An Efficient Metric of Automatic Weight Generation for Properties in Instance Matching Technique

TL;DR

This paper proposes a new mathematical metric for automatically generating property weights in instance matching, improving efficiency by leveraging information content principles.

Contribution

It introduces a novel metric based on property value ratios and information theory to enhance automatic weight generation in instance matching.

Findings

The proposed metric improves instance matching efficiency.

Experimental results validate the effectiveness of the metric.

The approach outperforms existing weight generation methods.

Abstract

The proliferation of heterogeneous data sources of semantic knowledge base intensifies the need of an automatic instance matching technique. However, the efficiency of instance matching is often influenced by the weight of a property associated to instances. Automatic weight generation is a non-trivial, however an important task in instance matching technique. Therefore, identifying an appropriate metric for generating weight for a property automatically is nevertheless a formidable task. In this paper, we investigate an approach of generating weights automatically by considering hypotheses: (1) the weight of a property is directly proportional to the ratio of the number of its distinct values to the number of instances contain the property, and (2) the weight is also proportional to the ratio of the number of distinct values of a property to the number of instances in a training dataset. The basic intuition behind the use of our approach is the classical theory of information content that infrequent words are more informative than frequent ones. Our mathematical model derives a metric for generating property weights automatically, which is applied in instance matching system to produce re-conciliated instances efficiently. Our experiments and evaluations show the effectiveness of our proposed metric of automatic weight generation for properties in an instance matching technique.