  Table is a popular data format to organize and present relational
information. Users often have to manually compose tables when gathering their
desiderate information (e.g., entities and their attributes) for decision
making. In this work, we propose to resolve a new type of heterogeneous query
viz: tabular query, which contains a natural language query description, column
names of the desired table, and an example row. We aim to acquire more entity
tuples (rows) and automatically fill the table specified by the tabular query.
We design a novel framework AutoTableComplete which aims to integrate schema
specific structural information with the natural language contextual
information provided by the user, to complete tables automatically, using a
heterogeneous knowledge base (KB) as the main information source. Given a
tabular query as input, our framework first constructs a set of candidate
chains that connect the given example entities in KB. We learn to select the
best matching chain from these candidates using the semantic context from
tabular query. The selected chain is then converted into a SPARQL query,
executed against KB to gather a set of candidate rows, that are then ranked in
order of their relevance to the tabular query, to complete the desired table.
We construct a new dataset based on tables in Wikipedia pages and Freebase,
using which we perform a wide range of experiments to demonstrate the
effectiveness of AutoTableComplete as well as present a detailed error analysis
of our method.
