The Ninth International Conference on Knowledge Capture aimed at attracting researchers from diverse areas of Artificial Intelligence, including knowledge representation, knowledge acquisition, intelligent user interfaces, problem-solving and reasoning, planning, agents, text extraction, and machine learning, information enrichment and visualization, as well as researchers interested in cyber-infrastructures to foster the publication, retrieval, reuse, and integration of data.
Today these data come from an increasingly heterogeneous set of resources that differ with regards to their domain, media format, quality, coverage, viewpoint, bias, and so on. More than the sheer amount of these data, their heterogeneity allows us to arrive at better models and answer complex questions that cannot be addressed in isolation but require the interaction of different scientific fields or perspectives.
In most cases, knowledge is not captured as a means to an end but to, for instance, enable better user interfaces, improve retrieval beyond simple keyword search, and so forth. For K-CAP 2017, we focused on the creation, enrichment, querying, and maintenance of knowledge graphs (not necessarily limited to the Semantic Web technology stack) out of heterogeneous data sources.
The Knowledge Capture conference has been held since 2001 as a follow-up of the Knowledge Acquisition Workshops (KAW) that ran between 1986 and 1999. More information about previous editions of K-CAP can be obtained on the website of the K-CAP conference series.