Language resources that systematically organize paraphrases for binary relations are of great value for various NLP tasks and have recently been advanced in projects like PATTY, WiseNet and DEFIE.This paper presents a new method for building such a resource and the resource itself, called POLY.Starting with a very large collection of multilingual sentences parsed into triples of phrases, our method clusters relational phrases using probabilistic measures.We judiciously leverage fine-grained semantic typing of relational arguments for identifying synonymous phrases.The evaluation of POLY shows significant improvements in precision and recall over the prior works on PATTY and DEFIE.An extrinsic use case demonstrates the benefits of POLY for question answering.We canonicalize the subject and object arguments

Relational phrases (e.g., "got married to") and their hypernyms (e.g., "is a relative of") are central for many tasks including question answering, open information extraction, paraphrasing, and entailment detection.This has motivated the development of several linguistic resources (e.g.DIRT, PATTY, and WiseNet) which systematically collect and organize relational phrases.These resources have demonstrable practical benefits, but are each limited due to noise, sparsity, or size.We present a new general-purpose method, RELLY, for constructing a large hypernymy graph of relational phrases with high-quality subsumptions using collective probabilistic programming techniques.Our graph induction approach integrates small highprecision knowledge bases together with large automatically curated resources, and reasons collectively to combine these resources into a consistent graph.Using RELLY, we construct a high-coverage, high-precision hypernymy graph consisting of 20K relational phrases and 35K hypernymy links.Our evaluation indicates a hypernymy link precision of 78%, and demonstrates the value of this resource for a document-relevance ranking task.