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Scalable Highly Expressive Reasoner

A technology that provides ontology analytics (OWL-DL without nominals) over highly expressive ontologies.


Date Posted: July 1, 2008
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Update: July 15, 2008

Broken hyperlinks fixed in PDF file; documentation added for EL+ reasoner; some UOBM queries modified to include more than one variable in the result set.

What is Scalable Highly Expressive Reasoner (SHER)?

Scalable Highly Expressive Reasoner (SHER) is a breakthrough technology that provides ontology analytics over highly expressive ontologies (OWL-DL without nominals). SHER does not do any inferencing on load; hence it deals better with quickly changing data (the downside is, of course, that reasoning is performed at query time). The tool can reason on approximately seven million triples in seconds, and it scales to data sets with 60 million triples, responding to queries in minutes. It has been used to semantically index 300 million triples from medical literature.

SHER tolerates logical inconsistencies in the data, and it can quickly point you to these inconsistencies in the data and help you clean up inconsistencies before issuing semantic queries. The tool explains (or justifies) why a particular result set is an answer to the query; this explanation is useful for validation by domain experts.

How does it work?

SHER's reasoning technique relies on a novel combination of indexing the instances of the database from the perspective of reasoning. This indexing technique summarizes the instance data into a compact representation that is used for reasoning.

The tool uses this representation to efficiently filter instance data that is irrelevant for answering a certain query, and, in a process called refinement, it selectively decompresses portions of the summarized representation relevant for the query. The combination of summarization and refinement is key to the tool's scalability. Internally, SHER uses the popular open-source OWL-DL reasoner called Pellet to reason over the summarized data and obtain justifications for the data inconsistency.

SHER answers membership queries as well as conjunctive queries using a set of optimization techniques described in the following two papers:

These optimization techniques use summarization in the context of conjunctive querying; they also incorporate faster incomplete reasoning techniques into query answering. Therefore, SHER has an internal knob that can be used to get fast, incomplete answers to queries. This faster algorithm can help retrieve large result sets for most queries within a minute or two.


About the technology author(s):

Julian Dolby, Ph.D., is a researcher at IBM's T. J. Watson Research Center. His research has three foci: scripting languages and virtual machines; applications of program analysis to software quality and understanding for a variety of languages; and semantic Web technology.

Achille Fokoue, a researcher at IBM Watson Research Lab, works on problems related to knowledge representation and reasoning, data management, and information integration.

Aditya Kalyanpur, Ph.D, joined IBM Watson as a research staff member in 2006. His research interests lie in knowledge representation and reasoning (especially in the context of the semantic Web), ontology engineering, and in applying semantics to software modeling and design.

Li Ma, Ph.D, joined IBM China Research Lab as a research staff member in 2003. He is interested in semantic Web data management, pattern recognition, and the application of semantic technologies to enterprise information integration and search.

Edith Schonberg, Ph.D., a manager at Watson Research Center, is interested in broadening the use of the semantic Web by creating better tools and inventing new applications. She also manages a project on diagnosing and eliminating run-time bloat in Java® applications.

Kavitha Srinivas, Ph.D., started the Scalable Highly Expressive Reasoner project in 2005 in order to build a scalable ontology reasoning engine for OWL ontologies. She is interested in the application of semantic technologies such as RDF and OWL to different domains and in harnessing the power of linked, open data for improving search and information retrieval.

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Related technologies

For platform(s):
Linux, Windows

For topics:
analysis, Databases and data management, ontology


 

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