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. Social networks, online clubs and organisations have come into existence. Information is extracted from these venues about a real world entity like a person, organisation, event, etc. However, this information may change over time, and there is a need for the sources to be up-to-date. Therefore, it is desirable to have a model to extract relevant data items from different sources and merge them to build a complete profile of an entity (entity profiling). Further, this model 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 two phase method-1) The first phase (resolution phase) links records to the queries. We have proposed and observed that the use of random forest for entity resolution increases the performance of the system as this has resulted in more records getting linked to the correct entity. Also, we used trustworthiness of a source as a feature to the random forest. 2) The second phase selects the appropriate values from records to complete a profile based on our proposed selection criteria. We have used various metrics for measuring the performance of the resolution phase as well as for the overall ReLiC framework. It is established through our results that the use of biased sources has significantly improved the performance of the ReLiC framework. Experimental results show that our proposed system, ReLiC outperforms the state-of-the-art.
Wang, J. et al @cite_0 have proposed a hybrid technique for entity resolution using both a human and a system. A system does the initial processing of data, and the users have to choose the correct pair from the most likely pairs identified by the system. Cheng, G. et al @cite_15 have worked on semi-automatic data integration. They have proposed a technique for selection of features to be used for interactive entity resolution, which makes use of human users to carry out the task of entity resolution. The features are selected in such a way that they convey the largest amount of diverse and characteristic information about an entity. However, this may slow down the entity profiling process, and the performance of the system will be dependent on the knowledge of the people involved. The FEVER framework @cite_16 is used to analyze different entity resolution approaches.
@cite_0 @cite_15 @cite_16
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Abstract Entity resolution is a fundamental task in data integration. Recent studies of this problem, including active learning, crowdsourcing, and pay-as-you-go approaches, have started to involve human users in the loop to carry out interactive entity resolution tasks, namely to invite human users to judge whether two entity descriptions refer to the same real-world entity. This process of judgment requires tool support, particularly when entity descriptions contain a large number of features (i.e. property–value pairs). To facilitate judgment, in this article, we propose to select, from entity descriptions, a subset of critical features as a summary to be shown and judged by human users. Features preferred to be selected are those that reflect the most commonalities shared by and the most conflicts between the two entities, and that carry the largest amount of characteristic and diverse information about them. Selected features are then grouped and ordered to improve readability and further speed up judgment. Experimental results demonstrate that summaries generated by our method help users judge more efficiently (3.57–3.78 times faster) than entire entity descriptions, without significantly hurting the accuracy of judgment. The accuracy achieved by our method is also higher than those achieved by existing summarization methods. We present FEVER, a new evaluation platform for entity resolution approaches. The modular structure of the FEVER framework supports the incorporation or reconstruction of many previously proposed approaches for entity resolution. A distinctive feature of FEVER is that it not only evaluates traditional measures such as precision and recall but also the effort for configuring (e.g., parameter tuning, training) a good entity resolution approach. FEVER thus strives for a fair comparative evaluation of different approaches by considering both the effectiveness and configuration effort.