Ontological Knowledge Research Articles

Decolonization of Indigenous Knowledge Systems in South Africa

This article analyses the protection of indigenous knowledge in South Africa, exploring if and how indigenous knowledge is aligned with existing policy and protocol frameworks as enacted by the government. Indigenous knowledge is mainly preserved in the memories of elders and shared through oral communication and traditional practices. The question arises: How can knowledge generated in indigenous knowledge systems research be recovered and protected to benefit indigenous knowledge owners and accessible for future generations? The study utilised literature review to critically analyse the policy, protocols, and strategies relating to the protection and preservation of indigenous knowledge systems. Decolonial theory and knowledge ontology and modelling framework were also used as underpinning theories to guide the study. Recommendations suggest the need for decolonizing indigenous knowledge systems through collaborative approach with indigenous knowledge holders and their communities.

Reviewing the «book» term explication in the context of documentology

The purpose of the article is to introduce new approach to explicate the term of «book». It implicates inter- and transdisciplinary study of accumulated knowledge on the book theory, combined basic schemes and terminological systems in the book theory, documentology and related areas, identification of conflicts and their elimination. The author suggests to use widely the documentoriented terms introduced by Yury N. Stolyarov and Galina N. Shvetsova-Vodka; differentiated modelling and typological classification of documents and other objects to deliver social information to the society; and convergence of their typological and ontological features. These would provide for the unity of theoretical and ontological knowledge but also of taxon and metrological and structural aspects of their theorization. The result of the study is that the «book» term is defined as an astadial unit of activities to provide social information to the society. The book is an information product, significant in its semiotic value and embodied in the form of scrolls, blocks of bound pages, laser, optical discs, records, digital devices for reading, etc.: intended for reading (listening or tactile sensing) and fulfills the function of transmitting knowledge and other semantic content to addressees.

An argumentative approach for handling inconsistency in prioritized Datalog ± ontologies

Prioritized Datalog ± is a well-studied formalism for modelling ontological knowledge and data, and has a success story in many applications in the (Semantic) Web and in other domains. Since the information content on the Web is both inherently context-dependent and frequently updated, the occurrence of a logical inconsistency is often inevitable. This phenomenon has led the research community to develop various types of inconsistency-tolerant semantics over the last few decades. Although the study of query answering under inconsistency-tolerant semantics is well-understood, the problem of explaining query answering under such semantics took considerably less attention, especially in the scenario where the facts are prioritized. In this paper, we aim to fill this gap. More specifically, we use Dung’s abstract argumentation framework to address the problem of explaining inconsistency-tolerant query answering in Datalog ± KB where facts are prioritized, or preordered. We clarify the relationship between preferred repair semantics and various notions of extensions for argumentation frameworks. The strength of such argumentation-based approach is the explainability; users can more easily understand why different points of views are conflicting and why the query answer is entailed (or not) under different semantics. To this end we introduce the formal notion of a dialogical explanation, and show how it can be used to both explain showing why query results hold and not hold according to the known semantics in inconsistent Datalog ± knowledge bases.

Gene Expression Landscape of Chronic Myeloid Leukemia K562 Cells Overexpressing the Tumor Suppressor Gene PTPRG.

This study concerns the analysis of the modulation of Chronic Myeloid Leukemia (CML) cell model K562 transcriptome following transfection with the tumor suppressor gene encoding for Protein Tyrosine Phosphatase Receptor Type G (PTPRG) and treatment with the tyrosine kinase inhibitor (TKI) Imatinib. Specifically, we aimed at identifying genes whose level of expression is altered by PTPRG modulation and Imatinib concentration. Statistical tests as differential expression analysis (DEA) supported by gene set enrichment analysis (GSEA) and modern methods of ontological term analysis are presented along with some results of current interest for forthcoming experimental research in the field of the transcriptomic landscape of CML. In particular, we present two methods that differ in the order of the analysis steps. After a gene selection based on fold-change value thresholding, we applied statistical tests to select differentially expressed genes. Therefore, we applied two different methods on the set of differentially expressed genes. With the first method (Method 1), we implemented GSEA, followed by the identification of transcription factors. With the second method (Method 2), we first selected the transcription factors from the set of differentially expressed genes and implemented GSEA on this set. Method 1 is a standard method commonly used in this type of analysis, while Method 2 is unconventional and is motivated by the intention to identify transcription factors more specifically involved in biological processes relevant to the CML condition. Both methods have been equipped in ontological knowledge mining and word cloud analysis, as elements of novelty in our analytical procedure. Data analysis identified RARG and CD36 as a potential PTPRG up-regulated genes, suggesting a possible induction of cell differentiation toward an erithromyeloid phenotype. The prediction was confirmed at the mRNA and protein level, further validating the approach and identifying a new molecular mechanism of tumor suppression governed by PTPRG in a CML context.

Diagnostic Value of Specialist Systems in Sports Knee Injuries.

In order to explore the diagnostic effect of the expert system in knee sports injury, a method of diagnostic value of expert system in sports knee sports injury is proposed. This paper mainly takes 200 professional football players in sports higher vocational colleges as the research object. There are 146 male athletes and 44 female athletes; we establish a football injury ontology knowledge base and use a reasoning engine to build an intelligent expert system diagnosis system, allowing users to quickly discover diseases, accurately diagnose injuries, and obtain the best means of rehabilitation. Through the investigation, it can be seen that the body parts caused by football injuries are more complex, and the types of injuries in each part are also different. Therefore, it is particularly important to establish an intelligent retrieval system with convenient query and clear diagnosis by the expert system. With the birth and development of computer and artificial intelligence technology, the development of artificial intelligence expert systems in the medical field has become a reality. The construction of this system will have theoretical and practical significance and application value.

Development of Ontology for Knowledge of Traditions Common Culture of Countries in the Greater Mekong Subregion

The development of ontology is one important research area in the digital humanities. This study aims at creating a semantic search system for traditions common culture in the Greater Mekong Subregion (GMS) to solve problems in semantic gaps. This paper presents the second phase of the main research. It will present how to develop ontologies for the traditions and common culture in the GMS to gain a perspicuous understanding of the traditions and common culture of those countries in the region. A theoretical concept of seven steps for ontology development was applied by using an ontology editor called Hozo Ontology Editor. The main ontology found in this study included 15 main classes: common culture, history, belief, purpose, location, ritual, activity, literature, values, place, time, principle, person, equipment, and ethnic group. Traditions common culture is a subclass of common culture classes that were found to be related to all classes in ontology. This ontology will be useful for developing a semantic search system of the traditions common culture of the GMS in the next steps of the main study.

A method for the ontology-based risk management of PPP construction projects

PurposeWith the increasing complexity of public–private partnership (PPP) projects, the amount of data generated during the construction process is massive. This paper aims to develop a new information management method to cope with the risk problems involved in dealing with such data, based on domain ontologies of the construction industry, to help manage PPP risks, share and reuse risk knowledge.Design/methodology/approachRisk knowledge concepts are acquired and summarized through PPP failure cases and an extensive literature review to establish a domain framework for risk knowledge using ontology technology to help manage PPP risks.FindingsThe results indicate that the risk ontology is capable of capturing key concepts and relationships involved in managing PPP risks and can be used to facilitate knowledge reuse and storage beneficial to risk management.Research limitations/implicationsThe classes in the risk knowledge ontology model constructed in this research do not yet cover all the information in PPP project risks and need to be further extended. Moreover, only the framework and basic methods needed are developed, while the construction of a working ontology model and the relationship between implicit and explicit knowledge is a complicated process that requires repeated modifications and evaluations before it can be implemented.Practical implicationsThe ontology provides a basis for turning PPP risk information into risk knowledge to allow the effective sharing and communication of project risks between different project stakeholders. It can also have the potential to help reduce the dependence on subjectivity by mining, using and storing tacit knowledge in the risk management process.Originality/valueThe apparent suitability of the nine classes of PPP risk knowledge (project model, risk type, risk occurrence stage, risk source, risk consequence, risk likelihood, risk carrier, risk management measures and risk case) is identified, and the proposed construction method and steps for a complete domain ontology for PPP risk management are unique. A combination of criteria- and task-based evaluations is also developed for assessing the PPP risk ontology for the first time.

A supervised protein complex prediction method with network representation learning and gene ontology knowledge

BackgroundProtein complexes are essential for biologists to understand cell organization and function effectively. In recent years, predicting complexes from protein–protein interaction (PPI) networks through computational methods is one of the current research hotspots. Many methods for protein complex prediction have been proposed. However, how to use the information of known protein complexes is still a fundamental problem that needs to be solved urgently in predicting protein complexes.ResultsTo solve these problems, we propose a supervised learning method based on network representation learning and gene ontology knowledge, which can fully use the information of known protein complexes to predict new protein complexes. This method first constructs a weighted PPI network based on gene ontology knowledge and topology information, reducing the network's noise problem. On this basis, the topological information of known protein complexes is extracted as features, and the supervised learning model SVCC is obtained according to the feature training. At the same time, the SVCC model is used to predict candidate protein complexes from the protein interaction network. Then, we use the network representation learning method to obtain the vector representation of the protein complex and train the random forest model. Finally, we use the random forest model to classify the candidate protein complexes to obtain the final predicted protein complexes. We evaluate the performance of the proposed method on two publicly PPI data sets.ConclusionsExperimental results show that our method can effectively improve the performance of protein complex recognition compared with existing methods. In addition, we also analyze the biological significance of protein complexes predicted by our method and other methods. The results show that the protein complexes predicted by our method have high biological significance.

A novel Ontology-guided Attribute Partitioning ensemble learning model for early prediction of cognitive deficits using quantitative Structural MRI in very preterm infants

Structural magnetic resonance imaging studies have shown that brain anatomical abnormalities are associated with cognitive deficits in preterm infants. Brain maturation and geometric features can be used with machine learning models for predicting later neurodevelopmental deficits. However, traditional machine learning models would suffer from a large feature-to-instance ratio (i.e., a large number of features but a small number of instances/samples). Ensemble learning is a paradigm that strategically generates and integrates a library of machine learning classifiers and has been successfully used on a wide variety of predictive modeling problems to boost model performance. Attribute (i.e., feature) bagging method is the most commonly used feature partitioning scheme, which randomly and repeatedly draws feature subsets from the entire feature set. Although attribute bagging method can effectively reduce feature dimensionality to handle the large feature-to-instance ratio, it lacks consideration of domain knowledge and latent relationship among features. In this study, we proposed a novel Ontology-guided Attribute Partitioning (OAP) method to better draw feature subsets by considering the domain-specific relationship among features. With the better-partitioned feature subsets, we developed an ensemble learning framework, which is referred to as OAP-Ensemble Learning (OAP-EL). We applied the OAP-EL to predict cognitive deficits at 2 years of age using quantitative brain maturation and geometric features obtained at term equivalent age in very preterm infants. We demonstrated that the proposed OAP-EL approach significantly outperformed the peer ensemble learning and traditional machine learning approaches.

Climate migration, resilience and adaptation in the Anthropocene: Insights from the migrating Frafra to Southern Ghana

Climate-induced indigenous migration has become a radical adaptation vision in the Anthropocene. The article focuses on the problematic of representation of indigenous traditional knowledge and imagination in the Anthropocene, in Frafra ethnic group especially. The article does so by critically examining how indigenous traditional knowledge politicise anthropogenic climate change and migration conceived as a struggle between regimes of governing. It analyses alternative approaches to adaptation and resilience, from the Western scientific knowledge and modernist ontologies, often relying on the engagement of local communities, actively produced through the possibility of the existence of multiplicity in the sense of contemporaneous plurality, understood as a relational outcome and contingent relation. I argue that indigenous traditional knowledge approaches to resilience and adaptation in the Anthropocene disrupt, contest and subvert modernist discourses of climate-induced migration. It is suggested that contemporary discourses of resilience and adaptation appear to be drawing to a close as it lacks an adequate agential, transformative and also opening up alternative possibilities.

Ontological support system of managerial decision-making of production tasks for a food enterprise

Resource and energy efficiency of industrial production, in particular, food production, is a defining requirement that will ensure its functioning without loss of quality and quantity of final products. This is achieved by observing the requirements for the operational parameters of the company’s technological processes and their operational changes. Given the complexity of the functioning of the energy component of the world and Ukraine due to military operations and their consequences, the issue of quality/cost ratio has become more acute. Therefore, for large manufacturing enterprises, the development of systems for supporting management decision-making in accordance with the Industry 4.0 concept becomes relevant. This will contribute to improving the production and economic indicators of the enterprise through coordinated actions of all links of production activities by structuring and processing large amounts of heterogeneous information. The purpose of the study is to develop a decision support system for the task of choosing the structure of an automated control system based on an ontological knowledge base. The developed application ontology uses descriptive logic and is interpreted as part of a digital production double implemented by a single ontological knowledge base and ontological repository. Considering existing international standards, the OWL2 language was chosen for the implementation of the ontological knowledge base. The ontology system architecture contains an ontology server, a Node-Red application, and a user form. A project decision support system that issues recommendations based on requests for the structure of the control system for a technological facility with uncertainties, considering the requirements and restrictions set for each technological process of a food enterprise, reduces the time to choose the appropriate structures, schemes, and methods. Thus, the designer receives the necessary information, supported by knowledge from the subject area, for the synthesis of an effective automated control system. It is also assumed that the ontological system will be expanded by connecting new created applied ontologies that implement related tasks of an industrial enterprise

Комплекс онтологий для виртуальных тренажѐров с биологической обратной связью

Currently, virtual simulators are being actively developed and implemented to solve problems of a wide profile. A special place among them is occupied by solutions based on virtual reality and biofeedback. The use of an ontological approach and semantic representation of information makes it possible to optimize the processing, storing, searching and analyzing data for the implementation of virtual simulators at all stages of their life cycle. The basic principles of creating virtual simulators with biofeedback, which are the basis of the software package for their creation, reproduction and maintenance, are presented. The implementation of virtual simulators requires a description of the virtual environment, the creation of a work scenario and managing it during operation based on individual user parameters. A complex of ontologies for virtual simulators with biofeedback is proposed, which consists of an ontology of the virtual environment, an ontology of research, an ontology of the user's map, an ontology of equipment and an ontology of knowledge about the user's states. It is shown that the use of this complex of ontologies significantly reduces labor costs when designing virtual simulators and to form an information representation of the solutions being developed, to simplify the creation and introduction of changes. To demonstrate the solutions presented, an example of a virtual simulator for performing work at height is presented, with the help of which it is possible to test a person for acrophobia (fear of heights) and prevention of anxiety disorders, with the possibility of changing the parameters of the virtual environment depending on the user's condition.

Онтологии для разработки и генерации адаптивных пользовательских интерфейсов редакторов баз знаний

The article discusses a method for creating automatically generated adaptive user interfaces of knowledge base editors based on an ontological approach. The main goal that guided the authors is to improve the quality and convenience of creating and editing knowledge bases, taking into account the specifics of the subject area, the characteristics of an expert user and other parameters. The description of the concept of a new approach to generating adaptive interfaces is given. Additionally, a detailed description of all ontologies, knowledge bases and models used in the specified approach to generating interfaces, as key elements of the proposed idea. The presented ontology of knowledge about interface design introduces new abstract elements that define interface tasks without explicitly defining their visual and functional representation, while each abstract element of this ontology has several representation options - adaptations of abstract elements. The ontology of graphical elements of the user interface defines a specific visual and functional representation of abstract elements in the form of ready-made reusable WIMP elements, such as lists, buttons, menus, etc. The presented knowledge base on interface design describes the rules for its formation depending on the structure of the domain ontology, user characteristics, and usability requirements. The presented interface model describes the structure of the generated adaptive interface in accordance with the user's individual preferences and other additional information. All the ontologies and knowledge bases described in the article are currently implemented on the IACPaaS platform.

A Constructivist Ontology Relation Learning Method.

From the perspective of philosophy, ontology relations denote ultimate semantic relations of related knowledge concepts. Beyond doubt, it is still a very difficult problem on how to automatically depict and construct ontology relations because of its high abstractness. Some latest research attempted to realize ontology relation learning by learning abstract hierarchies or similarities among knowledge concepts. Inspired by the requirements of associative semantic cognition like in the human brain, a constructivist ontology relation learning (CORL) method is put forward in this study by borrowing the idea of the constructivist learning theory. Wherein, two following points are supposed: 1) each symbol knowledge is looked as a token of representing certain abstract pattern and 2) each pattern denotes a type of relation structures on other patterns, or a directly observed event data, such as physical sensing data, natural image, sound data, text word etc. So, ontology relation could be considered as the associative support degrees from other knowledge concepts to the target concept, which reflects how one knowledge ontology can be demarcated by other knowledge concepts. Then, the knowledge network can be employed to represent an entire domain knowledge system. Meanwhile, an associative random walk mechanism (ARWM) on knowledge network can be considered to explain the semantic generative process of every document. Thus, CORL can be realized by integrating ARWM into an extended latent Dirichlet allocation (LDA) model. Some theoretical and experimental analysis are done. The corresponding results demonstrate that CORL can obtain effective associative semantic relations among concept words, and gain some novel characteristics in better representing knowledge ontology than existing methods.

Minimally-Supervised Joint Learning of Event Volitionality and Subject Animacy Classification

Volitionality and subject animacy are fundamental and closely related properties of an event. Their classification is challenging because it requires contextual text understanding and a huge amount of labeled data. This paper proposes a novel method that jointly learns volitionality and subject animacy at a low cost, heuristically labeling events in a raw corpus. Volitionality labels are assigned using a small lexicon of volitional and non-volitional adverbs such as deliberately and accidentally; subject animacy labels are assigned using a list of animate and inanimate nouns obtained from ontological knowledge. We then consider the problem of learning a classifier from the labeled events so that it can perform well on unlabeled events without the words used for labeling. We view the problem as a bias reduction or unsupervised domain adaptation problem and apply the techniques. We conduct experiments with crowdsourced gold data in Japanese and English and show that our method effectively learns volitionality and subject animacy without manually labeled data.

Anatomy and the type concept in biology show that ontologies must be adapted to the diagnostic needs of research

BackgroundIn times of exponential data growth in the life sciences, machine-supported approaches are becoming increasingly important and with them the need for FAIR (Findable, Accessible, Interoperable, Reusable) and eScience-compliant data and metadata standards. Ontologies, with their queryable knowledge resources, play an essential role in providing these standards. Unfortunately, biomedical ontologies only provide ontological definitions that answer What is it? questions, but no method-dependent empirical recognition criteria that answer How does it look? questions. Consequently, biomedical ontologies contain knowledge of the underlying ontological nature of structural kinds, but often lack sufficient diagnostic knowledge to unambiguously determine the reference of a term.ResultsWe argue that this is because ontology terms are usually textually defined and conceived as essentialistic classes, while recognition criteria often require perception-based definitions because perception-based contents more efficiently document and communicate spatial and temporal information—a picture is worth a thousand words. Therefore, diagnostic knowledge often must be conceived as cluster classes or fuzzy sets. Using several examples from anatomy, we point out the importance of diagnostic knowledge in anatomical research and discuss the role of cluster classes and fuzzy sets as concepts of grouping needed in anatomy ontologies in addition to essentialistic classes. In this context, we evaluate the role of the biological type concept and discuss its function as a general container concept for groupings not covered by the essentialistic class concept.ConclusionsWe conclude that many recognition criteria can be conceptualized as text-based cluster classes that use terms that are in turn based on perception-based fuzzy set concepts. Finally, we point out that only if biomedical ontologies model also relevant diagnostic knowledge in addition to ontological knowledge, they will fully realize their potential and contribute even more substantially to the establishment of FAIR and eScience-compliant data and metadata standards in the life sciences.

The Problem of the Relationship between Ontology and Theory of Knowledge in the Works of Samara Philosophers of the Late Soviet Period

The article discusses the issue of the relationship between ontology and theory of knowledge in the works of Samara philosophers of the late Soviet period. The purpose of the study is to identify the local specifics of Samara philosophical thought by revealing the system-forming significance of the problem of the conditional unity of being and thinking at the level of a distinctive separation between ontological premises and epistemological prospects of methodological reflection in scientific knowledge. The objectives of the article are (1) to establish the characteristic features of the views of Samara philosophers on the general methodological significance of the problem of the relationship between ontology and the theory of knowledge, (2) to demonstrate the methodological relevance of their theoretical principles in the context of the philosophy of science, (3) to substantiate the applicability of the latter for the activity approach to the social interpretation of theoretical results. In the course of the study, the author uses the method of distinctions, formed in onto-epistemology by M.A. Lifshitz, methods of functional analysis, diachronic reconstruction and rationing of the worldview legitimation of a particular subject theoretical entity. Thus, the article reveals the local specificity of the Samara philosophy of the era of developed socialism. The author analyzes the program for optimizing scientific knowledge based on the Borisov–Dukhanin methodology, tied to the materialistic solution of the “main question of philosophy” (i.e., the issue of the relationship of the being and consciousness). The article pays attention to the theory of the style of scientific thinking, proposed by V.T. Salosin, to the methodological foundations and reception of the socio-philosophical theory of M.S. Kvetnoy. The author examines the productivity of the Samara precedent of the interaction of worldview and methodological aspects in philosophy. The article concludes that the specificity of the Samara philosophers’ solution of the problem of the unity of being and thinking on the basis of the Marxist understanding of the the main question of philosophy lies in the fact that it is considered as a methodological problem of achieving logical consistency to express the balance between ontological premises and epistemological perspectives.

Construction and Research on Chinese Semantic Mapping Based on Linguistic Features and Sparse Self-Learning Neural Networks.

In this paper, we adopt the algorithms of linguistic feature Rong and sparse self-learning neural network to conduct an in-depth study and analysis of Chinese semantic mapping, which complements the emotion semantic representation ability of traditional word embedding and fully explores the emotion semantic information contained in the text in the task preprocessing stage. We incorporate various semantic features such as lexical information and location information to make the model have richer emotion semantic expression, and the model also uses an attention mechanism to allow various features to interact and abstract deeper contextual internal semantic associations to improve the model's sentiment classification performance. Finally, experiments are conducted on two publicly available English sentiment classification corpora, and the results prove that the model outperforms other comparison models and effectively improves the sentiment classification performance. The model uses deep memory networks and capsule networks to construct a transfer learning framework and effectively leverages the transfer learning properties of capsule networks to transfer knowledge embedded in large-scale labeled data from similar domains to the target domain, improving the classification performance on small data sets. The use of multidimensional combined features compensates for the lack of a one-dimensional feature attention mechanism, while multiple domain category-based attention computation layers are superimposed to obtain deeper domain-specific sentiment feature information. Based on the segmented convolutional neural network, the model first introduces the dependent subtree of relational attributes to obtain the position weights of each word in the sentence, then introduces domain ontology knowledge in the output layer to constrain the extraction results, and conducts experimental comparison through the data set to verify the validity of the model, which ensures the accuracy of the network term entity and relational attribute recognition extraction and makes the knowledge map constructed in this paper. It ensures the accuracy of the extraction rate of the web term entities and relationship attributes and makes the knowledge map constructed in this paper more factual.

Fuzzy Reasoning Procedure for Ontologies based on Rough Membership Approximation

One of the major challenges in modeling a real-world domain is how to effectively represent uncertain and incomplete knowledge of that domain. Several techniques for representing uncertainty in ontologies have been proposed with some of the techniques lacking provision for vague inference. The classical tableaux-based algorithm does not provide the flexibility for reasoning over such vague ontologies. However, several extensions of the tableaux-based algorithm have been proposed to cope with fuzzy reasoning. Similarly, several alternative reasoning methods for incomplete, inconsistent, and uncertain ontologies have been proposed. One of the major limitations of most of those techniques is that they require reengineering existing ontologies to cope with uncertainty. This paper proposes a satisfiability algorithm for vague ontologies that uses a rough set to approximate the concepts and roles. The proposed technique takes advantage of the existing ontology knowledge base to achieve vague reasoning without the need of reengineering the ontology. The results show that the proposed technique conforms to the tableaux-based algorithm while providing a way of reasoning over the uncertain aspects of ontologies.

Reasoning in inconsistent prioritized knowledge bases: an argumentative approach

<p>A study of query answering in prioritized ontological knowledge bases (KBs) has received attention in recent years. While several semantics of query answering have been proposed and their complexity is rather well-understood, the problem of explaining inconsistency-tolerant query answers has paid less attention. Explaining query answers permits users to understand not only what is entailed or not entailed by an inconsistent DL-LiteR KBs in the presence of priority, but also why. We, therefore, concern with the use of argumentation frameworks to allow users to better understand explanation techniques of querying answers over inconsistent DL-LiteR KBs in the presence of priority. More specifically, we propose a new variant of Dung’s argumentation frameworks, which corresponds to a given inconsistent DL-LiteR KB. We clarify a close relation between preferred subtheories adopted in such prioritized DL-LiteR setting and acceptable semantics of the corresponding argumentation framework. The significant result paves the way for applying algorithms and proof theories to establish preferred subtheories inferences in prioritized DL-LiteR KBs.</p>