Higher-order Relation Schema Induction using Tensor Factorization with Back-off and Aggregation

TL;DR

This paper introduces TFBA, a novel tensor factorization framework for higher-order relation schema induction from unlabeled text, addressing the challenge of inducing complex relation argument types beyond binary relations.

Contribution

The paper presents the first method for higher-order relation schema induction using tensor factorization with back-off and aggregation techniques.

Findings

TFBA effectively handles data sparsity in higher-order relation induction.

TFBA successfully induces complex relation schemata in real-world datasets.

The approach outperforms existing methods in higher-order relation schema induction.

Abstract

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.