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
2171472464 2061162567 2161098784 2135625884
Entity resolution is the problem of determining which records in a database refer to the same entities, and is a crucial and expensive step in the data mining process. Interest in it has grown rapidly in recent years, and many approaches have been proposed. However, they tend to address only isolated aspects of the problem, and are often ad hoc. This paper proposes a well-founded, integrated solution to the entity resolution problem based on Markov logic. Markov logic combines first-order logic and probabilistic graphical models by attaching weights to first-order formulas, and viewing them as templates for features of Markov networks. We show how a number of previous approaches can be formulated and seamlessly combined in Markov logic, and how the resulting learning and inference problems can be solved efficiently. Experiments on two citation databases show the utility of this approach, and evaluate the contribution of the different components. 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. We describe an approach for performing entity type recognition in heterogeneous semantic graphs in order to reduce the computational cost of performing coreferenceresolution. Our research specifically addresses the problem of working with semi-structured text that uses ontologies that are not informative or not known. This problem is similar to co reference resolution in unstructured text, where entities and their types are identified using contextual information and linguistic-based analysis. Semantic graphs are semi-structured with very little contextual information and trivial grammars that do not convey additional information. In the absence of known ontologies, performing co reference resolution can be challenging. Our work uses a supervised machine learning algorithm and entity type dictionaries to map attributes to a common attribute space. We evaluated the approach in experiments using data from Wikipedia, Freebase and Arnetminer. 1. Summary In Part I, four ostensibly different theoretical models of induction are presented, in which the problem dealt with is the extrapolation of a very long sequence of symbols—presumably containing all of the information to be used in the induction. Almost all, if not all problems in induction can be put in this form. Some strong heuristic arguments have been obtained for the equivalence of the last three models. One of these models is equivalent to a Bayes formulation, in which a priori probabilities are assigned to sequences of symbols on the basis of the lengths of inputs to a universal Turing machine that are required to produce the sequence of interest as output. Though it seems likely, it is not certain whether the first of the four models is equivalent to the other three. Few rigorous results are presented. Informal investigations are made of the properties of these models. There are discussions of their consistency and meaningfulness, of their degree of independence of the exact nature of the Turing machine used, and of the accuracy of their predictions in comparison to those of other induction methods. In Part II these models are applied to the solution of three problems—prediction of the Bernoulli sequence, extrapolation of a certain kind of Markov chain, and the use of phrase structure grammars for induction. Though some approximations are used, the first of these problems is treated most rigorously. The result is Laplace's rule of succession. The solution to the second problem uses less certain approximations, but the properties of the solution that are discussed, are fairly independent of these approximations. The third application, using phrase structure grammars, is least exact of the three. First a formal solution is presented. Though it appears to have certain deficiencies, it is hoped that presentation of this admittedly inadequate model will suggest acceptable improvements in it. This formal solution is then applied in an approximate way to the determination of the “optimum” phrase structure grammar for a given set of strings. The results that are obtained are plausible, but subject to the uncertainties of the approximation used.