The description logic handbook: theory, implementation, and applications
The description logic handbook: theory, implementation, and applications
Bridging the gap between OWL and relational databases
Proceedings of the 16th international conference on World Wide Web
Tractable Reasoning and Efficient Query Answering in Description Logics: The DL-Lite Family
Journal of Automated Reasoning
A faithful integration of description logics with logic programming
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Effective query rewriting with ontologies over DBoxes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Reconciling description logics and rules
Journal of the ACM (JACM)
Local closed world semantics: grounded circumscription for OWL
ISWC'11 Proceedings of the 10th international conference on The semantic web - Volume Part I
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John McCarthy defines Artificial Intelligence (AI) as the science and engineering of making intelligent machines, especially intelligent computer systems. To build intelligent systems, many of AI researchers, including experts in machine learning and data mining, focus on building "smart applications", which perform complex transformations and manipulations on raw data to discover insights and hidden patterns. On the other hand, the Semantic Web, which exploits the state of the art technologies (in particular ontology reasoning) from Knowledge Representation, a well established branch of AI, attempts to build intelligent systems based on "smart data", which is annotated and described by ontological vocabulary. The "smart application" and "smart data" directions constitute two major approaches to intelligent system. The former is generally the inductive approach that exploits the pattern of large amount of data. The later is generally the deductive approach that exploits the possibilities of even a small fraction of data. These two approaches complement each other in many complex scenarios: • mining technologies can be used to help discover knowledges from huge data sets; • semantic technologies can be used to check if the discovered knowledge are consistent with the existing knowledge and, if so, exploit them so as to meet application requirements.