Integrating Interactive Digital Maps into a Digital Library

Digital libraries and digital maps are two fast-growing technologies in the world of computing. In this thesis we have explored using digital maps to enhance the functionality of digital libraries. The Greenstone 3 digital library system was augmented though the use of the digital mapping system, Google Maps. An automatic place name recognition and disambiguation system was created to obtain geographical information from documents as they were viewed. This extracted information was presented as a map with markers showing the location of the places within the text of the document. We evaluated the system by performing a user study and an analysis of the speed, efficiency and accuracy of the place name recognition and disambiguation system. Participants in the user study completed most of the tasks easily and made comments expressing their satisfaction with the system. Analysis of the place recognition and disambiguation system was also positive, as the system was fast, relatively efficient and was highly accurate

Bayesian Non-Exhaustive Classification A Case Study: Online Name Disambiguation using Temporal Record Streams

The name entity disambiguation task aims to partition the records of multiple real-life persons so that each partition contains records pertaining to a unique person. Most of the existing solutions for this task operate in a batch mode, where all records to be disambiguated are initially available to the algorithm. However, more realistic settings require that the name disambiguation task be performed in an online fashion, in addition to, being able to identify records of new ambiguous entities having no preexisting records. In this work, we propose a Bayesian non-exhaustive classification framework for solving online name disambiguation task. Our proposed method uses a Dirichlet process prior with a Normal * Normal * Inverse Wishart data model which enables identification of new ambiguous entities who have no records in the training data. For online classification, we use one sweep Gibbs sampler which is very efficient and effective. As a case study we consider bibliographic data in a temporal stream format and disambiguate authors by partitioning their papers into homogeneous groups. Our experimental results demonstrate that the proposed method is better than existing methods for performing online name disambiguation task.Comment: to appear in CIKM 201

Effective Unsupervised Author Disambiguation with Relative Frequencies

This work addresses the problem of author name homonymy in the Web of Science. Aiming for an efficient, simple and straightforward solution, we introduce a novel probabilistic similarity measure for author name disambiguation based on feature overlap. Using the researcher-ID available for a subset of the Web of Science, we evaluate the application of this measure in the context of agglomeratively clustering author mentions. We focus on a concise evaluation that shows clearly for which problem setups and at which time during the clustering process our approach works best. In contrast to most other works in this field, we are sceptical towards the performance of author name disambiguation methods in general and compare our approach to the trivial single-cluster baseline. Our results are presented separately for each correct clustering size as we can explain that, when treating all cases together, the trivial baseline and more sophisticated approaches are hardly distinguishable in terms of evaluation results. Our model shows state-of-the-art performance for all correct clustering sizes without any discriminative training and with tuning only one convergence parameter.Comment: Proceedings of JCDL 201