to identify relevant tables in data lakes; e.g. tables that are unionable, joinable, or subsets of each other. Tabular neural models can be helpful…
Matching Table Metadata with Business Glossaries Using Large Language Models
Elita Lobo, Oktie Hassanzadeh, Nhan Pham +3
Enterprises often own large collections of structured data in the form of large databases or an enterprise data lake. Such data collections come with limited metadata and strict ac…