Sumit Mishra

Entity Matching Technique for Bibliographic Database

Some of the attributes of a database relation may evolve over time i.e., they change their values at different instants of time. For example, affiliation attribute of an author relation in a bibliographic database which maintains publication details of various authors, may change its value. When a database contains records of this nature and number of records grows to a large extent then it becomes really very challenging to identify which records belong to which entity due to lack of a proper key. In such a situation, the other attributes of the records and the timed information associated with the records may be useful in identifying whether the records belong to the same entity or different. In the proposed work, the records are initially clustered based on email-id attribute and the clusters are further refined based on other temporal and non-temporal attributes. The refinement process involves similarity check with other records and clusters. A comparative analysis with two existing systems DBLP and ArnetMiner shows that the proposed technique can able to produce better results in many cases.

On Validation of Clustering Techniques for Bibliographic Databases

In entity name disambiguation, performance evaluation of any approach is difficult. This is due to the fact that correct or actual results are often not known. Generally for evaluation purpose, three measures namely precision, recall and f-measure are used. They all are external validity indices because they need golden standard data. But in Bibliographic databases like DBLP, Arnetminer, Scopus, Web of Science, Google Scholar, etc., gold standard data is not easily available and it is very difficult to obtain this due to the overlapping nature of data. So, there is a need to use some other matrices for evaluation purpose. In this paper, some internal cluster validity index based schemes are proposed for evaluating entity name disambiguation algorithms when applied on bibliographic data without using any gold standard datasets. Two new internal validity indices are also proposed in the current paper for this purpose. Experimental results shown on seven bibliographic datasets reveal that proposed internal cluster validity indices are able to compare the results obtained by different methods without prior/gold standard. Thus the present paper demonstrates a novel way of evaluating any entity matching algorithm for bibliographic datasets without using any prior/gold standard information.

Divide and conquer based non-dominated sorting for parallel environment

Many of the real-life problems involve simultaneous optimization of multiple objectives. In recent years there is an enormous increase in the number of multi-objective optimization problems related to different real-life domains. Evolutionary algorithms are the most popular in solving these types of problems. The non-dominating sorting is one of the steps of any multiobjective evolutionary algorithms. This is used mostly to select the non-dominated set of solutions from a given population. In the past various efficient approaches are proposed in the literature to reduce the complexity of this step. As the evolutionary algorithms inhibit parallelism in it. But not all the existing non-dominating sorting approaches have the parallelism property. So in this paper, we have proposed a new approach named as DCNS (Divide and Conquer based Non-dominating Sorting) which inhibits parallelism in it. It has been shown theoretically and empirically that the proposed approach is computationally efficient than existing state-of-the-art methods.

Improved Solution to the Non-Domination Level Update Problem

Non-domination level update problem is to sort the non-dominated fronts after insertion or deletion of a solution. Generally the solution to this problem requires to perform the complete non-dominated sorting which is too expensive in terms of number of comparisons. Recently an Efficient Non-domination Level Update (ENLU) approach is proposed which does not perform the complete sorting. For this purpose, in this paper a space efficient version of ENLU approach is proposed without compromising the number of comparisons. However this approach does not work satisfactorily in all the cases. So we have also proposed another tree based approach for solving this non-domination level update problem. In case of insertion, the tree based approach always checks for same number of fronts unlike linear approach in which the number of fronts to be checked depends on the inserted solution. The result shows that in case where all the solutions are dominating in nature the maximum number of comparisons using tree based approach is

A multiobjective optimization based entity matching technique for bibliographic databases

\mathcal{O}(\log N)

GAEMTBD: Genetic algorithm based entity matching techniques for bibliographic databases

as opposed to

Fast implementation of steady-state NSGA-II

\mathcal{O}(N)

An automatic framework for entity matching in bibliographic databases

in ENLU approach. When all the solutions are equally divided into

GBOS: Generalized Best Order Sort algorithm for non-dominated sorting

K

A divide-and-conquer based efficient non-dominated sorting approach

fronts such that each solution in a front is dominated by all the solutions in the previous front then the maximum number of comparisons to find a deleted solution in case of tree based approach is