Semantic Relation Extraction from Socially-Generated Tags: A Methodology for Metadata Generation

Abstract

The massive social semantics resource presents both opportunities and challenges for metadata to leverage its power for information content representation. One such challenge is the lack of context information of these tags when they are used in retrieval and automatic processing. This paper reports a study that uses user-generated tags from Flickr as an example of social semantics sources to explore a new approach to enriching subject metadata. The proposed method involves using Flickr tags as the source, Google search results as the context of co-occurring tags and their relations, and natural language processing and machine learning as the processing techniques. The preliminary experiment built a context sentence collection from Google search results, which was then processed by natural language processing and machine learning algorithms. This new approach achieved a reasonably good rate of accuracy in assigning relations to groups of tags. The paper explored further the methodological implications of this new approach in using social semantics to enrich subject metadata.