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UID:/NewsandEvents/Archives/2020/newsitem/11624/29
 -August-2020-8th-Workshop-What-can-FCA-do-for-AI-F
 CA4AI-2020-Online-via-Zoom
DTSTAMP:20200828T000124
SUMMARY:8th Workshop "What can FCA do for AI?" (FC
 A4AI 2020), Online via Zoom
DTSTART;VALUE=DATE:20200829
DTEND;VALUE=DATE:20200829
LOCATION:Online via Zoom
DESCRIPTION:Formal Concept Analysis (FCA) is a mat
 hematically well-founded theory aimed at data anal
 ysis and classification. FCA allows one to build a
  concept lattice and a system of dependencies (imp
 lications and association rules) which can be used
  for many AI needs, e.g. knowledge processing, kno
 wledge discovery, knowledge representation and rea
 soning, ontology engineering as well as informatio
 n retrieval, recommendation, social network analys
 is and text processing. Recent years have been wit
 nessing increased scientific activity around FCA, 
 in particular a strand of work emerged that is aim
 ed at extending the possibilities of plain FCA w.r
 .t. knowledge processing. While the capabilities o
 f FCA are extended, new possibilities are arising 
 in the framework of FCA.  The 8th FCA4AI workshop,
  co-located with ECAI 2020, is (as usual) dedicate
 d to discuss such issues, and in particular:  - Ho
 w can FCA support AI activities in knowledge disco
 very, knowledge representation and reasoning, mach
 ine learning, natural language processing...  - By
  contrast, how the current developments in AI can 
 be integrated within FCA to help AI researchers to
  solve complex problems in their domain.  The work
 shop welcomes submissions in pdf format in Springe
 r's LNCS style. Submissions can be technical paper
 s not exceeding 12 pages, or system descriptions o
 r position papers on work in progress not exceedin
 g 6 pages. Submissions are via EasyChair.
X-ALT-DESC;FMTTYPE=text/html:<div>\n  <p>Formal Co
 ncept Analysis (FCA) is a mathematically well-foun
 ded theory aimed at data analysis and classificati
 on. FCA allows one to build a concept lattice and 
 a system of dependencies (implications and associa
 tion rules) which can be used for many AI needs, e
 .g. knowledge processing, knowledge discovery, kno
 wledge representation and reasoning, ontology engi
 neering as well as information retrieval, recommen
 dation, social network analysis and text processin
 g. Recent years have been witnessing increased sci
 entific activity around FCA, in particular a stran
 d of work emerged that is aimed at extending the p
 ossibilities of plain FCA w.r.t. knowledge process
 ing. While the capabilities of FCA are extended, n
 ew possibilities are arising in the framework of F
 CA.</p>\n\n  <p>The 8th FCA4AI workshop, co-locate
 d with ECAI 2020, is (as usual) dedicated to discu
 ss such issues, and in particular:<br>\n  - How ca
 n FCA support AI activities in knowledge discovery
 , knowledge representation and reasoning, machine 
 learning, natural language processing...<br>\n  - 
 By contrast, how the current developments in AI ca
 n be integrated within FCA to help AI researchers 
 to solve complex problems in their domain.</p>\n</
 div><div>\n  <p>The workshop welcomes submissions 
 in pdf format in Springer's LNCS style. Submission
 s can be technical papers not exceeding 12 pages, 
 or system descriptions or position papers on work 
 in progress not exceeding 6 pages. Submissions are
  via EasyChair.</p>\n</div>
URL:http://www.fca4ai.hse.ru/2020
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