Entity-Relationship Search over the Web

TL;DR

This paper introduces a novel probabilistic model called ERDM for Entity-Relationship search, capable of handling complex queries involving multiple entities and relationships without relying on predefined types.

Contribution

It presents the first probabilistic framework for E-R search and develops a supervised Early Fusion model using Markov Random Fields for dependency modeling.

Findings

Promising results on large-scale datasets with over 800 million entities and relationships.

Effective E-R search without fixed entity or relationship types.

Validated on three query collections with 469 queries.

Abstract

Entity-Relationship (E-R) Search is a complex case of Entity Search where the goal is to search for multiple unknown entities and relationships connecting them. We assume that a E-R query can be decomposed as a sequence of sub-queries each containing keywords related to a specific entity or relationship. We adopt a probabilistic formulation of the E-R search problem. When creating specific representations for entities (e.g. context terms) and for pairs of entities (i.e. relationships) it is possible to create a graph of probabilistic dependencies between sub-queries and entity plus relationship representations. To the best of our knowledge this represents the first probabilistic model of E-R search. We propose and develop a novel supervised Early Fusion-based model for E-R search, the Entity-Relationship Dependence Model (ERDM). It uses Markov Random Field to model term dependencies of E-R sub-queries and entity/relationship documents. We performed experiments with more than 800M entities and relationships extractions from ClueWeb-09-B with FACC1 entity linking. We obtained promising results using 3 different query collections comprising 469 E-R queries, with results showing that it is possible to perform E-R search without using fix and pre-defined entity and relationship types, enabling a wide range of queries to be addressed.