A knowledge graph is any graph-based knowledge base, which represents a large network of entities, their semantic types (e.g. a Person or an Organisation), properties (e.g. the name and birth date of a person), and relationships between entities such as a person working in an organisation. Academic research communities, as well as industrial stakeholders, have constructed a number of large-scale knowledge graphs in recent years such as DBpedia, YAGO, Freebase, Wikidata, Google Knowledge Graph, Microsoft Satori, Facebook Entity Graph, Yahoo! Knowledge Graph and others. They are intensively used in different application scenarios such as search, question answering, natural language processing, data integration and analytics, and for specialised areas such as digital humanities, business, life science and more.

However, due to the diversity of data sources and limitations of present knowledge extraction methods, most knowledge graphs face a variety of quality issues such as noise and vague data, inconsistency, inaccurate and out-of-date data, incomplete information, and poor interlinking between KGs. To facilitate wide adoption and advanced usage, it is crucial to ensure the quality of knowledge graphs.

There are still big gaps between present state of quality engineering techniques and high quality KGs and their effective applications. Therefore, this workshop aims to address not only the challenges and state-of-the-art solutions in quality assessment and improvement for knowledge graphs, but also challenges for effectively employing reliable KGs in different domains.

QEKGraph welcomes original research contributions crossing Data Quality and knowledge graph management and consumption. Scholars who have conducted research or developed impactful applications are invited to submit full papers with appropriately evaluated contributions. QEKGraph also welcomes vision/position papers on novel challenges or approaches to existing problems (short papers).