A Scalable Backward Chaining-based Reasoner for a Semantic Web

2014 Tenth International Conference on Signal-Image Technology and Internet-Based Systems, 2014

We present a new version of CEDAR, a taxonomic reasoner for large-scale ontologies. This extended version provides fuller support for TBox reasoning, checking consistency, and retrieving instances. CEDAR is built on top of the OSF formalism and based on an entirely new architecture which includes several optimization techniques. Using OSF graph structures, we define a bidirectional mapping between OSF structure and the Resource Description Framework (RDF) allowing a translation from OSF queries into SPARQL for retrieving instances. Experiments were carried out using very large ontologies. The results achieved by CEDAR were compared to those obtained by well-known Semantic Web reasoners such as FaCT++, Pellet, HermiT, TrOWL, and RacerPro. CEDAR performs on a par with the best systems for concept classification and several orders of magnitude more efficiently in terms of response time for Boolean query-answering.

2008

In this paper we discuss the challenges of performing reasoning on large scale RDF datasets from the Web. We discuss issues and practical solutions relating to reasoning over web data using a rule-based approach to forward-chaining; in particular, we identify the problem of ontology hijacking: new ontologies published on the Web re-defining the semantics of existing concepts resident in other ontologies. Our solution introduces consideration of authoritative sources.

Active Media Technology, 2009

Traditional reasoning tools for the Semantic Web cannot cope with Web scale data. One major direction to improve performance is parallelization. This article surveys existing studies, basic ideas and mechanisms for parallel reasoning, and introduces three major parallel applications on the Semantic Web: LarKC, MaRVIN, and Reasoning-Hadoop. Furthermore, this paper lays the ground for parallelizing unified search and reasoning at Web scale.

Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems, 2013

Ontology based knowledge management systems have a lot of potential: their applicability ranges from artificial intelligence, e.g., for knowledge representation and natural language processing, to information integration and retrieval systems, requirements analysis, and, most lately, to semantic web applications and workflow management systems. However the huge complexity of reasoning for ontologies with large TBoxes and/or ABoxes is often a barrier to their applicability in real-world settings especially those which are time sensitive. Materialization is a promising solution for scalable reasoning over ontologies with large ABoxes as it derives the implicit knowledge of an ontology and makes it available in a relational database. Although materialization can reduce the query answering time of an ontology, it has limitations in applications which require frequent update to the knowledge base. To overcome this problem, we developed a tool for incremental materialization which identifies the fragment of the ontology that needs to be updated due to the ABox or TBox change, thereby reducing the complexity of the exhaustive forward chaining required.

W3C recommendation, publishing open and machine-readable content on the Web has recently received a lot more attention, including from corporate and governmental bodies; there now exists a rich vein of heterogeneous RDF data published on the Web (the so-called “Web of Data”) accessible to all.

Lecture Notes in Computer Science, 2013

Ontologies are becoming increasingly important in largescale information systems such as healthcare systems. Ontologies can represent knowledge from clinical guidelines, standards, and practices used in the healthcare sector and may be used to drive decision support systems for healthcare, as well as store data (facts) about patients. Reallife ontologies may get very large (with millions of facts or instances). The effective use of ontologies requires not only a well-designed and well-defined ontology language, but also adequate support from reasoning tools. Main memory-based reasoners are not suitable for reasoning over large ontologies due to the high time and space complexity of their reasoning algorithms. In this paper, we present OwlOntDB, a scalable reasoning system for OWL 2 RL ontologies with a large number of instances, i.e., large ABoxes. We use a logic-based approach to develop the reasoning system by extending the Description Logic Programs (DLP) mapping between OWL 1 ontologies and datalog rules, to accommodate the new features of OWL 2 RL. We first use a standard DL reasoner to create a complete class hierarchy from an OWL 2 RL ontology, and translate each axiom and fact from the ontology to its equivalent datalog rule(s) using the extended DLP mapping. We materialize the ontology to infer implicit knowledge using a novel database-driven forward chaining method, storing asserted and inferred knowledge in a relational database. We evaluate queries using a modified SPARQL-DL API over the relational database. We show our system performs favourably with respect to query evaluation when compared to two main-memory based reasoners on several ontologies with large datasets including a healthcare ontology.

LDSR is a collection of datasets from th e Linked Open Data (LOD) W3C commun it y project, which have been selected and refin ed for th e purpose of presenting a us eful perspective to some of th e central LOD datasets and to present a good use-case for large-scale reasoning and data integration. The design objectives are as fol lows: (i) consistency with respect to the formal semanti cs, (ii) generality -no specific domain knowledge should be required to comprehend most of the semantics, and (iii) heterogeneity -data from mult iple data sources should be included. The current version of LDSR consists of about 440 million expli cit statements and includes DBP edia, Geonames, Wordnet, CIA Factbook, li ngvoj, and UMBEL. LDSR includes the ontologi es of th e datasets and th e following schemata, used by them: SKOS, FOAF, RSS, and Dublin Core.

2015 IEEE 31st International Conference on Data Engineering, 2015

Techniques for efficiently managing Semantic Web data have attracted significant interest from the data management and knowledge representation communities. A great deal of effort has been invested, especially in the database community, into algorithms and tools for efficient RDF query evaluation. However, the main interest of RDF lies in its blending of heterogeneous data and semantics. Simple RDF graphs can be seen as collections of facts, which may be further enriched with ontological schemas, or semantic constraints, based on which reasoning can be applied to infer new information. Taking into account this implicit information is crucial for answering queries.

2010

In order for Semantic Web applications to be successful a key component should be their ability to take advantage of rich content descriptions in meaningful ways. Reasoning consists a key part in this process and it consequently appears at the core of the Semantic Web architecture stack. From a practical point of view however, it is not always clear how applications may take advantage of the knowledge discovery capabilities that reasoning can offer to the Semantic Web. In this paper we present and survey current methods that can be used to integrate inference-based services with such applications. We argue that an important decision is to have reasoning tasks logically and physically distributed. To this end, we discuss relevant protocols and languages such as DIG and SPARQL and give an overview of our Knowledge Discovery Interface. Further, we describe the lessons-learned from remotely invoking reasoning services through the OWL API.

Proceedings of the 25th International Conference Companion on World Wide Web - WWW '16 Companion, 2016

In the semantic web, content is tagged with "meaning" or "semantics" to facilitate machine processing and web searching. In general, question answering systems that are built on top of reasoning and inference face a number of difficult issues. In this paper, we analyze scalability issues faced by a question answering system used by a knowledge base with science information that has been harvested from the web. Using this system, we will be able to answer questions that contain qualitative descriptors such as "groundbreaking", "top researcher", and "tenurable at university x". This question answering system has been built using ontologies, reasoning systems and custom based rules for the reasoning system. Furthermore, we evaluated the performance of our optimized backward chaining engine on supporting custom rules and designed the experimental environment including scalable datasets, rule sets, query sets and metrics and compared the experimental results with other in-memory ontology reasoning systems. The results show that our developed backward chaining ontology reasoning system has better scalability than in-memory reasoning systems.