TY - JOUR A1 - Loster, Michael A1 - Koumarelas, Ioannis A1 - Naumann, Felix T1 - Knowledge transfer for entity resolution with siamese neural networks JF - ACM journal of data and information quality N2 - The integration of multiple data sources is a common problem in a large variety of applications. Traditionally, handcrafted similarity measures are used to discover, merge, and integrate multiple representations of the same entity-duplicates-into a large homogeneous collection of data. Often, these similarity measures do not cope well with the heterogeneity of the underlying dataset. In addition, domain experts are needed to manually design and configure such measures, which is both time-consuming and requires extensive domain expertise.
We propose a deep Siamese neural network, capable of learning a similarity measure that is tailored to the characteristics of a particular dataset. With the properties of deep learning methods, we are able to eliminate the manual feature engineering process and thus considerably reduce the effort required for model construction. In addition, we show that it is possible to transfer knowledge acquired during the deduplication of one dataset to another, and thus significantly reduce the amount of data required to train a similarity measure. We evaluated our method on multiple datasets and compare our approach to state-of-the-art deduplication methods. Our approach outperforms competitors by up to +26 percent F-measure, depending on task and dataset. In addition, we show that knowledge transfer is not only feasible, but in our experiments led to an improvement in F-measure of up to +4.7 percent. KW - Entity resolution KW - duplicate detection KW - transfer learning KW - neural KW - networks KW - metric learning KW - similarity learning KW - data quality Y1 - 2021 U6 - https://doi.org/10.1145/3410157 SN - 1936-1955 SN - 1936-1963 VL - 13 IS - 1 PB - Association for Computing Machinery CY - New York ER - TY - JOUR A1 - Ellis, Jason Brent A1 - Abreu-Ellis, Carla Reis T1 - Student Perspectives of Social Networking use in Higher Education JF - KEYCIT 2014 - Key Competencies in Informatics and ICT N2 - Social networks are currently at the forefront of tools that lend to Personal Learning Environments (PLEs). This study aimed to observe how students perceived PLEs, what they believed were the integral components of social presence when using Facebook as part of a PLE, and to describe student’s preferences for types of interactions when using Facebook as part of their PLE. This study used mixed methods to analyze the perceptions of graduate and undergraduate students on the use of social networks, more specifically Facebook as a learning tool. Fifty surveys were returned representing a 65 % response rate. Survey questions included both closed and open-ended questions. Findings suggested that even though students rated themselves relatively well in having requisite technology skills, and 94 % of students used Facebook primarily for social use, they were hesitant to migrate these skills to academic use because of concerns of privacy, believing that other platforms could fulfil the same purpose, and by not seeing the validity to use Facebook in establishing social presence. What lies at odds with these beliefs is that when asked to identify strategies in Facebook that enabled social presence to occur in academic work, the majority of students identified strategies in five categories that lead to social presence establishment on Facebook during their coursework. KW - Social KW - networks KW - higher KW - education KW - personal KW - learning KW - environments KW - Facebook Y1 - 2015 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-82632 SN - 1868-0844 SN - 2191-1940 IS - 7 SP - 117 EP - 131 PB - Universitätsverlag Potsdam CY - Potsdam ER -