This article argues for the growing importance of quality metadata and the equation of that quality with precision and semantic grounding. Such semantic grounding requires metadata that derives from intentional human intervention as well as mechanistic measurement of content media. In both cases, one chief problem in the automatic generation of semantic metadata is leading to the overgeneration of inaccurate annotations. We look at a particular richly annotated image collection to show how context dramatically reduces the problem of over this particular corpus. In particular, we consider both the abstract measurement of contextual ambiguity over the collection and the application of a particular disambiguation algorithm to synthesized keyword searches across the selection.

Haase This paper describes the architecture, implementation and evaluation of NetSerf, a program for finding information archives on the Internet using natural language queries.NetSerf's query processor extracts structured, disambiguated representations from the queries.The query representations are matched to hand-coded representations of the archives using semantic knowledge from WordNet (a semantic thesaurus) and an on-line Webster's dictionary.NetSerf has been tested using a set of questions and answers developed independently for a game called Internet Hunt.The paper presents results comparing the performance of NetSerf and the standard IRs ystem SMART on this set of queries.