Papers Column Type Annotation
“Column Type Annotation” 태그가 달린 논문 38편 · 필터 해제
Interpretable Column Annotation with LLM-Symbolized Decision Process Materialization
Column annotation (CA), including column type annotation (CTA) and column property annotation (CPA), aims to identify the meanings of table columns and the semantic relationships among them. Recent CA methods usually use…
Column Type AnnotationLakeHopper: Cross Data Lakes Column Type Annotation through Model Adaptation
Column type annotation is vital for tasks like data cleaning, integration, and visualization. Recent solutions rely on resource-intensive language models fine-tuned on well-annotated columns from a particular set of tabl…
Column Type AnnotationRobust LLM-based Column Type Annotation via Prompt Augmentation with LoRA Tuning
Column Type Annotation (CTA) is a fundamental step towards enabling schema alignment and semantic understanding of tabular data. Existing encoder-only language models achieve high accuracy when fine-tuned on labeled colu…
Column Type AnnotationAn LLM Agent-Based Complex Semantic Table Annotation Approach
The Semantic Table Annotation (STA) task, which includes Column Type Annotation (CTA) and Cell Entity Annotation (CEA), maps table contents to ontology entities and plays important roles in various semantic applications.…
Cell Entity AnnotationColumn Type AnnotationEvaluating Knowledge Generation and Self-Refinement Strategies for LLM-based Column Type Annotation
Understanding the semantics of columns in relational tables is an important pre-processing step for indexing data lakes in order to provide rich data search. An approach to establishing such understanding is column type …
Column Type AnnotationIn-Context LearningColumn Property Annotation using Large Language Models
Column property annotation (CPA), also known as column relationship prediction, is the task of predicting the semantic relationship between two columns in a table given a set of candidate relationships. CPA annotations a…
Columns Property AnnotationColumn Type AnnotationData IntegrationIn-Context Learning+1ACCIO: Table Understanding Enhanced via Contrastive Learning with Aggregations
The attention to table understanding using recent natural language models has been growing. However, most related works tend to focus on learning the structure of the table directly. Just as humans improve their understa…
Column Type AnnotationContrastive LearningRACOON: An LLM-based Framework for Retrieval-Augmented Column Type Annotation with a Knowledge Graph
As an important component of data exploration and integration, Column Type Annotation (CTA) aims to label columns of a table with one or more semantic types. With the recent development of Large Language Models (LLMs), r…
Column Type AnnotationRetrievalKGLink: A column type annotation method that combines knowledge graph and pre-trained language model
The semantic annotation of tabular data plays a crucial role in various downstream tasks. Previous research has proposed knowledge graph (KG)-based and deep learning-based methods, each with its inherent limitations. KG-…
Column Type AnnotationDeep LearningLanguage ModelingLanguage ModellingWatchog: A Light-weight Contrastive Learning based Framework for Column Annotation
Relational Web tables provide valuable resources for numerous downstream applications, making table understanding, especially column annotation that identifies semantic types and relations of columns, a hot topic in the …
BenchmarkingColumns Property AnnotationColumn Type AnnotationContrastive LearningTorchicTab: Semantic Table Annotation with Wikidata and Language Models
An abundance of tabular data exists and is used by a wide range of applications. However, a big portion of these data lack the semantic information necessary for users and machines to properly understand them. This lack …
Columns Property AnnotationColumn Type AnnotationGraph MatchingTable annotationSemantic Annotation of Tabular Data for Machine-to-Machine Interoperability via Neuro-Symbolic Anchoring
In this paper we investigate automated annotation of tabular data using semantic technologies in combination with neural network embedding. Specifically, we propose an anchoring model in which property and cell types fro…
Columns Property AnnotationColumn Type AnnotationNetwork EmbeddingDREIFLUSS: A Minimalist Approach for Table Matching
This paper introduces DREIFLUSS, an innovative, minimalist approach designed to tackle the Column Type Annotation (CTA) and Column Property Annotation (CPA) tasks in the SemTab challenge. DREIFLUSS efficiently employs se…
Columns Property AnnotationColumn Type AnnotationData IntegrationKnowledge GraphsExploring Naive Bayes Classifiers for Tabular Data to Knowledge Graph Matching
The present research investigates the use of Naive Bayes classifiers to match knowledge graphs and tabular data, with particular emphasis on Column Type Annotation, Cell Entity Annotation, Column Property Annotation and …
Cell Entity AnnotationColumns Property AnnotationColumn Type AnnotationGraph Matching+1TableLlama: Towards Open Large Generalist Models for Tables
Semi-structured tables are ubiquitous. There has been a variety of tasks that aim to automatically interpret, augment, and query tables. Current methods often require pretraining on tables or special model architecture d…
Column Type AnnotationArcheType: A Novel Framework for Open-Source Column Type Annotation using Large Language Models
Existing deep-learning approaches to semantic column type annotation (CTA) have important shortcomings: they rely on semantic types which are fixed at training time; require a large number of training samples per type an…
Column Type AnnotationTable annotationzero-shot-classificationZero-Shot LearningAdversarial Attacks on Tables with Entity Swap
The capabilities of large language models (LLMs) have been successfully applied in the context of table representation learning. The recently proposed tabular language models have reported state-of-the-art results across…
Column Type AnnotationRepresentation LearningCHORUS: Foundation Models for Unified Data Discovery and Exploration
We apply foundation models to data discovery and exploration tasks. Foundation models include large language models (LLMs) that show promising performance on a range of diverse tasks unrelated to their training. We show …
AllColumn Type AnnotationManagementColumn Type Annotation using ChatGPT
Column type annotation is the task of annotating the columns of a relational table with the semantic type of the values contained in each column. Column type annotation is an important pre-processing step for data search…
Column Type AnnotationData IntegrationTable annotationSOTAB: The WDC Schema.org Table Annotation Benchmark
Understanding the semantics of table elements is a prerequisite for many data integration and data discovery tasks. Table annotation is the task of labeling table elements with terms from a given vocabulary. This paper p…
Columns Property AnnotationColumn Type AnnotationData IntegrationMissing Values+1