As of August 2019, DaSeLab will find a new home at the Department of Computer Science at Kansas State University. We're looking forward to the new adventures!
IOS Press just launched it's linked data portal LD Connect. With contributions by Krzysztof Janowicz's STKO Lab and DaSe Lab.
Michelle made a word cloud diagram from all DaSeLab papers from 2017. Some funky bits there (I'm pretty sure the Prot and the 'e refers to Protégé). But other than that, this looks rather in line with what I think we did ...
Semantic Web is an inherently multi-disciplinary field. The Artificial Intelligence community has contributed much in the way of formal logic and knowledge representation. Similarly, the applied computer science community, along with industry and government agencies, have contributed with application development and testing.
Four members of the DaSe Lab - Michelle Cheatham, Aaron Eberhart, Pascal Hitzler, Cogan Shimizu - visited the 16th International Semantic Web Conference, ISWC 2017, this year in Vienna, Austria, which is the key conference in the field.
Take away's from Jamie Taylor's (of Google) keynote at ISWC2017 today
The lab is proud to participate in the Data Science and Security Cluster (DSSC) at the Department of Computer Science and Engineering at Wright State University.
Members of the DSSC received a number of awards this spring:
Below a collection of videos and audio (with slides) of presentations and other material by DaSeLab members.
(last update: 2017-12-03)
Pascal Hitzler, Rule-Based OWL Modeling with ROWLTab Protégé Plugin, June 2017 (at ESWC 2017): http://videolectures.net/eswc2017_hitzler_OWL_modeling/
About a decade ago, not long after description logics had been chosen as the basis for the then-forthcoming W3C Recommendation for the Web Ontology Language OWL, a rather agressively voiced discussion as to whether a rule-based paradigm might have been a better choice emerged in the Semantic Web community. One of the arguments brought forth was that it were much easier to convey logical statements using rules rather then OWL (or description logic) axioms, thus making it easier for ontology engineers to model.