Description Logic Reasoning Research Articles

Consistent Knowledge Discovery from Evolving Ontologies

Deductive reasoning and inductive learning are the most common approaches for deriving knowledge. In real world applications when data is dynamic and incomplete, especially those exposed by sensors, reasoning is limited by dynamics of data while learning is biased by data incompleteness. Therefore discovering consistent knowledge from incomplete and dynamic data is a challenging open problem. In our approach the semantics of data is captured through ontologies to empower learning (mining) with (Description Logics) reasoning. Consistent knowledge discovery is achieved by applying generic, significative, representative association semantic rules. The experiments have shown scalable, accurate and consistent knowledge discovery with data from Dublin.

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.

Optimising fuzzy description logic reasoners with general concept inclusion absorption

General Concept Inclusion (GCI) absorption algorithms have shown to play an important role in classical Description Logic (DL) reasoners. They allow to transform GCIs into simpler forms to which we may apply specialised inference rules, returning important performance gains. In this work, we develop the first absorption algorithm for fuzzy DLs, implement it in the fuzzyDL reasoner and evaluate it extensively over both classical and fuzzy ontologies. The results show that our algorithm improves the performance of the reasoner significantly.

Answering Continuous Description Logic Queries

During the last years, mobile computing has been the focus of many research efforts, due mainly to the ever-growing use of mobile devices. In this context, there is a need to manage dynamic data, such as location data or other data provided by sensors. As an example, the continuous processing of location-dependent queries has been the subject of thorough research. However, there is still a need of highly expressive ways of formulating queries, augmenting in this way the systems' answer capabilities. Regarding this issue, the modeling power of Description Logics (DLs) and the inferring capabilities of their attached reasoners could fulfill this new requirement. The main problem is that DLs are inherently oriented to model static knowledge, that is, to capture the nature of the modeled objects, but not to handle changes in the property values (which requires a full ontology reclassification), as it is common in mobile computing environments (e.g., the location is expected to vary continually). In this paper, the authors present a novel approach to process continuous queries that combines 1) the DL reasoning capabilities to deal with static knowledge, with 2) the efficient data access provided by a relational database to deal with volatile knowledge. By marking at modeling time the properties that are expected to change during the lifetime of the queries, the authors'system is able to exploit both the results of the classification process provided by a DL reasoner, and the low computational costs of a database when accessing changing data (mobile environments, semantic sensors, etc.), following a two-step continuous query processing that enables us to handle continuous DL queries efficiently. Experimental results show the feasibility of the authors' approach.

Modeling, conflict detection, and verification of a new virtualization role‐based access control framework

AbstractIn the last 10 years, virtualization has become a widespread technique in cloud computing; however, few of the access control models have ever addressed the security issue of multi‐domain and virtualized network management; this paper enhanced the classic role‐based access control model through two concepts: domain and virtual machine. We defined a new model named VRBAC in which authorized users can migrate or copy virtual machines from one domain to another without causing a conflict. Domain users or groups are allowed to share permissions of not only resources like shared files but also virtual machines with others either from the same or a different domain. Three kinds of VRBAC policy conflicts are defined in forms of ontologies, which provide extra access to description logic reasoning and facilitate the policy conflict detection. The experimental results based on Microsoft Active Directory and VMware vSphere suggest that all policy conflicts can be detected effectively and efficiently. Moreover, the generated reports can provide conflict details such as conflict types, positions, and causes, which will serve as guidance for further resolution of the improper authorizations and access violations. Copyright © 2014 John Wiley & Sons, Ltd.

Consequence-based and fixed-parameter tractable reasoning in description logics

In this paper we investigate the consequence-based algorithms that are nowadays commonly used for subsumption reasoning with description logic ontologies, presenting the following novel results. First, we present a very general consequence-based reasoning algorithm that can be instantiated so as to capture the essential features of the known algorithms. Second, we use this algorithm to develop a novel framework for a quantitative and parametric analysis of the complexity of subsumption reasoning in description logic ontologies. Our approach is based on the notion of a decomposition—a graph-like structure that, roughly speaking, summarizes the models relevant during ontology reasoning. We identify width and length as decomposition parameters that determine the “level” of combinatorial reasoning. We prove that, when parameterized by a decomposition, our consequence-based algorithm runs in time that is fixed-parameter tractable in width and length. Third, we briefly discuss how length and width characterize the behavior of tableau algorithms. Fourth, we show that the width and length of existing ontologies are reasonably small, which, we believe, explains the good performance of consequence-based algorithms in practice. We thus obtain a sound foundation for a practical implementation of ontology reasoners, as well as a way to analyze the reasoners' performance.

Research on Contradiction Problem-Oriented Description Logic ALCD-ES

The raise of extension set provides reasonable logical foundation for the automatic solving of contradiction problem. This paper views contradiction as an unsatiable concept, an unrealizable action or an item that can't meet the satisfaction of a certain concept, analyzes a representation language in face of contradiction problem, which is, a new extension description logic--ALC D-ES . Syntax definition, semantic explanation and reasoning algorithm Tableau D-ES are given in details, which lay the theoretical foundation for the automatic solving method of contradiction problem based on extension description logic reasoning.

Model-theoretic inseparability and modularity of description logic ontologies

The aim of this paper is to introduce and study model-theoretic notions of modularity in description logic and related reasoning problems. Our approach is based on a generalisation of logical equivalence that is called model-theoretic inseparability. Two TBoxes are inseparable w.r.t. a vocabulary Σ if they cannot be distinguished by the Σ-reducts of their models and thus can equivalently be replaced by one another in any application where only vocabulary items from Σ are relevant. We study in-depth the complexity of deciding inseparability for the description logics EL and ALC and their extensions with inverse roles. We then discuss notions of modules of a TBox based on model-theoretic inseparability and develop algorithms for extracting minimal modules from acyclic TBoxes. Finally, we provide an experimental evaluation of our module extraction algorithm based on the large-scale medical TBox Snomed ct.

Consistency reasoning in lattice-based fuzzy Description Logics

Fuzzy Description Logics have been widely studied as a formalism for representing and reasoning with vague knowledge. One of the most basic reasoning tasks in (fuzzy) Description Logics is to decide whether an ontology representing a knowledge domain is consistent. Surprisingly, not much is known about the complexity of this problem for semantics based on complete De Morgan lattices. To cover this gap, in this paper we study the consistency problem for the fuzzy Description Logic L-SHI and its sublogics in detail.The contribution of the paper is twofold. On the one hand, we provide a tableaux-based algorithm for deciding consistency when the underlying lattice is finite. The algorithm generalizes the one developed for classical SHI. On the other hand, we identify decidable and undecidable classes of fuzzy Description Logics over infinite lattices. For all the decidable classes, we also provide tight complexity bounds.

Representations of Topological Relations Between Simple Regions in Description Logics

This paper proposes an operational approach to (1) formalize, in Description Logics (DL), the topological relations between simple regions and (2) automatically check whether a set of relations is consistent. The solution allows for the use of traditional DL reasoners (Pellet, Fact++, etc.) to check the consistency of relations and detect the sources of error. The solution does not require any specific extension of the DL or reasoner. The authors demonstrate how to apply this approach with Protégé and Fact++. Different spatial relations in agricultural and environmental applications are also provided to illustrate the possible uses of our method.

A knowledge-driven approach for process supervision in chemical plants

In this work, an ontology-based framework for process supervision in chemical plants is presented. A conceptualization of equipment, control systems and hazards has been developed. This conceptual model includes the semantic of each modeled term in order to obtain a heavyweight ontology. The ontology has been formalized using Description Logic (DL) (Krötzsch et al., 2012). A knowledge-driven approach has been adopted in order to demonstrate how DL reasoning could be used to support process supervision, detecting and diagnosing faults, without the help of external agents. In the proposed approach, a DL reasoner adds implicit facts to the ontology through forward chaining reasoning, from the current measurements to the characterization of hazards. Additionally, the system is able to check knowledge consistency and formally explain the obtained results. The system functionality has been illustrated in the Tennessee Eastman process.benchmark.

A formalization for semantic location granules

Location-based services have become an increasingly interesting research area in the last two decades. However, in many scenarios, dealing with the most precise location coordinates is not the best solution since people structure the world in geographic areas instead of coordinates. Since humans work with abstractions, and names are the way we refer to those abstractions, introducing semantics in geographic definitions becomes natural. For example, users can be interested in states with vacation resorts and may want to retrieve the state names, instead of the exact geographic limits of such states. Moreover, semantics introduces new challenges, such as how to exploit the location semantics to infer new information from known definitions. For instance, we may want a system to automatically obtain the value added tax (VAT) that should be applied by a shop in Madrid, inferring the applicable tax by considering the economic area where Madrid is included (in this case, Spain); notice that the VAT should not be inferred from a bigger economic area, like Europe, although it also includes Madrid geographically. Thus, the expression of locations at different granularities extends the traditional location-based query processing to consider the most appropriate semantics for each user. In this article, adopting description logics (DLs) as a base formalism, we provide a formalization of the notion of semantic location granule and semantic granule map. We benefit from the underlying semantics of the different granularities to extend the expressivity of location-based queries and automatically discover and infer new knowledge. The model we propose uses a DL reasoner to infer new granules relationships. In particular, a DL reasoner can infer containment and intersection relationships between location granules (and help to obtain several more relationships), which provides the way to introduce semantics in location-based queries. This is done within the logical frame of DLs, thus ensuring that our approach can be supported by existing regular DL reasoners (such as Pellet, Racer Pro, and HermiT) without the need to extend their reasoning capabilities.

Research of Semantic Information Retrieval Based on Conceptual Analysis of User Queries

With the spread and the rapid development of Internet, online information increases greatly. So ,how to organize and process the large amount of this information becomes a challenge. An important research of information retrieval technology is how to express users' intention accurately and diagnose the relations between Web pages and users' need. In this paper, a semantic information retrieval method based on conceptual analysis of user queries is proposed. The new method tightly integrates description logic reasoning and traditional information retrieval technique. In it, the user's search intention is expressed by description logic and the relevant Web pages are recalled by means of the traditional search engine. Further, the fuzzy description logic is introduced to confirm the relations between Web pages and users’ search needs. Hence, a prototype is implemented. The experimental results show the method proposed in this paper could enormously enhance the search result degree of correlation. This paper’s work is an important component of conceptual retrieval model, which provides valuable ideas and methods for the conceptual analysis of queries.

Finding all justifications in Snomed ct

SNOMED CT is a large-scale medical ontology which is developed using a variant of the inexpressive Description Logic EL, a logic-based knowledge representation formalism and a logical underpinning of OWL 2 EL Profile. Description Logic reasoning can be used to compute subsumption relationships between SNOMED CT concepts and to pinpoint the reasons why a certain subsumption holds by finding justifications (sets of axioms responsible for this relationship). This helps the ontology developers to understand such a relationship and to debug it if needed. This article describes an extension of the method of finding one justification to one that finds all justifications for a given subsumption, introduces a SNOMED CT-specific optimization, and presents empirical evaluation results. Our extensive experiments on SNOMED CT show that (i) the proposed modularization-based approach makes it practicable to find all justifications in most cases in SNOMED CT; (ii) the first ten justifications, if any, for a subsumption can be computed in an acceptable runtime, and can be displayed in an incremental manner, i.e., the ontology developers may inspect the first justification while the reasoner continues to find more; and (iii) there is a high degree of commonality among justifications for a subsumption of interest.

OntCheck: An Ontology-Driven Static Correctness Checking Tool for Component-Based Models

Component-based models are widely used for embedded systems. The models consist of components with input and output ports linked to each other. However, mismatched links or assumptions among components may cause many failures, especially for large scale models. Binding semantic knowledge into models can enable domain-specific checking and help expose modeling errors in the early stage. Ontology is known as the formalization of semantic knowledge. In this paper we propose an ontology-driven tool for static correctness checking of domain-specific errors. two kinds of important static checking, semantic type and domain-restrcted rules, are fulfilled in a unified framework. We first propose a formal way to precisely describe the checking requirements by ontology and then separately check them by a lattice-based constraint solver and a description logic reasoner. Compared with other static checking methods, the ontology-based method we proposed is model-externally configurable and thus flexible and adaptable to the changes of requirements. The case study demonstrates the effectiveness of our method.

Datalog-based framework for efficient query answering over fuzzy ontologies

Ontologies are set to play a key role in many domains, especially in the context of the Semantic Web. Ontology languages are based on crisp logic and thus cannot handle imprecision. Fuzzy extensions of ontology languages become unuseful if they cannot be processed by applications. In fact, despite their inference capabilities over complex TBoxes, description logic reasoners have a high ABox reasoning complexity. That is, to retrieve the instances of a given concept, we need to run the reasoning algorithm for each individual in the ABox. In Datalog systems, query answers are computed in one pass: bottom-up or top-down. Motivated by this concern, we propose in this paper a Datalog based framework, named OntoFlex, for efficient query answering over fuzzy ontologies. The framework implements the FSAQL query language over Flora-2 and XSB Datalog systems and proposes a correct mapping of fuzzy RDFS and fuzzy OWL2EL into crisp Datalog programs.

Ontology Knowledge Base Scheme for User Query Semantic Interpretation

The method of recent information retrieval passes into an semantic search to provide more accurate results than keyword-based search. But in common user case, they are still accustomed to using existing keyword-based search. Hence they are hard to create a typed structured query language. In this paper, we propose to ontology knowledge-base scheme for query interpretation of these user. The proposed scheme was designed based on the OWL-DL for description logic reasoning, it can provide a richer representation of the relationship between the object by using SWRL(Semantic Web Rule Language). Finally, we are describe the experimental results of the similarity measurement for verification of a user query semantic interpretation.

Combining CCG Parsing and DL for Chinese Question Answering

This paper presents a novel Web Question Answering(WQA) approach based on the combination of Combinatory Categorial Grammar (CCG), Description Logic (DL) ontology and Web Service (WS), in order to promote semantic-level accuracy through deep text understanding and improve the solutions for non-WH questions. Our framework transforms a question answering problem into a service planning problem for which HTN planner-SHOP2 is used. It addresses transition from natural language to semantic expression and dynamical composition of web services via planning, and execution of the web services planning. We propose to take DL based semantic modeling, i.e., translating lambda-expression encoding of question meaning into DL based semantic representations. The advantage of such approach is a seamless exploitation of existing semantic resource coded as DL ontology, which is widespread in such areas as Semantic Web and conceptual modeling. The experiments are conducted with a repository of complex Chinese questions which involves the satisfaction of some object property restrictions. The experimental results show that producing the semantic representations with the combination of CCG parsing and DL reasoning is an effective approach for question understanding at semantic level, in terms of both understanding accuracy promotion and semantic resource exploitation.

On the definition and design-time analysis of process performance indicators

A key aspect in any process-oriented organisation is the evaluation of process performance for the achievement of its strategic and operational goals. Process Performance Indicators (PPIs) are a key asset to carry out this evaluation, and, therefore, having an appropriate definition of these PPIs is crucial. After a careful review of the literature related and a study of the current picture in different real organisations, we conclude that there not exists any proposal that allows to define PPIs in a way that is unambiguous and highly expressive, understandable by technical and non-technical users and traceable with the Business Process (BP). In addition, like other activities carried out during the BP lifecycle, the management of PPIs is considered time-consuming and error-prone. Therefore, providing an automated support for them is very appealing from a practical point of view.In this paper, we propose the PPINOT metamodel, which allows such an advanced definition of PPIs and is independent of the language used to model the business process. Furthermore, we provide an automatic semantic mapping from the metamodel to Description Logics (DL) that allows the implementation of design-time analysis operations in such a way that DL reasoners’ facilities can be leveraged. These operations provide information that can assist process analysts in the definition and instrumentation of PPIs. Finally, to validate the usefulness of our proposal, we have used the PPINOT metamodel at the core of a software tool called the PPINOT Tool Suite and we have applied it in several real scenarios.

Improving semantic web services discovery using SPARQL-based repository filtering

Semantic Web Services discovery is commonly a heavyweight task, which has scalability issues when the number of services or the ontology complexity increase, because most approaches are based on Description Logic reasoning. As a higher number of services becomes available, there is a need for solutions that improve discovery performance. Our proposal tackles this scalability problem by adding a preprocessing stage based on two SPARQL queries that filter service repositories, discarding service descriptions that do not refer to any functionality or non-functional aspect requested by the user before the actual discovery takes place. This approach fairly reduces the search space for discovery mechanisms, consequently improving the overall performance of this task. Furthermore, this particular solution does not provide yet another discovery mechanism, but it is easily applicable to any of the existing ones, as our prototype evaluation shows. Moreover, proposed queries are automatically generated from service requests, transparently to the user. In order to validate our proposal, this article showcases an application to the OWL-S ontology, in addition to a comprehensive performance analysis that we carried out in order to test and compare the results obtained from proposed filters and current discovery approaches, discussing the benefits of our proposal.