Entity-Relationship Search over the Web
Pedro Saleiro, Natasa Milic-Frayling, Eduarda Mendes Rodrigues, Carlos, Soares

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…
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Taxonomy
TopicsTopic Modeling · Data Quality and Management · Information Retrieval and Search Behavior
