Birte Glimm

Konclude: System Description

This paper introduces Konclude, a high-performance reasoner for the Description Logic SROIQV. The supported ontology language is a superset of the logic underlying OWL 2 extended by nominal schemas, which allows for expressing arbitrary DL-safe rules. Konclude’s reasoning core is primarily based on the well-known tableau calculus for expressive Description Logics. In addition, Konclude also incorporates adaptations of more specialised procedures, such as consequence-based reasoning, in order to support the tableau algorithm. Konclude is designed for performance and uses well-known optimisations such as absorption or caching, but also implements several new optimisation techniques. The system can furthermore take advantage of multiple CPU’s at several levels of its processing architecture. This paper describes Konclude’s interface options, reasoner architecture, processing workflow, and key optimisations. Furthermore, we provide results of a comparison with other widely used OWL 2 reasoning systems, which show that Konclude performs eminently well on ontologies from any language fragment of OWL 2.

The OWL Reasoner Evaluation (ORE) 2015 Competition Report

The OWL Reasoner Evaluation competition is an annual competition (with an associated workshop) that pits OWLxa02 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWLxa02 DL and EL). In this paper, we discuss the design, execution and results of the 2015 competition with particular attention to lessons learned for benchmarking, comparative experiments, and future competitions.

Nominal schema absorption

Nominal schemas have recently been introduced as a new approach for the integration of DL-safe rules into the Description Logic framework. The efficient processing of knowledge bases with nominal schemas remains, however, challenging. We address this by extending the well-known optimisation of absorption as well as the standard tableau calculus to directly handle the (absorbed) nominal schema axioms. We implement the resulting extension of standard tableau calculi in a novel reasoning system and we integrate further optimisations. In our empirical evaluation, we show the effect of these optimisations and we find that the proposed approach performs well even when compared to other DL reasoners with dedicated rule support.

Coupling Tableau Algorithms for Expressive Description Logics with Completion-Based Saturation Procedures

Nowadays, saturation-based reasoners for the OWL EL profile are able to handle large ontologies such as SNOMED very efficiently. However, saturation-based reasoning procedures become incomplete if the ontology is extended with axioms that use features of more expressive Description Logics, e.g., disjunctions. Tableau-based procedures, on the other hand, are not limited to a specific OWL profile, but even highly optimised reasoners might not be efficient enough to handle large ontologies such as SNOMED. In this paper, we present an approach for tightly coupling tableau- and saturation-based procedures that we implement in the OWL DL reasoner Konclude. Our detailed evaluation shows that this combination significantly improves the reasoning performance on a wide range of ontologies.

Reasoning with Nominal Schemas through Absorption

Nominal schemas have recently been introduced as a new approach for the integration of DL-safe rules into the Description Logic framework. The efficient processing of knowledge bases with nominal schemas remains, however, challenging. We address this by extending the well-known optimisation of absorption as well as the standard tableau calculus to directly handle the (absorbed) nominal schema axioms. We implement the resulting extension of standard tableau calculi in the novel reasoning system Konclude and present further optimisations. In our empirical evaluation, we show the effect of these optimisations and we find that the proposed nominal schema handling performs well even when compared to (hyper)tableau systems with dedicated rule support.

Semantic exploitation of implicit patent information

In recent years patents have become increasingly important for businesses to protect their intellectual capital and as a valuable source of information. Patent information is, however, not employed to its full potential and the interpretation of structured and unstructured patent information in large volumes remains a challenge. We address this by proposing an integrated interdisciplinary approach that uses natural language processing and machine learning techniques to formalize multilingual patent information in an ontology. The ontology further contains patent and domain specific knowledge, which allows for aligning patents with technological fields of interest and other business-related artifacts. Our empirical evaluation shows that for categorizing patents according to well-known technological fields of interest, the approach achieves high accuracy with selected feature sets compared to related work focussing on monolingual patents. We further show that combining OWL RL reasoning with SPARQL querying over the patent knowledge base allows for answering complex business queries and illustrate this with real-world use cases from the automotive domain.

15 Years of Semantic Web: An Incomplete Survey

AbstractnIt has been 15xa0years since the first publications proposed the use of ontologies as a basis for defining information semantics on the Web starting what today is known as the Semantic Web Research Community. This work undoubtedly had a significant influence on AI as a field and in particular the knowledge representation and Reasoning Community that quickly identified new challenges and opportunities in using Description Logics in a practical setting. In this survey article, we will try to give an overview of the developments the field has gone through in these 15xa0years. We will look at three different aspects: the evolution of Semantic Web Language Standards, the evolution of central topics in the Semantic Web Community and the evolution of the research methodology.

Scalable Reasoning by Abstraction Beyond DL-Lite

Recently, it has been shown that ontologies with large datasets can be efficiently materialized by a so-called abstraction refinement technique. The technique consists of the abstraction phase, which partitions individuals into equivalence classes, and the refinement phase, which re-partitions individuals based on entailments for the representative individual of each equivalence class. In this paper, we present an abstraction-based approach for materialization in (mathrm {DLtext{- }Lite}), i.e. we show that materialization for (mathrm {DLtext{- }Lite}) does not require the refinement phase. We further show that the approach is sound and complete even when adding disjunctions and nominals to the language. The proposed technique allows not only for faster materialization and classification of the ontologies, but also for efficient consistency checking; a step that is often omitted by practical approaches based on query rewriting. A preliminary empirical evaluation on both real-life and benchmark ontologies demonstrates that the approach can handle ontologies with large datasets efficiently.

The OWL Reasoner Evaluation (ORE) 2015 Resources

The OWL Reasoner Evaluation (ORE) Competition is an annual competition (with an associated workshop) which pits OWL 2 compliant reasoners against each other on various standard reasoning tasks over naturally occurring problems. The 2015 competition was the third of its sort and had 14 reasoners competing in six tracks comprising three tasks (consistency, classification, and realisation) over two profiles (OWL 2 DL and EL). In this paper, we outline the design of the competition and present the infrastructure used for its execution: the corpora of ontologies, the competition framework, and the submitted systems. All resources are publicly available on the Web, allowing users to easily re-run the 2015 competition, or reuse any of the ORE infrastructure for reasoner experiments or ontology analysis.

On maintaining semantic networks: challenges, algorithms, use cases

Purpose – The purpose of this paper is to provide methods and algorithms to maintain a semantic network (SN). In previous work, the authors introduced the SN approach for bridging the gap of aligning enterprise information with business processes, i.e. for discovering explicit relations between them. What has been neglected so far, however, is SN maintenance, which is required to keep an SN consistent, complete and up-to-date. Design/methodology/approach – The paper illustrates an approach for SN maintenance. Specifically, the authors show how an SN evolves over time, classify properties of objects and relations captured in an SN and show how these properties can be maintained. An empirical evaluation, which is based on synthetic and real-world data, investigates the performance, scalability and practicability of the proposed algorithms. Findings – The authors prove the feasibility of the introduced algorithms in terms of runtime performance with a proof-of-concept implementation. Further, a real-world ca...