QEKGraph 2017 - 1st International Workshop on Quality Engineering Meets Knowledge Graph

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most knowledge graphs face a variety of quality issues such as noise and vague data, inconsistency, inaccurate and out-of-date data, incomplete information, and poor interlinking between KGs.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). Topics on which potential submitters are invited to contribute include, but are not limited to:
Quality issues in knowledge graphs
Quality assessment metrics and measures
Representation of quality assessment results
Data cleaning and knowledge graphs
Outlier detection in knowledge graphs
Triple classification and ranking
Trust and provenance of knowledge graphs
Data integration and quality control
Evaluation of quality of links in the Web of Data
Evaluation of link prediction methods
Evaluation benchmark and datasets
Multilingual knowledge graphs and quality control
Quality assessment at scale
Crowdsourcing and knowledge graph quality
Quality of specialised knowledge graphs
Trust and reliability of digital humanities and social science data
Advancing education with data quality engineering