2016/12/30 by Dietmar Seipel
Computer Science · Decision Sciences · #Advanced Database Systems and Queries #Artificial intelligence #Computer science #Database #Description logic #Information retrieval #Knowledge base #Knowledge representation and reasoning #Predicate (mathematical logic) #Programming language #Relational database #Rotation formalisms in three dimensions #Scientific Computing and Data Management #Semantic Web #Semantic Web Rule Language #Semantic Web Stack #Semantic Web and Ontologies #Semantic analytics #World Wide Web #XML #cs.AI #cs.DB #cs.PL
paper · pdf · doi:10.4204/eptcs.234.1
published as EPTCS 234, 2017, pp. 1-12 · In Proceedings WLP'15/'16/WFLP'16, arXiv:1701.00148
openalex publication_date 2016/12/30 · arxiv created 2017/01/03 · arxiv updated 2017/01/04 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/06
Modern knowledge base systems frequently need to combine a collection of databases in different formats: e.g., relational databases, XML databases, rule bases, ontologies, etc. In the deductive database system DDBASE, we can manage these different formats of knowledge and reason about them. Even the file systems on different computers can be part of the knowledge base. Often, it is necessary to handle different versions of a knowledge base. E.g., we might want to find out common parts or differences of two versions of a relational database. We will examine the use of abstractions of rule bases by predicate dependency and rule predicate graphs. Also the proof trees of derived atoms can help to compare different versions of a rule base. Moreover, it might be possible to have derivations joining rules with other formalisms of knowledge representation. Ontologies have shown their benefits in many applications of intelligent systems, and there have been many proposals for rule languages compatible with the semantic web stack, e.g., SWRL, the semantic web rule language. Recently, ontologies are used in hybrid systems for specifying the provenance of the different components.