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The Linked Open Data (LOD) cloud has gained significant attention in the Semantic Web community over the past few years. With rapid expansion in size and diversity, it consists of over 800 interlinked datasets with over 60 billion triples. These datasets encapsulate structured data and knowledge spanning over varied domains such as entertainment, life sciences, publications, geography, and government. Applications can take advantage of this by using the knowledge distributed over the interconnected datasets, which is not realistic to find in a single place elsewhere. However, two of the key obstacles in using the LOD cloud are the limited support for data integration tasks over concepts, instances, and properties, and relevant data source selection for querying over multiple datasets. We review, in brief, some of the important and interesting technical approaches found in the literature that address these two issues. We observe that the general purpose alignment techniques developed outside the LOD context fall short in meeting the heterogeneous data representation of LOD. Therefore, an LOD-specific review of these techniques (especially for alignment) is important to the community. The topics covered and discussed in this article fall under two broad categories, namely alignment techniques for LOD datasets and relevant data source selection in the context of query processing over LOD datasets.


This is the pre-peer reviewed version of the following article: Gunaratna, K., Lalithsena, S., & Sheth, A. (2014). Alignment and dataset identification of linked data in semantic web. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 4(2), 139-151, which has been published in final form at