In today’s technology-driven society, effective access to and use of information is a key enabler for progress. Driven by the demands for knowledge-based applications and the unprecedented availability of information on the Web, the study of knowledge capture is of crucial importance. Knowledge capture involves the extraction of useful knowledge from vast and diverse online sources as well as its acquisition directly from human experts.
Researchers and practitioners who work in the area of knowledge capture traditionally participate in several distinct communities, including knowledge engineering, machine learning, natural language processing, human–computer interaction, artificial intelligence, and the Semantic Web. K-CAP 2017 will provide a forum that brings together members of disparate research communities who are interested in efficiently capturing knowledge from a variety of sources and in creating representations that can be useful for automated reasoning, analysis, and other forms of machine processing, as well as to support users in knowledge-intensive collaborative tasks.
In addition, the wealth of information available and generated on the Web, both in structured, linked data forms and in unstructured information sources, brings the need for new methods to make knowledge emerge from such sources, following the recent democratisation, and somehow industrialisation, of areas such as open data, linked data and the Semantic Web. Such new methods must draw both from the traditional knowledge acquisition techniques and from more computational approaches, from large-scale data-mining, statistical analysis, data analytics, etc.
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