The Internet has enabled the creation of a growing number of large-scale knowledge bases in a variety of domains containing complementary information. Tools for automatically aligning these knowledge bases would make it possible to unify many sources of structured knowledge and answer complex queries. However, the efficient alignment of large-scale knowledge bases still poses a considerable challenge. Here, we present Simple Greedy Matching (SiGMa), a simple algorithm for aligning knowledge bases with millions of entities and facts. SiGMa is an iterative propagation algorithm which leverages both the structural information from the relationship graph as well as flexible similarity measures between entity properties in a greedy local search, thus making it scalable. Despite its greedy nature, our experiments indicate that SiGMa can efficiently match some of the world's largest knowledge bases with high precision. We provide additional experiments on benchmark datasets which demonstrate that SiGMa can outperform state-of-the-art approaches both in accuracy and efficiency.
The SiGMa algorithm is related to the collective entity resolution approach of Bhattacharya and Getoor @cite_1 , which proposed a greedy agglomerative clustering algorithm to cluster entities based on previous decisions. Their approach could handle constraints on the clustering, including a @math matching constraint in theory, though it was not implemented. A scalable solution for collective entity resolution was proposed recently in @cite_28 , by treating the sophisticated machine learning approaches to entity resolution as black boxes (see references therein), but running them on small neighborhoods and combining their output using a message-passing scheme. They do not consider exploiting a @math matching constraint though, as most entity resolution or record linkage work.
@cite_28 @cite_1
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First, we expose the instance of ER with EM algorithm to integrate different models.We apply ER for HDPD to achieve major performance in terms of response time.Matching instance is compared with BFA, Koosh-TND and Packages-TND algorithms.After the integration, we work on real data and compare with FC, SCC and FD algorithms.Our method improves in terms of response and communication time with existing methods. Distributed computing is linked and equated to the industrial revolution. Its transformational nature is, however, associated with significant instances in the form of internet of thing operations. Entity resolution (ER) is a problem of matching and resolving records that represent the same real world entity. This is a long-standing challenge in distributed databases and information retrieval as a statistic. In a centralized approach, the problem of ER has not been scaled well as large amount of data need to be sent to a central node. In this paper, we present an algorithm which deals with heterogeneous distributed probabilistic data (HDPD) and also reduces processing time in a distributed environment. We propose two different approaches. First, we explore this instance with a matching (identification) problem to integrate different data models with expectation-maximization (EM) algorithm. Second, we apply ER methodology for HDPD to achieve major performance in terms of response time to produce the outcome. We validate HDPD through experiments over a 100-node cluster that records significant performance improvements over naive approaches. This paper is expected to provide insights in to database organizations and new technological development for the growth of distributed environment. 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. 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. 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.