Abstract: In today’s scenario entity appear in multiple data sources so it is necessary to identify the records referring to the same real-world entity, which is named as Entity Resolution (ER).ER is one of the most substantial problems in data cleaning and ascends in many applications such as information integration and information retrieval. Familiar ER approaches are in sufficient to identify records based on pair wise likeness comparisons, which assumes that records referring to the same entity are more similar to each other than otherwise. However for certain circumstances this assumption does not always hold in practice and likeness comparisons do not work well when such assumption breaks. So to overcome outdated ER drawback a new set of rules which could describe the complex matching conditions between records and entities is proposed such as rule discovery algorithm, rule based ER algorithm along with blocking scheme methods to get more resolved classified entity set.
Keywords: Entity Resolution, Data Cleaning, Rule Learning and Meta blocking.