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.
Benjelloun, O. et al @cite_20 discusses the pairwise resolution of the entities. The novelty of this approach is the usage of the match and merge properties. The four properties considered here are Idempotence, Associativity, Commutativity and Relativity. The validity of these properties, once confirmed, helps in efficient entity resolution. Bilgic, M. et al @cite_14 proposed a method to solve entity resolution problem with Markov logic. This method proposes combining of first-order logic and probabilistic graphical models. Weights are associated with first-order formulas and taken as features for Markov networks. The combining of first-order logic and Markov network leads to proper learning and efficient solution to the problem. A theoretical framework for knowledge-based entity resolution using first-order logic is proposed in @cite_12 . The focus is on the analysis of knowledge patterns for optimizing a knowledge model, which is then used for entity resolution. A method for linking words or phrases in unstructured texts to entities using part of speech patterns in text is proposed in @cite_24 .
@cite_24 @cite_14 @cite_12 @cite_20
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Linking words or phrases in unstructured text to entities in knowledge bases is the problem of entity recognition and disambiguation. In this paper, we focus on the task of entity recognition in Web text to address the challenges of entity correctness, completeness and emergence that existing approaches mainly suffer from. Experimental results show that our approach significantly outperforms the state-of-the-art approaches in terms of precision, F-measure, micro-accuracy and macro-accuracy, while still preserving high recall. Visualizing and analyzing social networks is a challenging problem that has been receiving growing attention. An important first step, before analysis can begin, is ensuring that the data is accurate. A common data quality problem is that the data may inadvertently contain several distinct references to the same underlying entity; the process of reconciling these references is called entity-resolution. D-Dupe is an interactive tool that combines data mining algorithms for entity resolution with a task-specific network visualization. Users cope with complexity of cleaning large networks by focusing on a small subnetwork containing a potential duplicate pair. The subnetwork highlights relationships in the social network, making the common relationships easy to visually identify. D-Dupe users resolve ambiguities either by merging nodes or by marking them distinct. The entity resolution process is iterative: as pairs of nodes are resolved, additional duplicates may be revealed; therefore, resolution decisions are often chained together. We give examples of how users can flexibly apply sequences of actions to produce a high quality entity resolution result. We illustrate and evaluate the benefits of D-Dupe on three bibliographic collections. Two of the datasets had already been cleaned, and therefore should not have contained duplicates; despite this fact, many duplicates were rapidly identified using D-Dupe's unique combination of entity resolution algorithms within a task-specific visual interface Abstract Entity resolution is the process of determining whether a collection of entity representations refer to the same entity in the real world. In this paper we introduce a theoretical framework that supports knowledge-based entity resolution. From a logical point of view, the expressive power of the framework is equivalent to a decidable fragment of first-order logic including conjunction, disjunction and a certain form of negation. Although the framework is expressive for representing knowledge about entity resolution in a collective way, the questions that arise are: (1) how efficiently can knowledge patterns be processed; (2) how effectively can redundancy among knowledge patterns be eliminated. In answering these questions, we first study the evaluation problem for knowledge patterns. Our results show that this problem is NP-complete w.r.t. combined complexity but in ptime w.r.t. data complexity. This nice property leads us to investigate the containment problem for knowledge patterns, which turns out to be NP-complete. We further develop a notion of optimality for knowledge patterns and a mechanism of optimizing a knowledge model (i.e. a finite set of knowledge patterns). We prove that the optimality decision problem for knowledge patterns is still NP-complete.