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The digital revolution has brought most of the world on the world wide web.The data available on WWW has increased many folds in the past decade. Socialnetworks, online clubs and organisations have come into existence. Informationis extracted from these venues about a real world entity like a person,organisation, event, etc. However, this information may change over time, andthere is a need for the sources to be up-to-date. Therefore, it is desirable tohave a model to extract relevant data items from different sources and mergethem to build a complete profile of an entity (entity profiling). Further, thismodel should be able to handle incorrect or obsolete data items. In this paper,we propose a novel method for completing a profile. We have developed a twophase method-1) The first phase (resolution phase) links records to thequeries. We have proposed and observed that the use of random forest for entityresolution increases the performance of the system as this has resulted in morerecords getting linked to the correct entity. Also, we used trustworthiness ofa source as a feature to the random forest. 2) The second phase selects theappropriate values from records to complete a profile based on our proposedselection criteria. We have used various metrics for measuring the performanceof the resolution phase as well as for the overall ReLiC framework. It isestablished through our results that the use of biased sources hassignificantly improved the performance of the ReLiC framework. Experimentalresults show that our proposed system, ReLiC outperforms the state-of-the-art.

ReLiC: Entity Profiling by using Random Forest and Trustworthiness of a Source - Technical Report