Relation Schema Induction (RSI) is the problem of identifying type signatures
of arguments of relations from unlabeled text. Most of the previous work in
this area have focused only on binary RSI, i.e., inducing only the subject and
object type signatures per relation. However, in practice, many relations are
high-order, i.e., they have more than two arguments and inducing type
signatures of all arguments is necessary. For example, in the sports domain,
inducing a schema win(WinningPlayer, OpponentPlayer, Tournament, Location) is
more informative than inducing just win(WinningPlayer, OpponentPlayer). We
refer to this problem as Higher-order Relation Schema Induction (HRSI). In this
paper, we propose Tensor Factorization with Back-off and Aggregation (TFBA), a
novel framework for the HRSI problem. To the best of our knowledge, this is the
first attempt at inducing higher-order relation schemata from unlabeled text.
Using the experimental analysis on three real world datasets, we show how TFBA
helps in dealing with sparsity and induce higher order schemata.