Papers Column Type Annotation
“Column Type Annotation” 태그가 달린 논문 38편 · 필터 해제
KGCODE-Tab Results for SemTab 2022
This paper presents the results of KGCODE-Tab in the tabular data to knowledge graph matching contest SemTab 2022. As an efficient tabular data linking system, KGCODE-Tab is intended to participate in three tasks of the …
Cell Entity AnnotationColumns Property AnnotationColumn Type AnnotationGraph MatchingFrom Heuristics to Language Models: A Journey Through the Universe of Semantic Table Interpretation with DAGOBAH
This paper presents DAGOBAH SL 2022, a semantic table interpretation system that has been continuously improved over the last four years when participating in the SemTab challenge. This year, we have improved the lookup …
Cell Entity AnnotationColumn Type AnnotationResults of SemTab 2022
SemTab 2022 was the fourth edition of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching, successfully collocated with the 21st International Semantic Web Conference (ISWC) and the 17th Ontology Match…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingOntology MatchingTowards an Approach based on Knowledge Graph Refinement for Tabular Data to Knowledge Graph Matching
This paper presents our contribution to the Accuracy Track of Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab). This contribution consists of the proposition of an approach based on knowledge g…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingPositionPermutation Invariant Strategy Using Transformer Encoders for Table Understanding
Representing text in tables is essential for many business intelligence tasks such as semantic retrieval, data exploration and visualization, and question answering. Existing methods that leverage pretrained Transformer …
Column Type AnnotationEntity LinkingQuestion AnsweringRelation Extraction+2DAGOBAH: Table and Graph Contexts for Efficient Semantic Annotation of Tabular Data
In this paper, we present the latest improvements of the DAGOBAH system that performs automatic pre-processing and semantic interpretation of tables. In particular, we report promising results obtained in the SemTab 2021…
Cell Entity AnnotationColumn Type AnnotationTable annotationKepler-aSI at SemTab 2021
In this paper, we present our system Kepler-aSI, for the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2021). This system is participating for the second time in this campaign, bringing impro…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingKnowledge Graphs+1MAGIC: Mining an Augmented Graph using INK, starting from a CSV
A large portion of structured data does not yet reap the benefits of the Semantic Web. Therefore, The “Tabular Data to Knowledge Graph Matching” competition at ISWC tries to bridge this gap by evaluating and promoting th…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingTable annotationJenTab Meets SemTab 2021's New Challenges
While tables are a rich source of structured information, their automated use is oftentimes prevented by the inherent ambiguity contained within. Issues ranging from mere typos over inconsistent naming conventions to hom…
Cell Entity AnnotationColumn Type AnnotationGraph MatchingTable annotationTABBIE: Pretrained Representations of Tabular Data
Existing work on tabular representation learning jointly models tables and associated text using self-supervised objective functions derived from pretrained language models such as BERT. While this joint pretraining impr…
Cell DetectionColumn Type AnnotationRepresentation LearningTable annotationAnnotating Columns with Pre-trained Language Models
Inferring meta information about tables, such as column headers or relationships between columns, is an active research topic in data management as we find many tables are missing some of this information. In this paper,…
Columns Property AnnotationColumn Type AnnotationManagementMulti-Task Learning+3AMALGAM: A Matching Approach to fairfy tabuLar data with knowledGe grAph Model
In this paper we present AMALGAM, a matching approach to fairify tabular data with the use of a knowledge graph. The ultimate goal is to provide fast and efficient approach to annotate tabular data with entities from a b…
Column Type AnnotationGraph MatchingTough Tables: Carefully Evaluating Entity Linking for Tabular Data
Table annotation is a key task to improve querying the Web and support the Knowledge Graph population from legacy sources (tables). Last year, the SemTab challenge was introduced to unify different efforts to evaluate tabl…
Cell Entity AnnotationColumn Type AnnotationEntity LinkingKnowledge Graphs+1TURL: Table Understanding through Representation Learning
Relational tables on the Web store a vast amount of knowledge. Owing to the wealth of such tables, there has been tremendous progress on a variety of tasks in the area of table understanding. However, existing work gener…
Cell Entity AnnotationColumns Property AnnotationColumn Type AnnotationRelation Extraction+2Sato: Contextual Semantic Type Detection in Tables
Detecting the semantic types of data columns in relational tables is important for various data preparation and information retrieval tasks such as data cleaning, schema matching, data discovery, and semantic search. How…
Column Type AnnotationHybrid Machine LearningInformation RetrievalRetrieval+2Learning Semantic Annotations for Tabular Data
The usefulness of tabular data such as web tables critically depends on understanding their semantics. This study focuses on column type prediction for tables without any meta data. Unlike traditional lexical matching-ba…
Column Type AnnotationPredictionTable annotationType predictionSherlock: A Deep Learning Approach to Semantic Data Type Detection
Correctly detecting the semantic type of data columns is crucial for data science tasks such as automated data cleaning, schema matching, and data discovery. Existing data preparation and analysis systems rely on diction…
Column Type AnnotationDeep LearningTable annotationVocal Bursts Type Prediction+1ColNet: Embedding the Semantics of Web Tables for Column Type Prediction
Automatically annotating column types with knowledge base (KB) concepts is a critical task to gain a basic understanding of web tables. Current methods rely on either table metadata like column name or entity corresponde…
Column Type AnnotationTable annotationType predictionVocal Bursts Type Prediction