Real-world Ontologies Research Articles

Efficient Extraction of EL-Ontology Deductive Modules

Because widely used real-world ontologies are often complex and large, one important challenge has emerged: designing tools for users to focus on sub-ontologies corresponding to their specific interests. To this end, various modules have been introduced to provide concise ontology views. This work concentrates on extracting deductive modules that preserve logical entailment over a given vocabulary. Existing deductive module proposals are either inefficient from a computing point of view or unsatisfactory from a quality point of view because the modules extracted are not concise enough. For example, minimal modules guarantee the most concise results, but their computation is highly time-consuming, while ⊥⊤∗-modules are easy to compute but usually contain many redundant items. To overcome computation cost and lack of quality, we propose to compute two kinds of deductive modules called pseudo-minimal modules and complete modules for EL-ontology. Our deductive module definitions rely on associating a tree representation with an ontology, and their computation is based on SAT encoding. Our experiments on real-world ontologies show that our pseudo-minimal modules are indeed minimal modules in almost all cases (98.9%), and computing pseudo-minimal modules is more efficient (99.79 times faster on average) than the state-of-the-art method Zoom for computing minimal modules. Also, our complete modules are more compact than ⊥⊤∗-modules, but their computation time remains comparable. Finally, note that our proposal applies to EL-ontologies while Zoom only works for EL-terminologies.

Contextual semantic embeddings for ontology subsumption prediction

Automating ontology construction and curation is an important but challenging task in knowledge engineering and artificial intelligence. Prediction by machine learning techniques such as contextual semantic embedding is a promising direction, but the relevant research is still preliminary especially for expressive ontologies in Web Ontology Language (OWL). In this paper, we present a new subsumption prediction method named BERTSubs for classes of OWL ontology. It exploits the pre-trained language model BERT to compute contextual embeddings of a class, where customized templates are proposed to incorporate the class context (e.g., neighbouring classes) and the logical existential restriction. BERTSubs is able to predict multiple kinds of subsumers including named classes from the same ontology or another ontology, and existential restrictions from the same ontology. Extensive evaluation on five real-world ontologies for three different subsumption tasks has shown the effectiveness of the templates and that BERTSubs can dramatically outperform the baselines that use (literal-aware) knowledge graph embeddings, non-contextual word embeddings and the state-of-the-art OWL ontology embeddings.

A graph-based method for interactive mapping revision in DL-Lite

Discovering the semantic relationships among heterogeneous ontologies has been one of the core research topics in Semantic Web. As ontology matching systems inevitably adopt heuristic strategies, wrong mappings are often contained in final alignments. Most methods for mapping revision depend on dealing with logical incoherence. However, erroneous mappings that do not cause incoherence may be left out. Hence, manual validations with domain expertise are needed. Nevertheless, existing interactive methods for mapping revision still suffer from two limitations. Firstly, revision methods designed to present friendly graphical interfaces and contextual information related to mappings pay little attention to reducing manual decisions, leading to the efficiency problem. Secondly, revision methods focusing on automated reasoning to reduce manual decisions employ models of high complexity, leading to the practicality problem when dealing with alignments across large-scale ontologies. To address these problems, we propose a novel graph-based method for interactive mapping revision, aiming to reduce the manual efforts as much as possible. DL-Lite ontologies and their mappings are encoded into an integrated graph, where the mapping arcs will be judged by the experts. We define the decision space tailored for mapping revision, which can be used to improve the efficiency of manual making decisions. After a manual decision is made in each interaction, the mapping arcs will be automatically updated in the integrated graph. The whole update process modeled in our defined graph-based decision space could be accomplished in polynomial time. We further design an impact function based on the integrated graph and weights of mappings, which can display the most influential mappings to experts. In this way, the number of manual decisions can be reduced further. To cope with the practicality of our method for alignments of large-scale ontologies, we introduce the notion of “reliable” mappings as an attempt to alleviate the burden of experts for making decisions, and propose two soft principles to ensure the reliability of selected mappings. Moreover, we define influence relation and design a corresponding algorithm to enhance the method for detecting incoherence of the integrated graph, which is transformed from the ontologies beyond DL-Lite and their mappings. We implement our method and evaluate its efficiency by 16 alignments generated across real-world ontologies. Experimental results show that our proposed method can improve the efficiency by 19% on average and save more manual decisions than other interactive revision methods in most cases.

One step at a time: An efficient approach to query-based ontology debugging

When ontologies reach a certain size and complexity, faults such as inconsistencies, unsatisfiable classes or wrong entailments are hardly avoidable. Locating the incorrect axioms that cause these faults is a hard and time-consuming task. Addressing this issue, several techniques for a semi-automatic fault localization in ontologies have been proposed and extensively studied. One class of these approaches involve a human expert who provides answers to system-generated queries about the intended (correct) ontology in order to reduce the possible fault locations. To suggest as informative questions as possible, existing methods draw on various algorithmic optimizations as well as heuristics. However, these computations are often based on certain assumptions about the interacting expert.In this work, we demonstrate that these assumptions might not always be adequate and discuss consequences of their violations. In particular, we characterize a range of expert types with different query answering behavior and show that existing approaches are far from achieving optimal efficiency for all of them. In addition, we find that the cost metric adopted by state-of-the-art techniques might not always be realistic and that a change of metric has a decisive impact on the best choice of query answering strategy. As a remedy, we suggest a new – and simpler – type of expert question that leads to a stable fault localization performance for all analyzed expert types and effort metrics, and has numerous further advantages over existing techniques. Moreover, we present an algorithm which computes and optimizes this new query type in worst-case polynomial time and which is fully compatible with existing concepts (e.g., query selection heuristics) and infrastructure (e.g., debugging user interfaces) in the field.Comprehensive experiments on faulty real-world ontologies attest that the new querying method is substantially and statistically significantly superior to existing techniques both in terms of the number of necessary expert interactions and in terms of the query computation time. We find that relying on the new querying method can save an interacting expert more than 80% of their work, and can reduce the expert’s waiting time for the next query by more than three orders of magnitude. Beside these findings, we demonstrate that the efficiency of existing query-based tools can be significantly boosted by suggesting an appropriate query answering strategy to an expert; we also make recommendations in this regard. Further, we suggest optimal configurations of a debugger for situations where the new type of query is used.Remarkably, the proposed approach is not only applicable to ontologies, but to any monotonic knowledge representation language, and can even be adopted to solve general model-based diagnosis problems expressible using Reiter’s theory.

Knowledge graph embeddings for dealing with concept drift in machine learning

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. As data is evolving on a temporal basis, its underlying knowledge is subject to many challenges. Concept drift,11This work is addressing the challenge concept drift in machine learning as opposed to concept drift in the Semantic Web community where “concept” (class) meaning in ontology TBox shifts from versioning, iterations or modifications. Note that changes in ABox alone can also lead to concept drift in learning from ontology streams.. as one of core challenge from the stream learning community, is described as changes of statistical properties of the data over time, causing most of machine learning models to be less accurate as changes over time are in unforeseen ways. This is particularly problematic as the evolution of data could derive to dramatic change in knowledge. We address this problem by studying the semantic representation of data streams in the Semantic Web, i.e., ontology streams. Such streams are ordered sequences of data annotated with ontological vocabulary. In particular we exploit three levels of knowledge encoded in ontology streams to deal with concept drifts: i) existence of novel knowledge gained from stream dynamics, ii) significance of knowledge change and evolution, and iii) (in)consistency of knowledge evolution. Such knowledge is encoded as knowledge graph embeddings through a combination of novel representations: entailment vectors, entailment weights, and a consistency vector. We illustrate our approach on classification tasks of supervised learning. Key contributions of the study include: (i) an effective knowledge graph embedding approach for stream ontologies, and (ii) a generic consistent prediction framework with integrated knowledge graph embeddings for dealing with concept drifts. The experiments have shown that our approach provides accurate predictions towards air quality in Beijing and bus delay in Dublin with real world ontology streams.

Extracting Satisfiability-Preserving Modules From the OWL RL Ontology for Efficient Reasoning

Reasoning on large ontologies has been identified as an important challenge. Concept satisfiability is one of the core reasoning tasks in ontology engineering. To improve the efficiency of concept satisfiability checking for OWL RL ontologies, we propose an approach for extracting modules from the OWL RL ontology. Our approach is based on the idea that for a given concept being checked, not all of the ontology elements are related to the checking process. By transforming the OWL RL axioms into first-order constraints and analyzing the dependencies among the constraints, we give a strategy for deleting the irrelevant elements; thus, a simplified ontology that preserves the original checking result can be obtained. The experimental results obtained for a set of real-world ontologies show that our method can effectively reduce the reasoning time, and the efficiency is increased by 1.15 times to 9.08 times.

GeoLink Data Set: A Complex Alignment Benchmark from Real-world Ontology

Ontology alignment has been studied for over a decade, and over that time many alignment systems and methods have been developed by researchers in order to find simple 1-to-1 equivalence matches between two ontologies. However, very few alignment systems focus on finding complex correspondences. One reason for this limitation may be that there are no widely accepted alignment benchmarks that contain such complex relationships. In this paper, we propose a real-world data set from the GeoLink project as a potential complex ontology alignment benchmark. The data set consists of two ontologies, the GeoLink Base Ontology (GBO) and the GeoLink Modular Ontology (GMO), as well as a manually created reference alignment that was developed in consultation with domain experts from different institutions. The alignment includes 1:1, 1:n, and m:n equivalence and subsumption correspondences, and is available in both Expressive and Declarative Ontology Alignment Language (EDOAL) and rule syntax. The benchmark has been expanded from its original version to contain real-world instance data from seven geoscience data providers that has been published according to both ontologies. This allows it to be used by extensional alignment systems or those that require training data. This benchmark has been incorporated into the Ontology Alignment Evaluation Initiative (OAEI) complex track to help researchers test their automated alignment systems and algorithms. This paper also analyzes the challenges inherent in effectively generating, detecting, and evaluating complex ontology alignments and provides a road map for future work on this topic.

Restricted Chase Termination for Existential Rules: A Hierarchical Approach and Experimentation

AbstractThe chase procedure for existential rules is an indispensable tool for several database applications, where its termination guarantees the decidability of these tasks. Most previous studies have focused on the skolem chase variant and its termination analysis. It is known that the restricted chase variant is a more powerful tool in termination analysis provided a database is given. But all-instance termination presents a challenge since the critical database and similar techniques do not work. In this paper, we develop a novel technique to characterize the activeness of all possible cycles of a certain length for the restricted chase, which leads to the formulation of a framework of parameterized classes of the finite restricted chase, called $k$-$\mathsf{safe}(\Phi)$ rule sets. This approach applies to any class of finite skolem chase identified with a condition of acyclicity. More generally, we show that the approach can be applied to the hierarchy of bounded rule sets previously only defined for the skolem chase. Experiments on a collection of ontologies from the web show the applicability of the proposed methods on real-world ontologies.

Towards evaluating complex ontology alignments

Abstract The development of semi-automated and automated ontology alignment techniques is an important part of realizing the potential of the Semantic Web. Until very recently, most existing work in this area was focused on finding simple (1:1) equivalence correspondences between two ontologies. However, many real-world ontology pairs involve correspondences that contain multiple entities from each ontology. These ‘complex’ alignments pose a challenge for existing evaluation approaches, which hinders the development of new systems capable of finding such correspondences. This position paper surveys and analyzes the requirements for effective evaluation of complex ontology alignments and assesses the degree to which these requirements are met by existing approaches. It also provides a roadmap for future work on this topic taking into consideration emerging community initiatives and major challenges that need to be addressed.

Model migration and rough edges: British actuaries and the ontologies of modelling.

The existing literature on modelling provides two main ways of viewing model migration: a modular view, which seeks to decompose models in their constitutive elements, and thus provides a view on what it is that migrates; and a practice-based view, which focuses on modelling as an activity, and understands a model as intricately entangled with its context of use. This article brings together these two sensitivities by focusing on ontologies of modelling. The paper presents a case study of the appropriation of modern finance theory’s ‘no-arbitrage’ models by British actuaries – a process that gradually unfolded at around the turn of the century and led to significant friction within the UK’s insurance industry. We can distinguish two main modelling ontologies: a ‘risk-neutral ontology’, which underpins no-arbitrage models and holds that the value of financial instruments is determined by ‘arbitrage’; and, a ‘real-world ontology’, which assumes that the economic world consists of real probabilities that may be approximated through a combination of archival-statistical methods and expert judgment. The appropriation of the risk-neutral modelling ontology was made possible by the declining legitimacy of actuarial expertise as ‘financial stewards’ of life insurance companies. The risk-neutral modelling ontology provided an ‘objective’ alternative to the traditional actuarial models, which explicitly required actuaries to make ‘prudent’ judgments. Despite the fact that the no-arbitrage modelling was considered an ‘objective’ affair, the valuation models that insurers use today are strongly shaped by political compromises, a result of the ‘rough edges’ of models.

Exploring duality on ontology debugging

Ontology debugging is an important and intractable reasoning task. Minimal unsatisfiability-preserving subset (MUPS) which is a minimal subset of an ontology to debug an unsatisfiable concept, is an important concept in ontology debugging. Hitting set relation between conflict set and diagnosis is essential for computing all conflict sets. And there has been many successful explorations about hitting set duality between conflict set and diagnosis in many fields. So in this paper, we will explore the duality between MUPS and minimal correctness-preserving subset (MCPS) which denotes the minimal diagnosis of a concept to debug unsatisfiable concepts on ontology debugging domain. Then several methods for computing all MUPSes will be devised based on the duality between MUPS and MCPS, meanwhile parallel strategies are also applied to newly proposed methods. And we show, by an empirical evaluation, that performances of different methods on real world ontologies from different domains. And it is proved that it is meaningful to explore duality on ontology debugging, as it really boosts the efficiency when applied to complex ontologies compared to the previous methods.

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.

ABox Abduction via Forgetting in ALC

Abductive reasoning generates explanatory hypotheses for new observations using prior knowledge. This paper investigates the use of forgetting, also known as uniform interpolation, to perform ABox abduction in description logic (ALC) ontologies. Non-abducibles are specified by a forgetting signature which can contain concept, but not role, symbols. The resulting hypotheses are semantically minimal and consist of a disjunction of ABox axioms. These disjuncts are each independent explanations, and are not redundant with respect to the background ontology or the other disjuncts, representing a form of hypothesis space. The observations and hypotheses handled by the method can contain both atomic or complex ALC concepts, excluding role assertions, and are not restricted to Horn clauses. Two approaches to redundancy elimination are explored in practice: full and approximate. Using a prototype implementation, experiments were performed over a corpus of real world ontologies to investigate the practicality of both approaches across several settings.

Ontology knowledge mining for ontology conceptual enrichment

ABSTRACTActually, to accomplish knowledge sharing, specific parts derived from existing ontological resources are employed. Therefore, several researchers have been interested in merging these knowledge-bases by enriching target ontology with novel knowledge coming from source ones, they use either statistical models or expert’s intervention to provide the relevance and placement of new concepts. Nevertheless, real world ontologies are large size, thus, the enrichment/merging process turns to be time consuming and hard to handle. To cope with these limitations, we propose an ontology knowledge mining based approach for ontology conceptual enrichment. First we reorganize both ontological structures by defining hierarchies of reduced conceptual clusters grouping similar concepts of targeted thematic. Then, we proceed to align both hierarchical structures to detect similar clusters. Finally, we proceed to enrich the source hierarchy with different clusters of the target structure. The results of tests performed with our method on real domain ontologies show their effectiveness.

EAPB: entropy-aware path-based metric for ontology quality

BackgroundEntropy has become increasingly popular in computer science and information theory because it can be used to measure the predictability and redundancy of knowledge bases, especially ontologies. However, current entropy applications that evaluate ontologies consider only single-point connectivity rather than path connectivity, and they assign equal weights to each entity and path.ResultsWe propose an Entropy-Aware Path-Based (EAPB) metric for ontology quality by considering the path information between different vertices and textual information included in the path to calculate the connectivity path of the whole network and dynamic weights between different nodes. The information obtained from structure-based embedding and text-based embedding is multiplied by the connectivity matrix of the entropy computation. EAPB is analytically evaluated against the state-of-the-art criteria. We have performed empirical analysis on real-world medical ontologies and a synthetic ontology based on the following three aspects: ontology statistical information (data quantity), entropy evaluation (data quality), and a case study (ontology structure and text visualization). These aspects mutually demonstrate the reliability of the proposed metric. The experimental results show that the proposed EAPB can effectively evaluate ontologies, especially those in the medical informatics field.ConclusionsWe leverage path information and textual information to enrich the network representational learning and aid in entropy computation. The analytics and assessments of semantic web can benefit from the structure information but also the text information. We believe that EAPB is helpful for managing ontology development and evaluation projects. Our results are reproducible and we will release the source code and ontology of this work after publication. (Source code and ontology: https://github.com/AnonymousResearcher1/ontologyEvaluate).

A parallel computing architecture for high-performance OWL reasoning

Abstract The Web Ontology Language (OWL) is a widely used knowledge representation language for describing knowledge in application domains by using classes, properties, and individuals. Ontology classification is an important and widely used service that computes a taxonomy of all classes occurring in an ontology. It can require significant amounts of runtime, but most OWL reasoners do not support any kind of parallel processing. We present a novel thread-level parallel architecture for ontology classification, which is ideally suited for shared-memory SMP servers, but does not rely on locking techniques and thus avoids possible race conditions. We evaluated our prototype implementation with a set of real-world ontologies. Our experiments demonstrate a very good scalability resulting in a speedup that is linear to the number of available cores.

A Framework and Positive Results for IAR-answering

Inconsistency-tolerant semantics, like the IAR semantics, have been proposed as means to compute meaningful query answers over inconsistent Description Logic (DL) ontologies. So far query answering under the IAR semantics (IAR-answering) is known to be tractable only for arguably weak DLs like DL-Lite and the quite restricted EL⊥nr fragment of EL⊥. Towards providing a systematic study of IAR-answering, in the current paper we first present a general framework/algorithm for IAR-answering which applies to arbitrary DLs but need not terminate. Nevertheless, this framework allows us to develop a sufficient condition for tractability of IAR-answering and hence of termination of our algorithm. We then show that this condition is always satisfied by the arguably expressive DL DL-Litebool, providing the first positive result for IAR-answering over a non-Horn-DL. In addition, recent results show that this condition usually holds for real-world ontologies and techniques and algorithms for checking it in practice have also been studied recently; thus, overall our results are highly relevant in practice. Finally, we have provided a prototype implementation and a preliminary evaluation obtaining encouraging results.

Rewriting Minimizations for Efficient Query Answering over Ontologies

Computing a (Union of Conjunctive Queries — UCQ) rewriting ℛ for an input query and ontology and evaluating it over the given dataset is a prominent approach to query answering over ontologies. However, ℛ can be large and complex in structure hence additional techniques, like query subsumption and data constraints, need to be employed in order to minimize ℛ and lead to an efficient evaluation. Although sound in theory, how to efficiently and effectively implement many of these techniques in practice could be challenging. For example, many systems do not implement query subsumption. In the current paper we present several practical techniques for UCQ rewriting minimization. First, we present an optimized algorithm for eliminating redundant (w.r.t. subsumption) queries as well as a novel framework for rewriting minimization using data constraints. Second, we show how these techniques can also be used to speed up the computation of ℛ in first place. Third, we integrated all our techniques in our query rewriting system IQAROS and conducted an extensive experimental evaluation using many artificial as well as challenging real-world ontologies obtaining encouraging results as, in the vast majority of cases, our system is more efficient compared to the two most popular state-of-the-art systems.

GEL: A Platform-Independent Reasoner for Parallel Classification with OWL EL Ontologies Using Graph Representation

In the community of Semantic Web, the state-of-the-art reasoners are all implemented on specific platforms. As the Web Ontology Language (OWL) has been widely used in different real-world applications, it is required that the task of reasoning can be flexibly adapted to different platforms. In this paper, we take the first effort to give a platformindependent approach for parallel classification of the description logic OWL EL, which is a tractable fragment of OWL 2. Classification, which is the task of computing a subsumption hierarchy between concepts, plays an important role in many applications of OWL EL. The current classification methods rely on the specific parallel computation platforms and can hardly achieve a tradeoff between efficiency and scalability. To develop a platform-independent approach for performing classification in OWL EL, we first give a novel and well defined graph formalism GEL for representing EL ontologies and present a group of sound and complete rules for ontology classification on a GEL graph. Based on this formalism, we design a parallel classification algorithm, which can be implemented on different parallel computation platforms, and we also show the correctness of the algorithm. We implement a system, which can easily switch between multi-core and a distributed cluster. Finally, we conduct experiments on several real-world OWL EL ontologies. The experimental results show that our system outperforms two state-of-the-art EL reasoning systems, and has a linear scalability on the extensions of GO ontologies.

Modeling ontology evolution via Pi-Calculus

Extending and updating real world ontologies is unavoidable challenging with our ever-evolving understanding of the world and the evolution of the world by itself. In the current researches, changes are usually modeled as passive instant results of operations such as to add a child class or to delete a property, executed by the administrators. But this view neglects the real world facts that (1) an ontology evolves continuously over time, not just hopping instantly between static versions; (2) a change is a coherent procedure although it may be separated from the administrative point of view into different phases, such as to request, detect, represent, evaluate, implement and propagate the change. This paper provides an inside perspective of the ontology itself to model its evolution. The ontology entities are regarded as autonomous agents with find-grained state specifications. The operational semantics of the changes are formalized as series of information exchange actions of the agents. Pi-Calculus serves to describe the operational semantics in our modeling. Firstly, an ontology is encoded into a hyper graph. Then the nodes and edges in the hyper graph are formalized as Pi-Calculus processes. The replication operator is used to add new entities to the ontology and the communication rule works for the resolution of the information exchanges. Thus, a change is modeled as a coherent procedure as clarified in its operational semantics. A case study shows the feasibility of our method on the Mobility Workbench (MWB). We believe that the operational semantics in our scenario disclose the autonomous evolving nature of ontology evolution.