CLASSIC: a structural data model for objects
SIGMOD '89 Proceedings of the 1989 ACM SIGMOD international conference on Management of data
Reasoning and revision in hybrid representation systems
Reasoning and revision in hybrid representation systems
Translating description logics to information server queries
CIKM '93 Proceedings of the second international conference on Information and knowledge management
A Space-Economical Suffix Tree Construction Algorithm
Journal of the ACM (JACM)
A Terminological Knowledge Representation System with Complete Inference Algorithms
PDK '91 Proceedings of the International Workshop on Processing Declarative Knowledge
A semantics and complete algorithm for subsumption in the classic description logic
Journal of Artificial Intelligence Research
Feature generation for sequence categorization
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Modeling and Query Patterns for Process Retrieval in OWL
ISWC '09 Proceedings of the 8th International Semantic Web Conference
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Representing and manipulating sequences in description logics (DLs) has typically been achieved through the use of new sequence-specific operators or by relying on host-language functions. This paper shows that it is not necessary to add additional features to a DL to handle sequences, and instead describes an approach for dealing with sequences as first-class entities directly within a DL without the need for extensions or extra-linguistic constructs. The key idea is to represent sequences using suffix trees, then represent the resulting trees in a DL using traditional (tractable) concept and role operators. This approach supports the representation of a variety of information about a sequence, such as the locations and numbers of occurrences of all subsequences of the sequence. Moreover, subsequence testing and pattern matching reduce to subsumption checking in this representation, while computing the least common subsumer of two terms supports the application of inductive learning to sequences.