Description Logic Reasoning Research Articles

Using Ontologies for the Formalization and Recognition of Criticality for Automated Driving

Knowledge representation and reasoning has a long history of examining how knowledge can be formalized, interpreted, and semantically analyzed by machines. In the area of automated vehicles, recent advances suggest the ability to formalize and leverage relevant knowledge as a key enabler in handling the inherently open and complex context of the traffic world. This paper demonstrates ontologies to be a powerful tool for a) modeling and formalization of and b) reasoning about factors associated with criticality in the environment of automated vehicles. For this, we leverage the well-known 6-Layer Model to create a formal representation of the environmental context. Within this representation, an ontology models domain knowledge as logical axioms, enabling deduction on the presence of critical factors within traffic scenarios. For executing automated analyses, a joint description logic and rule reasoner is used in combination with an a-priori predicate augmentation. We elaborate on the modular approach, present a publicly available implementation, and exemplarily evaluate the method by means of a large-scale drone data set of urban traffic scenarios.

Optimizing a tableau reasoner and its implementation in Prolog

One of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism.The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILLP and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavors. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners.In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations.

Efficient TBox Reasoning with Value Restrictions using the wer Reasoner

AbstractThe inexpressive Description Logic (DL) ${\cal F}{{\cal L}_0}$ , which has conjunction and value restriction as its only concept constructors, had fallen into disrepute when it turned out that reasoning in ${\cal F}{{\cal L}_0}$ w.r.t. general TBoxes is ExpTime-complete, that is, as hard as in the considerably more expressive logic ${\cal A}{\cal L}{\cal C}$ . In this paper, we rehabilitate ${\cal F}{{\cal L}_0}$ by presenting a dedicated subsumption algorithm for ${\cal F}{{\cal L}_0}$ , which is much simpler than the tableau-based algorithms employed by highly optimized DL reasoners. Our experiments show that the performance of our novel algorithm, as prototypically implemented in our ${\cal F}{{\cal L}_0}$ wer reasoner, compares very well with that of the highly optimized reasoners. ${\cal F}{{\cal L}_0}$ wer can also deal with ontologies written in the extension ${\cal F}{{\cal L}_ \bot }$ of ${\cal F}{{\cal L}_0}$ with the top and the bottom concept by employing a polynomial-time reduction, shown in this paper, which eliminates top and bottom. We also investigate the complexity of reasoning in DLs related to the Horn-fragments of ${\cal F}{{\cal L}_0}$ and ${\cal F}{{\cal L}_ \bot }$ .

Diagnosing Changes in An Ontology Stream: A DL Reasoning Approach

Recently, ontology stream reasoning has been introduced as a multidisciplinary approach, merging synergies from Artificial Intelligence, Database and World-Wide-Web to reason on semantics-augmented data streams, thus a way to answering questions on real time events. However existing approaches do not consider stream change diagnosis i.e., identification of the nature and cause of changes, where explaining the logical connection of knowledge and inferring insight on time changing events are the main challenges. We exploit the Description Logics (DL)-based semantics of streams to tackle these challenges. Based on an analysis of stream behavior through change and inconsistency over DL axioms, we tackled change diagnosis by determining and constructing a comprehensive view on potential causes of inconsistencies. We report a large-scale evaluation of our approach in the context of live stream data from Dublin City Council.

Enhancing Probabilistic Model Checking with Ontologies

Probabilistic model checking (PMC) is a well-established method for the quantitative analysis of state based operational models such as Markov decision processes. Description logics (DLs) provide a well-suited formalism to describe and reason about knowledge and are used as basis for the web ontology language (OWL). We investigate how such knowledge described by DLs can be integrated into the PMC process, introducing ontology-mediated PMC. Specifically, we propose ontologized programs as a formalism that links ontologies to behaviors specified by probabilistic guarded commands, the de-facto standard input formalism for PMC tools such as Prism. Through DL reasoning, inconsistent states in the modeled system can be detected. We present three ways to resolve these inconsistencies, leading to different Markov decision process semantics. We analyze the computational complexity of checking whether an ontologized program is consistent under these semantics. Further, we present and implement a technique for the quantitative analysis of ontologized programs relying on standard DL reasoning and PMC tools. This way, we enable the application of PMC techniques to analyze knowledge-intensive systems.We evaluate our approach and implementation on amulti-server systemcase study,where different DL ontologies are used to provide specifications of different server platforms and situations the system is executed in.

A Breakdown of System of Systems Needs Using Architecture Frameworks, Ontologies and Description Logic Reasoning

Aerospace systems are connected with the operational environment and other systems in general. The focus in aerospace product development is consequently shifting from a singular system perspective to a System-of-Systems (SoS) perspective. This increasing complexity gives rise to new levels of uncertainty that must be understood and managed to produce aerospace solutions for an ever-changing future. This paper presents an approach to using architecture frameworks, and ontologies with description logic reasoning capabilities, to break down SoS needs into required capabilities and functions. The intention of this approach is to provide a consistent way of obtaining the functions to be realized in order to meet the overarching capabilities and needs of an SoS. The breakdown with an architecture framework results in an initial design space representation of functions to be performed. The captured knowledge is then represented in an ontology with description logic reasoning capabilities, which provides a more flexible way to expand and process the initial design space representation obtained from the architecture framework. The proposed approach is ultimately tested in a search and rescue case study, partly based on the operations of the Swedish Maritime Administration. The results show that it is possible to break down SoS needs in a consistent way and that ontology with description logic reasoning can be used to process the captured knowledge to both expand and reduce an available design space representation.

Ontology-based Knowledge System and Team Verification Tool for Competitive Pokemon

Competitive Pokemon is a domain with rich semantics and complex relationships between its elements. Current research in the domain has focused on developing AI agents to select moves within a match, ignoring the problem of team building. We propose a team verification tool based on ontologies to model the complexities of the domain. A user can input their team into the tool, and the tool uses a description logic reasoner to classify Pokemon into their appropriate roles. The tool exports a visual representation of the team and a score evaluating its competitive viability. The classifications made by the TeamVerify tool have 87.7% precision and 86.0% recall in a multiclass, multilabel domain. We expect such a tool to reduce the learning curve for novice players who have not yet built intuitions on proper team structure.

Minimalistic fuzzy ontology reasoning: An application to Building Information Modeling

This paper presents a minimalistic reasoning algorithm to solve imprecise instance retrieval in fuzzy ontologies with application to querying Building Information Models (BIMs)—a knowledge representation formalism used in the construction industry. Our proposal is based on a novel lossless reduction of fuzzy to crisp reasoning tasks, which can be processed by any Description Logics reasoner. We implemented the minimalistic reasoning algorithm and performed an empirical evaluation of its performance in several tasks: interoperation with classical reasoners (Hermit and TrOWL), initialization time (comparing TrOWL and a SPARQL engine), and use of different data structures (hash tables, databases, and programming interfaces). We show that our software can efficiently solve very expressive queries not available nowadays in regular or semantic BIMs tools.

Optimizing a Tableau Reasoner and Its Implementation in Prolog

One of the foremost reasoning services for knowledge bases is finding all the justifications for a query. This is useful for debugging purpose and for coping with uncertainty. Among Description Logics (DLs) reasoners, the tableau algorithm is one of the most used. However, in order to collect the justifications, the reasoners must manage the non-determinism of the tableau method. For these reasons, a Prolog implementation can facilitate the management of such non-determinism. The TRILL framework contains three probabilistic reasoners written in Prolog: TRILL, TRILL and TORNADO. Since they are all part of the same framework, the choice about which to use can be done easily via the framework settings. Each one of them uses different approaches for probabilistic inference and handles different DLs flavours. Our previous work showed that they can sometimes achieve better results than state-of-the-art (non-)probabilistic reasoners. In this paper we present two optimizations that improve the performances of the TRILL reasoners. The first one consists into identifying the fragment of the KB that allows to perform inference without losing the completeness. The second one modifies which tableau rule to apply and their order of application, in order to reduce the number of operations. Experimental results show the effectiveness of the introduced optimizations.

Principles of KLM-style Defeasible Description Logics

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC .

A Lightweight Defeasible Description Logic in Depth

In this thesis we study KLM-style rational reasoning in defeasible Description Logics. We illustrate that many recent approaches to derive consequences under Rational Closure (and its stronger variants, lexicographic and relevant closure) suffer the fatal drawback of neglecting defeasible information in quantified concepts. We propose novel model-theoretic semantics that are able to derive the missing entailments in two differently strong flavours. Our solution introduces a preference relation to distinguish sets of models in terms of their typicality (amount of defeasible information derivable for quantified concepts). The semantics defined through the most typical (most preferred) sets of models are proven superior to previous approaches in that their entailments properly extend previously derivable consequences, in particular, allowing to derive defeasible consequences for quantified concepts. The dissertation concludes with an algorithmic characterisation of this uniform maximisation of typicality, which accompanies our investigation of the computational complexity for deriving consequences under these new semantics.

A semantic assessment framework for e-learning systems

Semantic web technologies are applied in a wide spectrum of applications including search engines, web services discovery and composition, semantic tags and electronic assessment of e-learning systems. Recently, the field of electronic assessment has grabbed the attention of many researchers. Electronic assessment is a real challenge taking to consideration the diversity of question types. E-assessment of answers to open questions has created new challenges regarding extracting students' answers using natural language processing techniques and infer new knowledge using description logic reasoning. To be able to automate the process of assessing the students' answers, they have to be annotated with semantics. This paper presents a thorough survey of significant research efforts that adopt semantic annotation to enhance the process of e-assessment. Moreover, in this paper, we propose a semantic-based framework that automates the process of electronically assessing the answers of questions on e-learning systems based on their semantic descriptions.

A semantic assessment framework for e-learning systems

Semantic web technologies are applied in a wide spectrum of applications including search engines, web services discovery and composition, semantic tags and electronic assessment of e-learning systems. Recently, the field of electronic assessment has grabbed the attention of many researchers. Electronic assessment is a real challenge taking to consideration the diversity of question types. E-assessment of answers to open questions has created new challenges regarding extracting students' answers using natural language processing techniques and infer new knowledge using description logic reasoning. To be able to automate the process of assessing the students' answers, they have to be annotated with semantics. This paper presents a thorough survey of significant research efforts that adopt semantic annotation to enhance the process of e-assessment. Moreover, in this paper, we propose a semantic-based framework that automates the process of electronically assessing the answers of questions on e-learning systems based on their semantic descriptions.

From Horn-SRIQ to Datalog: A Data-Independent Transformation That Preserves Assertion Entailment

Ontology-based access to large data-sets has recently gained a lot of attention. To access data efficiently, one approach is to rewrite the ontology into Datalog, and then use powerful Datalog engines to compute implicit entailments. Existing rewriting techniques support Description Logics (DLs) from ELH to Horn-SHIQ. We go one step further and present one such data-independent rewriting technique for Horn-SRIQ⊓, the extension of Horn-SHIQ that supports role chain axioms, an expressive feature prominently used in many real-world ontologies. We evaluated our rewriting technique on a large known corpus of ontologies. Our experiments show that the resulting rewritings are of moderate size, and that our approach is more efficient than state-of-the-art DL reasoners when reasoning with data-intensive ontologies.

Efficient Concept Induction for Description Logics

Concept Induction refers to the problem of creating complex Description Logic class descriptions (i.e., TBox axioms) from instance examples (i.e., ABox data). In this paper we look particularly at the case where both a set of positive and a set of negative instances are given, and complex class expressions are sought under which the positive but not the negative examples fall. Concept induction has found applications in ontology engineering, but existing algorithms have fundamental performance issues in some scenarios, mainly because a high number of invokations of an external Description Logic reasoner is usually required. In this paper we present a new algorithm for this problem which drastically reduces the number of reasoner invokations needed. While this comes at the expense of a more limited traversal of the search space, we show that our approach improves execution times by up to several orders of magnitude, while output correctness, measured in the amount of correct coverage of the input instances, remains reasonably high in many cases. Our approach thus should provide a strong alternative to existing systems, in particular in settings where other systems are prohibitively slow.

A Semantic Platform for Surveillance of Adverse Childhood Experiences

ObjectiveWe introduce the Semantic Platform for Adverse Childhood Experiences (ACEs)Surveillance (SPACES). It facilitates the access to the relevant integrated information, enables discovering the causality pathways and assists researchers, clinicians, public health practitioners, social workers, and health organization in studying the ACEs, identifying the trends, as well as planning and implementing preventive and therapeutic strategies.IntroductionAdverse Childhood Experiences (ACEs) have been linked to a variety of detrimental health and social outcomes. In the last 20 years, the association between ACEs with several adult health risk behaviors, conditions, and diseases including suicides, and substance abuse [1], mental health disturbances and impaired memory [2], nervous, endocrine and immune systems impairments [3], and criminal activities [4] have been studied. One of the challenges in studying and timely diagnosis of ACEs is that the links between specific childhood experiences and their health outcomes are not totally clear. Similarly, an integrated dataset builtfrom multiple sources is often required for effective ACEs surveillance. The SPACES project aims at providing a semantic infrastructure to facilitate data sharing and integration and answer causal queries [5] to improve ACEs surveillance.MethodsWe create the ACEs ontology to facilitate the integration of data coming from various distributed sources (e.g. ontologies, databases, surveys, interviews, and literature) and maintain interoperability between the data sources. We re-used some of the existing bio-ontologies in the domain, although they captured the domain’s knowledge in different levels of granularity, e.g., Homeless is defined in SNOMED CT and Medical Dictionary for Regulatory Activities (MedDRA) with different hierarchies, or some related concepts may be disconnected, e.g., SNOMED CT defines Abuse but not Verbal Abuse while MedDRA defines Verbal abuse but not as an Abuse. In order to improve reasoning with the aggregated data, we perform two different kinds of inference. Semantic inference uses the ACEs ontology to creates new conclusions by connecting similar concepts. On the other hand, statistical inference is used to formulate rules that link co-occurring concepts. These two kinds of inference, statistical and semantic, work in tandem and the conclusions from one method can then be used as the basis for the other kind of inference.ResultsThe ACEs Ontology is a unique resource for capturing knowledge in the domain of AdverseChildhood Experiences. The ontology will be openly and freely available via the relevant online community’s portals (e.g. The NCBO BioPortal). The logical validation of the ontology is performed using description logic reasoners. A set of use-case scenarios are designed to demonstrate the feasibility and usability of the ontology for data integration and intelligent query answering.ConclusionsIn this paper, we present the SPACE project that aims to develop a platform to improve adverse childhood experiences surveillance. The tool uses semantic and statistical methods to improve data access, integration, and reasoning.

Ontology-Oriented Software Effort Estimation System for E-commerce Applications Based on Extreme Programming and Scrum Methodologies

Abstract Presently, software industry is severely suffering from inaccurate effort estimation and inadequate unstructured or semi-structured project history management. In fact, both are difficult to accomplish and hence badly impact the software projects. We proposed improvements in the effort estimation and the project history management of e-commerce projects focusing on Extreme Programing (XP) and Scrum methodologies using ontology models in our software effort estimation system. Proposed system infers suitable estimate in the form of time, resources and lessons learnt as per the project leader’s requirements by using description logic and HermiT reasoner. To validate our approach, we have performed a case study comprising 20 Business-to-Consumer (B2C) web projects and performed comparative analysis on the collected efforts in both XP and Scrum contexts by applying (Mean Magnitude of Relative Error) MMRE and PRED(25) prediction accuracy measures. Likewise, software functional size of understudy e-commerce projects was measured using COSMIC functional size measurement methodology. Regression analysis of relations among actual COSMIC function points, estimated effort, and actual effort spent for the projects show better significance-F and R2 values for our approach. The comparative results show that overall proposed approach provides accurate estimates and significantly improves over planning poker and delphi methods by 10% and 30%, respectively.

Probabilistic DL Reasoning with Pinpointing Formulas: A Prolog-based Approach

AbstractWhen modeling real-world domains, we have to deal with information that is incomplete or that comes from sources with different trust levels. This motivates the need for managing uncertainty in the Semantic Web. To this purpose, we introduced a probabilistic semantics, named DISPONTE, in order to combine description logics (DLs) with probability theory. The probability of a query can be then computed from the set of its explanations by building a Binary Decision Diagram (BDD). The set of explanations can be found using thetableau algorithm, which has to handle non-determinism. Prolog, with its efficient handling of non-determinism, is suitable for implementing the tableau algorithm. TRILL and TRILLPare systems offering a Prolog implementation of the tableau algorithm. TRILLPbuilds apinpointing formulathat compactly represents the set of explanations and can be directly translated into a BDD. Both reasoners were shown to outperform state-of-the-art DL reasoners. In this paper, we present an improvement of TRILLP, named TORNADO, in which the BDD is directly built during the construction of the tableau, further speeding up the overall inference process. An experimental comparison shows the effectiveness of TORNADO. All systems can be tried online in the TRILL on SWISH web application athttp://trill.ml.unife.it/.

Consequence-Based Reasoning for Description Logics with Disjunctions and Number Restrictions


 
 
 Classification of description logic (DL) ontologies is a key computational problem in modern data management applications, so considerable effort has been devoted to the development and optimisation of practical reasoning calculi. Consequence-based calculi combine ideas from hypertableau and resolution in a way that has proved very effective in practice. However, existing consequence-based calculi can handle either Horn DLs (which do not support disjunction) or DLs without number restrictions. In this paper, we overcome this important limitation and present the first consequence-based calculus for deciding concept subsumption in the DL ALCHIQ+. Our calculus runs in exponential time assuming unary coding of numbers, and on ELH ontologies it runs in polynomial time. The extension to disjunctions and number restrictions is technically involved: we capture the relevant consequences using first-order clauses, and our inference rules adapt paramodulation techniques from first-order theorem proving. By using a well-known preprocessing step, the calculus can also decide concept subsumptions in SRIQ---a rich DL that covers all features of OWL 2 DL apart from nominals and datatypes. We have implemented our calculus in a new reasoner called Sequoia. We present the architecture of our reasoner and discuss several novel and important implementation techniques such as clause indexing and redundancy elimination. Finally, we present the results of an extensive performance evaluation, which revealed Sequoia to be competitive with existing reasoners. Thus, the calculus and the techniques we present in this paper provide an important addition to the repertoire of practical implementation techniques for description logic reasoning.
 
 

A Note on a Description Logic of Concept and Role Typicality for Defeasible Reasoning Over Ontologies

In this work, we propose a meaningful extension of description logics for non-monotonic reasoning. We introduce $$\mathcal {ALCH}^{\bullet }$$ , a logic allowing for the representation of and reasoning about both typical class-membership and typical instances of a relation. We propose a preferential semantics for $$\mathcal {ALCH}^{\bullet }$$ in terms of partially-ordered DL interpretations which intuitively captures the notions of typicality we are interested in. We define a tableau-based algorithm for checking $$\mathcal {ALCH}^{\bullet }$$ knowledge-base consistency that always terminates and we show that it is sound and complete w.r.t. our preferential semantics. The general framework we here propose can serve as the foundation for further exploration of non-monotonic reasoning in description logics and similarly structured logics.