Handling Heterogeneous Data in Knowledge Graphs: A Survey
DOI:
https://doi.org/10.13052/jwe1540-9589.2147Keywords:
Knowledge graph, knowledge fusion, entity linking, entity alignment, knowledge baseAbstract
In this era of information where everything is digital, data tends to be ubiquitous. Data Analytics is a term that covers all the areas that deal with the logical analysis of raw data Graph analytics is one of the emerging domains of data analytics that represents and analyses data in the form of knowledge graphs. Knowledge graphs play a vital role in analysing and processing data in order to make decisions. In knowledge graphs the data is stored in the form of entities, relationships between the entities and the attributes of entities as well as attributes of relationships. Construction of knowledge graph and its analytics face multiple challenges like data redundancy, heterogeneity of data, missing data, dynamic nature of real-world data etc. This paper focuses on the issue related to heterogeneity of data while constructing a knowledge graph, and it provides a systematic literature review over construction of knowledge graphs from heterogeneous data sources. This review compiles state-of-the-art knowledge fusion techniques. To conduct this systematic literature review, an exhaustive approach has been adopted to identify various procedures and algorithms included and adapted by different research works for knowledge graph construction.
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