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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. 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.to the corresponding entity and this data can be used for various information extraction tasks on the entity. Entities can be of different types, for example, people, places, items, etc. Data having different kinds of attributes need to be used for performing entity resolution on different entities. Entity resolution is an important step for entity profiling and to the best of our knowledge, classifiers were not used in the literature for entity resolution. This motivated us to study the use of classifiers for entity resolution. Due to which, the proposed method worked effectively on different types of data. Entity profiling helps in acquiring accurate information from data extracted from various unreliable sources. But, to the best of our knowledge, assigning a bias to a reliable source for profiling was not considered earlier in the literature and this motivated us to use a biased source. The objective of this work is to develop a novel technique for effectively obtaining the complete profiles of entities using structured data records from different sources.

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Introduction: Amlapitta mentioned in Ayurveda is one of the common clinical condition of the gastrointestinal system in which Pitta get Vidhagdhm or “Amlam Paka. Causes of Amlapitta especially compromise Ahara Janya Hetu, such as excessive intake of Abhishyandi and Pishtanna, Acidic, Hot substances, Alcohol, Adhyashan, Viruddhashan, Vidahiannapana and intake of food in Ajirna condition.   In modern science, Amlapita can be correlated with Gastroesophageal reflux disease (GERD), in which hyperacidity occurs due to excess acid production in the stomach. Aim and objectives: The prime aim of this paper is to study the efficacy of Shamana and Shodhana Chikitsa in Ayurveda in the management of Adhog Amlapitta. Material and method: It is a single case study of 64 years-old male patients who had complaints, e.g., constant Pain in the Epigastric region, Abdominal distension, burning sensation in the throat, chest, and abdomen restlessness. Observations and Results: All clinical features in this patient had reduced significantly by using Shodhana and Shaman Aushadhi recommended by ancient Acharyas in the management of Amlapitta.