Detecting Opportunities for Differential Maintenance of Extracted Views

TL;DR

This paper formalizes and proposes algorithms for maintaining consistency of extracted views from source documents in semi-structured data, inspired by relational view maintenance and applied to document spanners.

Contribution

It introduces a formal classification of document updates and algorithms for detecting pseudo-irrelevant updates in the context of information extraction using document spanners.

Findings

Classifies document updates into irrelevant, autonomously computable, and pseudo-irrelevant.

Provides algorithms to detect pseudo-irrelevant updates.

Extends view maintenance concepts to semi-structured data extraction.

Abstract

Semi-structured and unstructured data management is challenging, but many of the problems encountered are analogous to problems already addressed in the relational context. In the area of information extraction, for example, the shift from engineering ad hoc, application-specific extraction rules towards using expressive languages such as CPSL and AQL creates opportunities to propose solutions that can be applied to a wide range of extraction programs. In this work, we focus on extracted view maintenance, a problem that is well-motivated and thoroughly addressed in the relational setting. In particular, we formalize and address the problem of keeping extracted relations consistent with source documents that can be arbitrarily updated. We formally characterize three classes of document updates, namely those that are irrelevant, autonomously computable, and pseudo-irrelevant with respect to a given extractor. Finally, we propose algorithms to detect pseudo-irrelevant document updates with respect to extractors that are expressed as document spanners, a model of information extraction inspired by SystemT.