Authors:
Mahmoud Boghdady
and
Neamat El-Tazi
Affiliation:
Faculty of Computers and Information and Cairo University, Egypt
Keyword(s):
Record Linkage, Profile Matching, Graph Theory, Data Quality, Call Data Record, Social Interactions, Online Social Networks.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Collaboration and e-Services
;
Data Analytics
;
Data Engineering
;
Data Warehouse Management
;
e-Business
;
Enterprise Information Systems
;
Information Integration
;
Integration/Interoperability
;
Knowledge Discovery and Information Retrieval
;
Knowledge Engineering and Ontology Development
;
Knowledge-Based Systems
;
Ontologies and the Semantic Web
;
Symbolic Systems
;
Web Analytics
Abstract:
With the advent of the big-data era and the rapid growth of the amount of data, companies are faced with more
opportunities and challenges to outperform their peers, innovate, compete, and capture value from big-data
platforms such as social networks. Utilizing the full benefit of social media requires companies to identify
their own customers against customers as a whole by linking their local data against data from social media
applying record-linkage techniques that differ from simple to complex. For large sources that have huge data
and fewer constraints over data, the linking process produces low quality results and requires a lot of pairwise
comparisons. We propose a study on how to calculate similarity score not only based on string similarity
techniques or topological graph similarity, but also using graph interactions between nodes to effectively
achieve better linkage results.