Search

Data lakes are repositories of data stored in natural/raw format. A data lake may include structured data from relational databases, semi-structured data (i.e., JSON, CSV), unstructured data (i.e., text data), or binary data (i.e., images, audio, video). It is usually built on top of cost-efficient infrastructures such as Hadoop, Amazon S3, MongoDB, ElasticSearch, etc. Several organisations rely on big data lakes for crucial tasks such as reporting, visualisation, advanced analytics, machine learning, and business intelligence. A major limitation of this solution is that without descriptive metadata and a mechanism to maintain it, such data tend to be noisy, making their management and analysis complex and time-consuming. Therefore, there is the need to add a semantic layer based on a formal ontology to describe the data and efficient mechanism to represent them as a knowledge graph. In this paper, we present a methodology to add a semantic layer to a data lake and thus obtain a knowledge graph that can support structured queries and advanced data exploration. We describe a practical implementation of a methodology applied to a data lake consisting of text data describing the online marketplace for lodging and tourism activities. We report statistics about the data lake and the resulting knowledge graph.

With the expansion in the amount of data being produced as Linked Data (LD), the opportunity to build use cases has also increased. However, a crippling problem to the reliability of these use cases is the underlying poor data quality. Moreover, the ability to assess the quality of the consumed LD, based on the satisfaction of the consumers' quality requirements, significantly influences usability of such data for a given use case. In this paper, we propose a data quality assessment methodology specifically designed for LD. This methodology consists of three phases and six steps with specific emphasis on considering a use case. Copyright is held by the author/owner(s)