Abstract. OWL 2 RL was standardized as a less expressive but scalable subset of OWL 2 that allows a forward-chaining implementation. However, building an enterprise-scale forward-chaining based inference engine that can 1) take advantage of modern multi-core computer architectures, and 2) efficiently update inference for additions remains a challenge. In this paper, we present an OWL 2 RL inference engine implemented inside the Oracle database system, using novel techniques for parallel processing that can readily scale on multi-core machines and clusters. Additionally, we have added support for efficient incremental maintenance of the inferred graph after triple additions. Finally, to handle the increasing number of owl:sameAs relationships present in Semantic Web datasets, we have provided a hybrid in-memory/disk based approach to efficiently compute compact equivalence closures. We have done extensive testing to evaluate these new techniques; the test results demonstrate that our inference engine is capable of performing efficient inference over ontologies with billions of triples using a modest hardware configuration.
Ontologies are increasingly being used to build applications that utilize domain-specific knowledge. This paper addresses the problem of supporting ontology-based semantic matching in RDBMS. Specifically, 1) A set of SQL operators, namely ONT_RELATED, ONT_EXPAND, ONT_DISTANCE, and ONT_PATH, are introduced to perform ontology-based semantic matching, 2) A new indexing scheme ONT_INDEXTYPE is introduced to speed up ontology-based semantic matching operations, and 3) System-defined tables are provided for storing ontologies specified in OWL. Our approach enables users to reference ontology data directly from SQL using the semantic match operators, thereby opening up possibilities of combining with other operations such as joins as well as making the ontology-driven applications easy to develop and efficient. In contrast, other approaches use RDBMS only for storage of ontologies and querying of ontology data is typically done via APIs. This paper presents the ontology-related functionality including inferencing, discusses how it is implemented on top of Oracle RDBMS, and illustrates the usage with several database applications.
Partitioning is typically employed on large-scale data to improve manageability, availability, and performance. However, for tables connected by a referential constraint (capturing a parent-child relationship), the current approaches require individually partitioning each table thereby burdening the user with the task of maintaining the tables equi-partitioned, which not only is cumbersome but also error prone. This paper proposes a new partitioning method (partition by reference) that allows tables with a parent-child relationship to be logically equi-partitioned by inheriting the partition key from the parent table without duplicating the key columns. The partitioning key is resolved through an existing parent-child relationship, enforced by an active referential constraint. This logical dependency is used to automatically i) cascade partition maintenance operations performed on parent table to child tables, and ii) handle migration of child rows when partition key or parent key in parent table is updated, as a single atomic operation. This method has been introduced in Oracle Database 11gR1 with support for tables with both single level and composite partitioning methods. The paper describes the key concepts of table partitioning by reference method, discusses the design and implementation challenges, and presents an experimental study covering a usage scenario common in Information Life Cycle Management (ILM) applications.
Ontologies are increasingly being used to build applications that utilize domain-specific knowledge. This paper addresses the problem of supporting ontology-based semantic matching in RDBMS. Specifically, 1) A set of SQL operators, namely ONT_RELATED, ONT_EXPAND, ONT_DISTANCE, and ONT_PATH, are introduced to perform ontology-based semantic matching, 2) A new indexing scheme ONT_INDEXTYPE is introduced to speed up ontology-based semantic matching operations, and 3) System-defined tables are provided for storing ontologies specified in OWL. Our approach enables users to reference ontology data directly from SQL using the semantic match operators, thereby opening up possibilities of combining with other operations such as joins as well as making the ontology-driven applications easy to develop and efficient. In contrast, other approaches use RDBMS only for storage of ontologies and querying of ontology data is typically done via APIs. This paper presents the ontology-related functionality including inferencing, discusses how it is implemented on top of Oracle RDBMS, and illustrates the usage with several database applications.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.