Automatic Metadata Generation using Associative Networks

TL;DR

This paper presents a novel, content-analysis-independent method for automatic metadata generation that propagates metadata through associative networks built from existing metadata, improving efficiency and applicability across media types.

Contribution

It introduces an associative network-based approach for metadata propagation, avoiding costly content analysis and enabling broad application across resource media.

Findings

Effective metadata propagation demonstrated on bibliographic datasets

Method is computationally inexpensive and media-agnostic

Outperforms traditional content-based metadata generation techniques

Abstract

In spite of its tremendous value, metadata is generally sparse and incomplete, thereby hampering the effectiveness of digital information services. Many of the existing mechanisms for the automated creation of metadata rely primarily on content analysis which can be costly and inefficient. The automatic metadata generation system proposed in this article leverages resource relationships generated from existing metadata as a medium for propagation from metadata-rich to metadata-poor resources. Because of its independence from content analysis, it can be applied to a wide variety of resource media types and is shown to be computationally inexpensive. The proposed method operates through two distinct phases. Occurrence and co-occurrence algorithms first generate an associative network of repository resources leveraging existing repository metadata. Second, using the associative network as a substrate, metadata associated with metadata-rich resources is propagated to metadata-poor resources by means of a discrete-form spreading activation algorithm. This article discusses the general framework for building associative networks, an algorithm for disseminating metadata through such networks, and the results of an experiment and validation of the proposed method using a standard bibliographic dataset.