There has been a rapid growth in the use of symbolic representations along with their applications in many important tasks. Symbolic representations, in the form of Knowledge Graphs (KGs), constitute large networks of real-world entities and their relationships. On the other hand, sub-symbolic artificial intelligence has also become a mainstream area of research. Many studies have been proposed which focus on learning distributed representations from KGs. These KGs are generated manually or automatically by processing text or other data sources. The workshop also targets the problem of capturing formal semantics in sub-symbolic systems. The focus of the workshop is to allow these two communities to join forces in order to develop more effective algorithms and applications.
Papers must be formatted in CEUR style guidelines in the two-columned style (no page numbers). See details on CEUR-WS. Papers should be submitted submitted via EasyChair. Submissions can fall in one of the following categories:
Authors are encouraged to submit negative (i.e., failing) results with strong contribution and an analysis of the results.
Accepted papers (after blind review) will be published by CEUR–WS companion volume.
At least one of the authors of the accepted papers must register for the workshop for the paper to be included into the workshop proceedings.