Existing approaches for table annotation with entities and types either capture the structure of table using graphical models , or learn embeddings of table entries without accounting for the complete syntactic structure . We propose TabGCN , which uses Graph Convolutional Networks to capture the complete structure of tables , knowledge graph and the training annotations , and jointly learns embeddings for table elements as well as the entities and types . To account for knowledge incompleteness , TabGCN 's embeddings can be used to discover new entities and types . Using experiments on 5 benchmark datasets , we show that TabGCN significantly outperforms multiple state-of-the-art baselines for table annotation , while showing promising performance on downstream table-related applications . Table data abounds in webpages and organizational documents . Annotation of table entries , such as columns , cells and rows , using available background knowledge ( e.g. Yago , DBPedia , Freebase , etc . ) , such as knowledge of entities and their types , helps in better understanding and semantic interpretation of such tabular data . The challenge , however , is that such web tables do not adhere to any standard format , schema or convention ( Limaye et al . , 2010 ) . Additionally , knowledge graphs are typically incomplete -entities and types mentioned in tables may not always exist in the knowledge graph . Therefore , it becomes necessary to expand the knowledge graph with new entities ( Zhang et al . , 2020 ) and types for annotating tables . Initial research on table annotation ( Limaye et al . , 2010 ; Takeoka et al . , 2019 ; Bhagavatula et al . , 2015 ) used probabilistic graphical models to capture the complete row-column structure of tables and also the knowledge graph for collective annotation . More recent approaches using embeddings ( Gentile et al . , 2017 ; Zhang and Balog , 2018 ; Zhang et al . , 2019 ; Chen et al . , 2019 ; Yin et al . , 2020 ) only partly capture the syntactic structure of tables , and also ignore the structure of the knowledge graph . The problem of incompleteness of the knowledge representation ( Zhang et al . , 2020 ) is mostly not addressed . In this work , we propose the TabGCN model that uses a Graph Convolutional Network ( GCN ) ( Kipf and Welling , 2017 ) to unify the complete syntactic structure of tables ( rows , columns and cells ) and that of the knowledge graph ( entities and types ) via available annotations . The embeddings of the table elements as well as knowledge graph entities and types are trained jointly and end-to-end . While GCNs have been used for learning embeddings for many NLP tasks using the syntactic and semantic structure of natural language sentences ( Marcheggiani and Titov , 2017 ; Vashishth et al . , 2019 ) , encoding tabular structure using GCNs has not been addressed before . The model and embeddings thus trained are used to annotate new tables with known entities and types , while discovering hitherto unseen entities and types . Additionally , we use the trained embeddings for tables and rows for downstream table-related tasks -identifying similar tables , and identifying the appropriate table for any row . We demonstrate these capabilities of TabGCN using experiments on 5 benchmark web table datasets comparing against 5 existing models . We show that WebGCN significantly improves performance for entity and type annotation . For the other tasks , we show that the same embeddings show impressive performance . No existing model can perform all of these tasks . Our contributions are as follows : ( a ) We propose a model called TabGCN based on the GCN architecture that captures the complete syntactic structure of tables as well as the knowledge representation , and learns embeddings of tables , rows , columns and cells , as well as entities and types jointly and in an end-to-end fashion . ( b ) TabGCN addresses incompleteness in the knowledge representation by discovering new entities and types . ( c ) TabGCN significantly outperforms 5 existing approaches in 5 different benchmark datasets for the task of table annotation . ( d ) The trained embeddings show impressive performance in downstream tasks such as identifying similar tables and assignment of rows to appropriate tables . We have proposed a model for that jointly learns representations of tables , rows , columns and cell , as well as entities and types by capturing the complete syntactic structure of all tables , the relevant entities and types and the available annotations using the Graph Convolutional Network . As a result , TabGCN unifies the benefits of probabilistic graphical model based approaches and embedding based approaches for table annotation . Using these embeddings , TabGCN significantly outperforms existing approaches for table annotation , as well as entity and type discovery .

Challenge: Existing embedding-based approaches for table annotation only partially capture the syntactic structure of tables while ignoring the structure of the knowledge graph.
Approach: They propose a graph convolutional network that jointly learns embeddings for table structures, knowledge graphs, and annotations.
Outcome: The proposed model outperforms 5 state-of-the-art baselines for table annotation and other table-related downstream applications on 5 benchmark datasets