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.
Benny, S. et al @cite_18 proposed a Hadoop framework for entity resolution using the Map and Reduce algorithm on big data. A technique for carrying out entity resolution on heterogeneous distributed probabilistic data is proposed in @cite_31 . They use the expectation maximization algorithm for integrating the data. They have reported significant performance improvements over existing methods for entity resolution in a distributed environment. Ayat, N. et al @cite_37 proposed a method for entity resolution on probabilistic data. They have proposed algorithms for entity resolution using context-free and context-sensitive similarity functions. Hu, W. et al @cite_9 have proposed a scalable technique to address the problem of entity linkage on the semantic web. They have used a bootstrapping method, taking into consideration both the semantic co-referent entities as well as the similarity between property values of the entity for entity resolution.
@cite_9 @cite_18 @cite_31 @cite_37
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Abstract Large amount of data is being generated from sensors, satellites, social media etc. This big data (velocity, variety, veracity, value and veracity) can be processed so as to make timely decisions by the decision makers. This paper presents results of the proposed Hadoop framework that performs entity resolution in Map and reduce phase. MapReduce phase matches two real world objects and generates rules. The similarity score of these rules are used for matching stream data during testing phase. Similarity is calculated using 13 different semantic measures such as token-based similarity, edit-based similarity, hybrid similarity, phonetic similarity as well as domain dependent Natural language processing measures. Semantic measures are implemented using Hive programming. The proposed system is tested using e-catalogues of Amazon and Google. In this paper we describe graph-based parallel algorithms for entity resolution that improve over the map-reduce approach. We compare two approaches to parallelize a Locality Sensitive Hashing (LSH) accelerated,IterativeMatch-Merge (IMM) entity resolution technique: BCP, where records hashed together are compared at a single node reducer, vs an alternative mechanism (RCP) where comparison load is better distributed across processors especially in the presence of severely skewed bucket sizes. We analyze the BCP and RCP approaches analytically as well as empirically using a large synthetically generated dataset. We generalize the lessons learned from our experience and submit that the RCP approach is also applicable in many similar applications that rely on LSH or related grouping strategies to minimize pair-wise comparisons. Consider two data providers, each maintaining records of different feature sets about common entities. They aim to learn a linear model over the whole set of features. This problem of federated learning over vertically partitioned data includes a crucial upstream issue: entity resolution, i.e. finding the correspondence between the rows of the datasets. It is well known that entity resolution, just like learning, is mistake-prone in the real world. Despite the importance of the problem, there has been no formal assessment of how errors in entity resolution impact learning. In this paper, we provide a thorough answer to this question, answering how optimal classifiers, empirical losses, margins and generalisation abilities are affected. While our answer spans a wide set of losses --- going beyond proper, convex, or classification calibrated ---, it brings simple practical arguments to upgrade entity resolution as a preprocessing step to learning. One of these suggests that entity resolution should be aimed at controlling or minimizing the number of matching errors between examples of distinct classes. In our experiments, we modify a simple token-based entity resolution algorithm so that it indeed aims at avoiding matching rows belonging to different classes, and perform experiments in the setting where entity resolution relies on noisy data, which is very relevant to real world domains. Notably, our approach covers the case where one peer have classes, or a noisy record of classes. Experiments display that using the class information during entity resolution can buy significant uplift for learning at little expense from the complexity standpoint. Many databases contain uncertain and imprecise references to real-world entities. The absence of identifiers for the underlying entities often results in a database which contains multiple references to the same entity. This can lead not only to data redundancy, but also inaccuracies in query processing and knowledge extraction. These problems can be alleviated through the use of entity resolution. Entity resolution involves discovering the underlying entities and mapping each database reference to these entities. Traditionally, entities are resolved using pairwise similarity over the attributes of references. However, there is often additional relational information in the data. Specifically, references to different entities may cooccur. In these cases, collective entity resolution, in which entities for cooccurring references are determined jointly rather than independently, can improve entity resolution accuracy. We propose a novel relational clustering algorithm that uses both attribute and relational information for determining the underlying domain entities, and we give an efficient implementation. We investigate the impact that different relational similarity measures have on entity resolution quality. We evaluate our collective entity resolution algorithm on multiple real-world databases. We show that it improves entity resolution performance over both attribute-based baselines and over algorithms that consider relational information but do not resolve entities collectively. In addition, we perform detailed experiments on synthetically generated data to identify data characteristics that favor collective relational resolution over purely attribute-based algorithms.