Description Logic Reasoning Research Articles

A polynomial Time Subsumption Algorithm for Nominal Safe [formula omitted] under Rational Closure

Description Logics (DLs) under Rational Closure (RC) is a well-known framework for non-monotonic reasoning in DLs. In this paper, we address the concept subsumption decision problem under RC for nominal safe ELO⊥, a notable and practically important DL representative of the OWL 2 profile OWL 2 EL. Our contribution here is to define a polynomial time subsumption procedure for nominal safe ELO⊥ under RC that relies entirely on a series of classical, monotonic EL⊥ subsumption tests. Therefore, any existing classical monotonic EL⊥ reasoner can be used as a black box to implement our method. We then also adapt the method to one of the known extensions of RC for DLs, namely Defeasible Inheritance-based DLs without losing the computational tractability.

Verification of fuzzy UML models with fuzzy Description Logic

The fuzzy UML model has been introduced to enable the conceptual modeling of imprecise data in many applications. How to prevent, detect, and correct errors as early as possible in the modeling process by verifying the correctness of fuzzy UML models is desirable. But it is difficult to manually verify the correctness of the models. Thanks to the effective reasoning service of fuzzy Description Logics (DLs), they are considered as natural candidates for supporting the verification of fuzzy UML models. In this paper we propose a complete and practical approach for verifying fuzzy UML models with fuzzy DLs. In detail, we first propose an approach for transforming a fuzzy UML model into a fuzzy DL knowledge base. Then we further reduce the verification tasks of the fuzzy UML model to reasoning problems of the transformed fuzzy DL knowledge base. Also, the correctness of the approach is proved, and a running example is provided to explain the approach in detail. Final we implemented a prototype that can transform fuzzy UML models into fuzzy DL knowledge bases. Case studies show that the proposed approach is feasible and the prototype translator actually works. By means of our approach and tool, the transformed fuzzy DL knowledge base and reasoning tasks can be input into the existing fuzzy DL reasoners, and the reasoning results reported by the reasoners can be passed back to the designer to improve the quality of the fuzzy UML model.

Hidden information retrieval and evaluation method and tools utilising ontology reasoning applied for financial fraud analysis.

The paper summarizes a semantic association evaluation and reasoning method, utilising domain and problem solving ontologies. The method combines algorithms for data aggregation and logic reasoning utilising concrete financial data. As an outcome method supports suspicious behaviour recognition of money loundering schemes. These scenarios and schemes are implemented by the analysts using ontology-based constructs. Provided tools cover all stages of data processing starting from structural data extraction and migration, aggregation to reasoning using logic (DL and FOL) constructs. Advances in automatic reasoning and the availability of semantic processing tools encourage analysts to extend existing link analysis methods towards contextual data processing. To demonstrate presented method, a proof of concept environment IAFEC Ontology Toolkit has been described. It delivers initial financial fraud identification schemes (rules) based on set of problem solving ontologies. The novelty in such approach comes from incorporating heterogeneous types of data, which usually are processed by graph methods. The semantic tool, extend capabilities of graph-based (homogeneous) approach by delivering context-aware indirect association identification, and inference path explanation and inspection capabilities. Presented material describes the method and analytical algorithms, which demonstrate description logic reasoning and graph-based semantic association identification and ranking. Developed method has been implemented, as a Protégé OWL 5.0 environment extension, supplemented with web-services delivering distributed data processing, aggregation (not available in ontology languages). The environment provides declarative processing capabilities enabling analysts to design configurable processing flow, and new financial fraud identification schemes.

Minimally inconsistent reasoning in Semantic Web.

Reasoning with inconsistencies is an important issue for Semantic Web as imperfect information is unavoidable in real applications. For this, different paraconsistent approaches, due to their capacity to draw as nontrivial conclusions by tolerating inconsistencies, have been proposed to reason with inconsistent description logic knowledge bases. However, existing paraconsistent approaches are often criticized for being too skeptical. To this end, this paper presents a non-monotonic paraconsistent version of description logic reasoning, called minimally inconsistent reasoning, where inconsistencies tolerated in the reasoning are minimized so that more reasonable conclusions can be inferred. Some desirable properties are studied, which shows that the new semantics inherits advantages of both non-monotonic reasoning and paraconsistent reasoning. A complete and sound tableau-based algorithm, called multi-valued tableaux, is developed to capture the minimally inconsistent reasoning. In fact, the tableaux algorithm is designed, as a framework for multi-valued DL, to allow for different underlying paraconsistent semantics, with the mere difference in the clash conditions. Finally, the complexity of minimally inconsistent description logic reasoning is shown on the same level as the (classical) description logic reasoning.

Керований онтологіею інструмент для застосування стилей програмування

Діяльність програміста буде більш ефективною, а програмне забезпечення зрозумілим коли при створенні програмного забезпечення застосовуються стилі (стандарти) програмування, які забезпечують зрозумілість програмних текстів. Мета: В цьому дослідженні представлено засіб для реалізації нового методу, який застосовує онтології і автоматизує процеси використання стилів програмування. Зокрема розглядається представлення стандартів в формі онтології і застосування ризонеру дескриптивної логіки для програміста. Метою статті є розробка засобу для підтримки керованого онтологією застосування стилів програмування. Методи дослідження: онтологічне представлення стилів програмування; об’єктно-орієнтоване програмування; кероване онтологією застосування стилів програмування. Результати: отримано архітектуру та мовою Java реалізовано засоби які забезпечують інструментальну підтримку методу керованого онтологією застосування стилів програмування. На прикладі іменування стандарту програмування мови Java надано особливості реалізації і застосування засобу. Обговорення: Застосування стандартів програмування при конструюванні програмного забезпечення, відсутність засобів автоматизації процесів застосування стандартів програмування, засіб на основі нового методу керованого онтологією застосування стилів програмування, приклад реалізації архітектури засобу для іменувань стандарту мови Java.

A quality requirements model and verification approach for system of systems based on description logic

System of systems engineering (SoSE) involves the complex procedure of translating capability needs into the high-level requirements for system of systems (SoS) and evaluating how the SoS quality requirements meet their capability needs. One of the key issues is to model the SoS requirements and automate the verification procedure. To solve the problem of modeling and verification, meta-models are proposed to refine both functional and non-functional characteristics of the SoS requirements. A domain-specific modeling language is defined by extending Unified Modeling Language (UML) class and association with fuzzy constructs to model the vague and uncertain concepts of the SoS quality requirements. The efficiency evaluation function of the cloud model is introduced to evaluate the efficiency of the SoS quality requirements. Then a concise algorithm transforms the fuzzy UML models into the description logic (DL) ontology so that the verification can be automated with a DL reasoner. This method implements modeling and verification of high-level SoS quality requirements. A crisp case is used to facilitate and demonstrate the correctness and feasibility of this method.

An approach to translating OCL invariants into OWL 2 DL axioms for checking inconsistency

Checking the design specification for contradictions at the early phase of the software development process is crucial to ensure that the design is implementable. However, the high expressivity of OCL makes manual inconsistency checking a difficult task. In addition, the developers cannot detect these problems by OCL itself due to its lack of automated reasoning support. We investigate an approach to translating OCL invariants into OWL 2 DL axioms. We do this where the OCL expression contained in an invariant is converted to the corresponding OWL 2 DL class expression in a compositional way. Our approach covers the OCL expressions including four: PrimaryExp, RelationalExp, LogicalExp and IfExp types. Considering the distinction between the CWA and OWA, we achieve correct translation from RelationalExp using closure axiom. Also, we employ an ontology design pattern to overcome the limitations of OWL 2 DL expressivity when translating IfExp. Then inconsistency checking is done through description logic reasoning by the OWL 2 DL high-performance reasoner. We construct an inductive proof to establish the correctness of our translation approach. Moreover, we evaluate our approach using the implemented TUCO tool prototype.

A Hybrid DL and RL Based Reasoner for Optimum Entailments in Ontologies

Objective: To present a reasoner based on the hybrid approach which integrates the Description Logic (DL) reasoner with Rule Logic (RL) based reasoner for optimizing the reasoning process in ontology's. Methods/Statistical Analysis: The idea here is to integrate the capabilities of both atomic reasoners. This hybrid reasoner will first load the ontology and make this ontology active; it will then separate the Terminological axioms (Tbox) from the Assertion axiom (Abox). It will thus call the Description Logic (DL) based reasoner for Tbox reasoning which will perform reasoning by classifying and realizing the elements of a given ontology. Afterwards it will upload the rules set for performing Rule based entailments. It will thus perform the assertion reasoning based on rules set and the given axioms. Findings: This approach integrates the effective and efficient inferencing capability of a DL algorithm based reasoner with the scalability of rule based reasoners. The experimental research shows that by following the hybrid approach the entailments can be made more efficient and scalable as compared to the other reasoners. This framework was successful in performing the Tbox entailments by calling the Pellet reasoner and it achieves a faster execution of Abox queries through the use of Generic Rule Reasoner of Jena Framework which perform the entailments in the RDF Schema and the rule set. This approach enriches the reasoning task in two ways first it separates the two schemas of any given ontology and secondly it handles the memory more efficiently. Applications: This framework gives us an efficient way for performing entailments in any kind of ontology whether it is a simple ontology or ontology based on OWL2 profile languages.

Tableau reasoning for description logics and its extension to probabilities

The increasing popularity of the Semantic Web drove to a widespread adoption of Description Logics (DLs) for modeling real world domains. To help the diffusion of DLs, a large number of reasoning algorithms have been developed. Usually these algorithms are implemented in procedural languages such as Java or C++. Most of the reasoners exploit the tableau algorithm which features non-determinism, that is not easily handled by those languages. Prolog directly manages non-determinism, thus is a good candidate for dealing with the tableau’s non-deterministic expansion rules. We present TRILL, for “Tableau Reasoner for descrIption Logics in proLog”, that implements a tableau algorithm and is able to return explanations for queries and their corresponding probability, and TRILL P , for “TRILL powered by Pinpointing formulas”, which is able to compute a Boolean formula representing the set of explanations for a query. Reasoning on real world domains also requires the capability of managing probabilistic and uncertain information. We show how TRILL and TRILL P can be used to compute the probability of queries to knowledge bases following DISPONTE semantics. Experiments comparing these with other systems show the feasibility of the approach.

Design of the Tableau Reasoner TGC2 for Description Logics

Ontologies have been applied in a wide range of practical domains. They play a key role in data modeling, information integration, and the creation of semantic web services, intelligent web sites and intelligent software agents. The Web ontology language OWL, recommended by W3C, is based on description logics (DLs). Automated reasoning in DLs is very important for the success of OWL, as it provides support for visualization, debugging, and querying of ontologies. The existing ontology reasoners are not yet satisfactory, especially when dealing with qualified number restrictions and large ontologies. In this paper, we present the design of our new reasoner TGC2, which uses tableaux with global caching for reasoning in E xpTime-complete DLs. The characteristic of TGC2 is that it is based on our tableau methods with the optimal ( ExpTime) complexity, while the existing well-known tableau-based reasoners for DLs have a non-optimal complexity (at least NExpTime). We briefly describe the tableau methods used by TGC2. We then provide the design principles of TGC2 and some important optimization techniques for increasing the efficiency of this reasoner. We also present preliminary evaluation results of TGC2. They show that TGC2 deals with qualified number restrictions much better than the other existing reasoners.

A Method for Matching Crowd‐sourced and Authoritative Geospatial Data

AbstractA method for matching crowd‐sourced and authoritative geospatial data is presented. A level of tolerance is defined as an input parameter as some difference in the geometry representation of a spatial object is to be expected. The method generates matches between spatial objects using location information and lexical information, such as names and types, and verifies consistency of matches using reasoning in qualitative spatial and description logic. We test the method by matching geospatial data from OpenStreetMap and the national mapping agencies of Great Britain and France. We also analyze how the level of tolerance affects the precision and recall of matching results for the same geographic area using 12 different levels of tolerance within a range of 1 to 80 m. The generated matches show potential in helping enrich and update geospatial data.

Answering Fuzzy Conjunctive Queries Over Finitely Valued Fuzzy Ontologies

Fuzzy Description Logics (DLs) provide a means for representing vague knowledge about an application domain. In this paper, we study fuzzy extensions of conjunctive queries (CQs) over the DL $${\mathcal {SROIQ}}$$ based on finite chains of degrees of truth. To answer such queries, we extend a well-known technique that reduces the fuzzy ontology to a classical one, and use classical DL reasoners as a black box. We improve the complexity of previous reduction techniques for finitely valued fuzzy DLs, which allows us to prove tight complexity results for answering certain kinds of fuzzy CQs. We conclude with an experimental evaluation of a prototype implementation, showing the feasibility of our approach.

Isolation-based subsumption reasoning with enormous volume of web ontologies for scalable semantic service discovery

The description of service with expressive Web Ontology Language (OWL) ontologies facilitates semantics-based service advertisement and discovery. However, modern description logic (DL) reasoners do not provide sufficiently tolerable and scalable infrastructure for the advertisement and discovery of service profiles on the basis of enormous volumes of OWL ontologies on the web. In this paper, we present an import graph-based tolerable and scalable reasoner that can be implemented on top of off-the-shelf DL reasoners. The reasoner has been designed to reduce the reasoning workload of these reasoners by excluding ontologies not relevant for resolving a given subsumption query. On average, the proposed method isolates about 87.6%-99.9% of all ontologies when performing a given reasoning task. This leads to a significant reduction, by at least 85.6%, in the response time of subsumption reasoning when a naive reasoning strategy involving the loading of all ontologies onto a reasoner is used.

Formal Specification of the Framework for NSSA

As the computer network has evolved to provide the user many services, the attacks on these networks to disrupt the services and to gain access to resources has also evolved. New entities in form of services, hardware, network protocols etc. are being added to the network, which is leading to new ways to attack the network. The complexity of the system is increasing so fast that it is becoming increasingly difficult for network administrator to comprehend the situation and react in an appropriate manner. Situation becomes more complex as there is no uniform terminology. Though serious efforts in form of Common Vulnerability Enumeration (CVE), Common Weakness Enumeration (CWE), Common Attack Pattern Enumeration and Classification (CAPEC) etc. has been made, still a long way is to go. In this paper we have proposed a formal specification of the framework for network security situational awareness (NSSA) using ontological engineering approach. We have modeled a computer network by modeling its components i.e. hardware, software, services using ontology. Also vulnerabilities and attacks on these computers are modeled. We populate our ontology with various instances of vulnerabilities, CVSS scores, attacks and possible services in the network. Knowledge representation methods are used in order to provide Description Logic reasoning and inference over network security status concept. Secondly we propose an ontology based system which predicts probable attacks using inference and information provided by the environment.

Pay-As-You-Go Description Logic Reasoning by Coupling Tableau and Saturation Procedures

Nowadays, saturation-based reasoners for the OWL EL profile of the Web Ontology Language are able to handle large ontologies such as SNOMED very efficiently. However, it is currently unclear how saturation-based reasoning procedures can be extended to very expressive Description Logics such as SROIQ--the logical underpinning of the current and second iteration of the Web Ontology Language. Tableau-based procedures, on the other hand, are not limited to specific Description Logic languages or OWL profiles, but even highly optimised tableau-based 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 for a wide range of ontologies.

Semantic reasoning on mobile devices: Do Androids dream of efficient reasoners?

The massive spread of mobile computing in our daily lives has attracted a huge community of mobile application (apps) developers. These developers can take advantage of the benefits of semantic technologies (such as knowledge sharing and reusing, and knowledge decoupling) to enhance their applications. Moreover, the use of semantic reasoners would enable them to create more intelligent applications capable of discovering new knowledge, inferred from the available information.However, using semantic APIs and reasoners on current mobile devices is not a trivial task. In this paper, we show that the most popular current available Description Logics (DL) reasoners can be used on Android-based devices, and detail the efforts needed to port them to the Android platform. We also analyze the performance of these reasoners on current smartphones/tablets against more than 300 ontologies from the ORE 2013 ontology set, showing that, despite a notable difference with respect to desktop computers, their use is feasible.

Probabilistic Description Logics under the distribution semantics

Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, t hat we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then defines a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE KBs. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE shows that it can handle probabilistic KBs of realistic size.

A description logic-based policy compliance checker for trust negotiation

Automated trust negotiation (ATN) is an approach to regulating the gradual exchange of sensitive resources, which are protected by access control policies, between two strangers to establish mutual trust in open distributed systems. Policy compliance checkers of ATN determine which credentials satisfy an access control policy and whether a particular set of credentials satisfies the relevant policy. We propose a description logic-based approach to policy compliance checking, in which the description logic (DL) \(\mathcal {SHOIN(D)}\) is exploited to formalize credentials and policies of ATN, and the state-of-the-art DL reasoners are leveraged for policy compliance checking. By exploring the semantics of credentials and policies defined by DL, our approach can promote the success of a negotiation whenever it is semantically possible. As long as a policy can be satisfied, our approach can find the credentials satisfying the policy. These credentials can be either syntactically defined in the policy or semantically imply those defined. In addition, benefiting from DL reasoning, attribute delegations that are modeled as semantic relations among attributes can be retrieved by our approach as the evidence of a negotiator’s satisfaction of an access control policy. This evidence is quite necessary in the ATN environment where negotiators are usually strangers belonging to different domains without a common knowledge of delegations.

Knowledge Representation Using Type-2 Fuzzy Rough Ontologies in Ontology Web Language

Nowadays semantic web languages play a vital role in representing ontologies. In order to deal with uncertainty in the semantic web languages, we need a concrete procedure to represent such information. We may tackle this problem by extending the current semantic web languages with uncertainty. In this article, we extend the current standard language OWL2 (version 2 of the Web Ontology Language), to deal with uncertainty features and also propose an appropriate methodology to represent type-2 fuzzy rough ontologies by the aid of OWL2 annotation properties. We also provide a plug-in, used to edit type-2 fuzzy rough ontologies using OWL2 annotation properties and parsers that translate the type-2 fuzzy rough ontologies into the languages supported by current description logic (DL) reasoners. Moreover, we conduct a large user group study to prove the effectiveness of the proposed work.

Ontology-based feature modeling: An empirical study in changing scenarios

A software product line (SPL) is a set of software systems that have a particular set of common features and that satisfy the needs of a particular market segment or mission. Feature modeling is one of the key activities involved in the design of SPLs. The feature diagram produced in this activity captures the commonalities and variabilities of SPLs. In some complex domains (e.g., ubiquitous computing, autonomic systems and context-aware computing), it is difficult to foresee all functionalities and variabilities a specific SPL may require. Thus, Dynamic Software Product Lines (DSPLs) bind variation points at runtime to adapt to fluctuations in user needs as well as to adapt to changes in the environment. In this context, relying on formal representations of feature models is important to allow them to be automatically analyzed during system execution. Among the mechanisms used for representing and analyzing feature models, description logic (DL) based approaches demand to be better investigated in DSPLs since it provides capabilities, such as automated inconsistency detection, reasoning efficiency, scalability and expressivity. Ontology is the most common way to represent feature models knowledge based on DL reasoners. Previous works conceived ontologies for feature modeling either based on OWL classes and properties or based on OWL individuals. However, considering change or evolution scenarios of feature models, we need to compare whether a class-based or an individual-based feature modeling style is recommended to describe feature models to support SPLs, and especially its capabilities to deal with changes in feature models, as required by DSPLs. In this paper, we conduct a controlled experiment to empirically compare two approaches based on each one of these modeling styles in several changing scenarios (e.g., add/remove mandatory feature, add/remove optional feature and so on). We measure time to perform changes, structural impact of changes (flexibility) and correctness for performing changes in our experiment. Our results indicate that using OWL individuals requires less time to change and is more flexible than using OWL classes and properties. These results provide insightful assumptions towards the definition of an approach relying on reasoning capabilities of ontologies that can effectively support products reconfiguration in the context of DSPL.