Relevant bibliographies by topics / Formal ontologies

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Academic literature on the topic 'Formal ontologies'

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Published: 10 March 2023

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Journal articles on the topic "Formal ontologies"

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Sanfilippo, Emilio M., Yoshinobu Kitamura, and Robert I. M. Young. "Formal ontologies in manufacturing." Applied Ontology 14, no. 2 (April 25, 2019): 119–25. http://dx.doi.org/10.3233/ao-190209.

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Otte, J. Neil, John Beverley, and Alan Ruttenberg. "BFO: Basic Formal Ontology1." Applied Ontology 17, no. 1 (March 15, 2022): 17–43. http://dx.doi.org/10.3233/ao-220262.

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Basic Formal Ontology (BFO) is a top-level ontology consisting of thirty-six classes, designed to support information integration, retrieval, and analysis across all domains of scientific investigation, presently employed in over 350 ontology projects around the world. BFO is a genuine top-level ontology, containing no terms particular to material domains, such as physics, medicine, or psychology. In this paper, we demonstrate how a series of cases illustrating common types of change may be represented by universals, defined classes, and relations employing the BFO framework. We provide discussion of these cases to provide a template for other ontologists using BFO, as well as to facilitate comparison with the strategies proposed by ontologists using different top-level ontologies.
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Lumb, L. I., J. R. Freemantle, J. I. Lederman, and K. D. Aldridge. "Annotation modeling with formal ontologies: Implications for informal ontologies." Computers & Geosciences 35, no. 4 (April 2009): 855–61. http://dx.doi.org/10.1016/j.cageo.2008.03.009.

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Sanfilippo, Emilio, and Walter Terkaj. "Editorial: Formal Ontologies meet Industry." Procedia Manufacturing 28 (2019): 174–76. http://dx.doi.org/10.1016/j.promfg.2018.12.028.

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Abrusci, V. Michele, Christophe Fouqueré, and Marco Romano. "Formal Ontologies and Coherent Spaces." Journal of Applied Logic 12, no. 1 (March 2014): 67–74. http://dx.doi.org/10.1016/j.jal.2013.07.003.

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Lukashevich, N. V. "Concepts in formal and linguistic ontologies." Automatic Documentation and Mathematical Linguistics 45, no. 4 (August 2011): 155–62. http://dx.doi.org/10.3103/s0005105511040030.

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Shaked, Avi, and Oded Margalit. "Sustainable Risk Identification Using Formal Ontologies." Algorithms 15, no. 9 (September 2, 2022): 316. http://dx.doi.org/10.3390/a15090316.

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The cyber threat landscape is highly dynamic, posing a significant risk to the operations of systems and organisations. An organisation should, therefore, continuously monitor for new threats and properly contextualise them to identify and manage the resulting risks. Risk identification is typically performed manually, relying on the integration of information from various systems as well as subject matter expert knowledge. This manual risk identification hinders the systematic consideration of new, emerging threats. This paper describes a novel method to promote automated cyber risk identification: OnToRisk. This artificial intelligence method integrates information from various sources using formal ontology definitions, and then relies on these definitions to robustly frame cybersecurity threats and provide risk-related insights. We describe a successful case study implementation of the method to frame the threat from a newly disclosed vulnerability and identify its induced organisational risk. The case study is representative of common and widespread real-life challenges, and, therefore, showcases the feasibility of using OnToRisk to sustainably identify new risks. Further applications may contribute to establishing OnToRisk as a comprehensive, disciplined mechanism for risk identification.
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Jongeling, T. B., and P. P. Kirschenmann. "FORMAL AND HYPOTHETICAL OR HEURISTIC ONTOLOGIES." Grazer Philosophische studien 29, no. 1 (August 13, 1987): 217–23. http://dx.doi.org/10.1163/18756735-90000322.

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Jansen, L., and S. Schulz. "Formal Ontologies in Biomedical Knowledge Representation." Yearbook of Medical Informatics 22, no. 01 (August 2013): 132–46. http://dx.doi.org/10.1055/s-0038-1638845.

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Summary Objectives: Medical decision support and other intelligent applications in the life sciences depend on increasing amounts of digital information. Knowledge bases as well as formal ontologies are being used to organize biomedical knowledge and data. However, these two kinds of artefacts are not always clearly distinguished. Whereas the popular RDF(S) standard provides an intuitive triple-based representation, it is semantically weak. Description logics based ontology languages like OWL-DL carry a clear-cut semantics, but they are computationally expensive, and they are often misinterpreted to encode all kinds of statements, including those which are not ontological. Method: We distinguish four kinds of statements needed to comprehensively represent domain knowledge: universal statements, terminological statements, statements about particulars and contingent statements. We argue that the task of formal ontologies is solely to represent universal statements, while the non-ontological kinds of statements can nevertheless be connected with ontological representations. To illustrate these four types of representations, we use a running example from parasitology. Results: We finally formulate recommendations for semantically adequate ontologies that can efficiently be used as a stable framework for more context-dependent biomedical knowledge representation and reasoning applications like clinical decision support systems.
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Müller, R., O. Mailahn, and R. Peifer. "Tool: Eine Sprachdomäne für die Montageplanung*/A domain specific language for assembly planning – Software-supported planning of human-robot cooperation based on ontologies." wt Werkstattstechnik online 108, no. 09 (2018): 606–10. http://dx.doi.org/10.37544/1436-4980-2018-09-42.

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Die Planung von Montagesystemen wird durch die Einführung von cyber-physischen Modulen und neuen Formen der Zusammenarbeit von Mensch und Roboter zunehmend komplexer. Ontologien können Planungswissen bezüglich Beziehungen und Restriktionen formal abbilden. Mit der hier beschriebenen Sprachdomäne werden Ontologien für Montageplaner zugänglich und anwendbar. Die Planung kann auf diese Weise beschleunigt und flexibilisiert werden.   The planning of assembly systems is becoming increasingly complex with the introduction of cyber-physical modules and new forms of human-robot cooperation. Ontologies can formally capture planning knowledge in terms of relationships and restrictions. The domain specific language described here makes ontologies accessible and usable for assembly planners. Thus, planning may be accelerated and designed more flexibly.
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Dissertations / Theses on the topic "Formal ontologies"

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Lieto, Antonio. "Non classical concept representation and reasoning in formal ontologies." Doctoral thesis, Universita degli studi di Salerno, 2012. http://hdl.handle.net/10556/346.

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2010 - 2011
Formal ontologies are nowadays widely considered a standard tool for knowledge representation and reasoning in the Semantic Web. In this context, they are expected to play an important role in helping automated processes to access information. Namely: they are expected to provide a formal structure able to explicate the relationships between different concepts/terms, thus allowing intelligent agents to interpret, correctly, the semantics of the web resources improving the performances of the search technologies. Here we take into account a problem regarding Knowledge Representation in general, and ontology based representations in particular; namely: the fact that knowledge modeling seems to be constrained between conflicting requirements, such as compositionality, on the one hand and the need to represent prototypical information on the other. In particular, most common sense concepts seem not to be captured by the stringent semantics expressed by such formalisms as, for example, Description Logics (which are the formalisms on which the ontology languages have been built). The aim of this work is to analyse this problem, suggesting a possible solution suitable for formal ontologies and semantic web representations. The questions guiding this research, in fact, have been: is it possible to provide a formal representational framework which, for the same concept, combines both the classical modelling view (accounting for compositional information) and defeasible, prototypical knowledge ? Is it possible to propose a modelling architecture able to provide different type of reasoning (e.g. classical deductive reasoning for the compositional component and a non monotonic reasoning for the prototypical one)? We suggest a possible answer to these questions proposing a modelling framework able to represent, within the semantic web languages, a multilevel representation of conceptual information, integrating both classical and non classical (typicality based) information. Within this framework we hypothesise, at least in principle, the co-existence of multiple reasoning processes involving the different levels of representation. This works is organized as follows: in chapter 1 the semantic web languages and the description logics formalisms on which they are based are briefly presented. Then, in chapter 2, the problem on which this work is focused (e.g. conceptual representation) is illustrated and the general idea of the proposed multi-layer framework is sketched. In chapter 3 the psychological theories about concepts based on prototypes and exemplars are surveyed. In this chapter we argue that such distinction can be useful in our approach because it allows (i) to have a more complete representation of the concepts and (ii) to hypothesise different types of non monotonic reasoning processes (e.g. non monotonic categorization). In chapter 4 the proposed modeling architecture is presented and, in chapter 5, it is evaluated on particular information retrieval tasks. The chapter 6 is dedicated to the conclusions. [edited by author]
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Linck, Ricardo Ramos. "Conceptual modeling of formal and material relations applied to ontologies." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2014. http://hdl.handle.net/10183/108626.

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Ontologias representam uma conceitualização compartilhada de uma comunidade de conhecimento. São construídas a partir da descrição dos significados dos conceitos, descritos através de seus atributos e dos relacionamentos entre os conceitos. Conceitos se referem ao objeto da conceitualização, o universo do discurso. São caracterizados por seus atributos e domínios de valores possíveis. Relacionamentos são utilizados para descreverem de que forma os conceitos se estruturam no mundo. Nas ontologias todos os conceitos são hierarquicamente definidos, porém existem outros relacionamentos que são definicionais, dando identidade aos conceitos e sentido ao mundo. Além dos relacionamentos de subsunção que constroem as taxonomias de conceitos, outras relações formais e materiais auxiliam na estruturação do domínio e na definição conceitual. As ferramentas de modelagem, no entanto, ainda são falhas em diferenciar os vários tipos de relacionamentos formais e materiais para atribuir as possibilidades de raciocínio automático. Em especial, relacionamentos mereológicos e partonômicos carecem de opções de implementação que permitam extrair o potencial semântico da modelagem. Este projeto de pesquisa tem como ponto de partida o estudo da literatura sobre ontologias e relações, em especial sobre relações formais e materiais, incluindo relações mereológicas e partonômicas, revisando os princípios encontrados nas ontologias. Além disso, nós identificamos os fundamentos teóricos das relações e analisamos a aplicação dos conceitos das relações sobre as principais ontologias de fundamentação em prática na atualidade. Na sequência, a partir das propostas levantadas, este trabalho propõe uma alternativa para a modelagem conceitual destas relações em uma ontologia de domínio visual. Esta alternativa foi disponibilizada na ferramenta de construção de ontologias do Projeto Obaitá, a qual está sendo desenvolvida pelo Grupo de Pesquisa de Bancos de Dados Inteligentes (BDI) da UFRGS.
Ontologies represent a shared conceptualization of a knowledge community. They are built from the description of the meaning of concepts, expressed through their attributes and their relationships. Concepts refer to the object of conceptualization, the universe of discourse. They are characterized by their attributes and domains of possible values. Relationships are used to describe how the concepts are structured in the world. In ontologies all concepts are hierarchically defined, however there are other relationships that are definitional, giving identity to the concepts and meaning to the world. In addition to the subsumption relationships that build the taxonomies of concepts, other formal and material relations assist in structuring the domain and the conceptual definition. The modeling tools, however, are still deficient in differentiating the various types of formal and material relationships in order to assign the possibilities of automated reasoning. In particular, mereological and partonomic relationships lack of implementation options that allow extracting the semantic potential when modeling. This research project takes as a starting point the study of the literature on ontologies and relations, especially on formal and material relations, including mereological and partonomic relations, reviewing the principles found on ontologies. Furthermore, we identify the theoretical foundations of the relations and analyze the application of the relations concepts to the main foundational ontologies in use nowadays. Following, from the raised proposals, this work proposes an alternative for the conceptual modeling of these relations in a visual domain ontology. This alternative has been made available on the ontology building tool of the Obaitá Project, which is under development by the Intelligent Databases Research Group (BDI) from UFRGS.
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Venugopal, Manu. "Formal specification of industry foundation class concepts using engineering ontologies." Diss., Georgia Institute of Technology, 2011. http://hdl.handle.net/1853/42868.

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Architecture, Engineering, Construction (AEC) and Facilities Management (FM) involve domains that require a very diverse set of information and model exchanges to fully realize the potential of Building Information Modeling (BIM). Industry Foundation Classes (IFC) provides a neutral and open schema for interoperability. Model View Definitions (MVD) provide a common subset for specifying the exchanges using IFC, but are expensive to build, test and maintain. A semantic analysis of IFC data schema illustrates the complexities of embedding semantics in model views. A software engineering methodology based on formal specification of shared resources, reusable components and standards that are applicable to the AEC-FM industry for development of a Semantic Exchange Module (SEM) structure for IFC schema is adopted for this research. This SEM structure is based on engineering ontologies that are capable of developing more consistent MVDs. In this regard, Ontology is considered as a machine-readable set of definitions that create a taxonomy of classes and subclasses, and relationships between them. Typically, the ontology contains the hierarchical description of important entities that are used in IFC, along with their properties and business rules. This model of an ontological framework, similar to that of Semantic Web, makes the IFC more formal and consistent as it is capable of providing precise definition of terms and vocabulary. The outcome of this research, a formal classification structure for IFC implementations for the domain of Precast/ Prestressed Concrete Industry, when implemented by software developers, provides the mechanism for applications such as modular MVDs, smart and complex querying of product models, and transaction based services, based on the idea of testable and reusable SEMs. It can be extended and also helps in consistent implementation of rule languages across different domains within AEC-FM, making data sharing across applications simpler with limited rework. This research is expected to impact the overall interoperability of applications in the BIM realm.
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Hacid, Kahina. "Handling domain knowledge in system design models. An ontology based approach." Phd thesis, Toulouse, INPT, 2018. http://oatao.univ-toulouse.fr/20157/7/HACID_kahina.pdf.

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Complex systems models are designed in heterogeneous domains and this heterogeneity is rarely considered explicitly when describing and validating processes. Moreover, these systems usually involve several domain experts and several design models corresponding to different analyses (views) of the same system. However, no explicit information regarding the characteristics neither of the domain nor of the performed system analyses is given. In our thesis, we propose a general framework offering first, the formalization of domain knowledge using ontologies and second, the capability to strengthen design models by making explicit references to the domain knowledgeformalized in these ontology. This framework also provides resources for making explicit the features of an analysis by formalizing them within models qualified as ‘’points of view ‘’. We have set up two deployments of our approach: a Model Driven Engineering (MDE) based deployment and a formal methods one based on proof and refinement. This general framework has been validated on several no trivial case studies issued from system engineering.
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Leshi, Olumide. "An Approach to Extending Ontologies in the Nanomaterials Domain." Thesis, Linköpings universitet, Institutionen för datavetenskap, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-170255.

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As recently as the last decade or two, data-driven science workflows have become increasingly popular and semantic technology has been relied on to help align often parallel research efforts in the different domains and foster interoperability and data sharing. However, a key challenge is the size of the data and the pace at which it is being generated, so much that manual procedures lag behind. Thus, eliciting automation of most workflows. In this study, the effort is to continue investigating ways by which some tasks performed by experts in the nanotechnology domain, specifically in ontology engineering, could benefit from automation. An approach, featuring phrase-based topic modelling and formal topical concept analysis is further motivated, together with formal implication rules, to uncover new concepts and axioms relevant to two nanotechnology-related ontologies. A corpus of 2,715 nanotechnology research articles helps showcase that the approach can scale, as seen in a number of experiments conducted. The usefulness of document text ranking as an alternative form of input to topic models is highlighted as well as the benefit of implication rules to the task of concept discovery. In all, a total of 203 new concepts are uncovered by the approach to extend the referenced ontologies
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Hassan, Mohsen. "Knowledge Discovery Considering Domain Literature and Ontologies : Application to Rare Diseases." Thesis, Université de Lorraine, 2017. http://www.theses.fr/2017LORR0092/document.

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De par leur grand nombre et leur sévérité, les maladies rares (MR) constituent un enjeu de santé majeur. Des bases de données de référence, comme Orphanet et Orphadata, répertorient les informations disponibles à propos de ces maladies. Cependant, il est difficile pour ces bases de données de proposer un contenu complet et à jour par rapport à ce qui est disponible dans la littérature. En effet, des millions de publications scientifiques sur ces maladies sont disponibles et leur nombre augmente de façon continue. Par conséquent, il serait très fastidieux d’extraire manuellement et de façon exhaustive des informations sur ces maladies. Cela motive le développement des approches semi-automatiques pour extraire l’information des textes et la représenter dans un format approprié pour son utilisation dans d’autres applications. Cette thèse s’intéresse à l’extraction de connaissances à partir de textes et propose d’utiliser les résultats de l’extraction pour enrichir une ontologie de domaine. Nous avons étudié trois directions de recherche: (1) l’extraction de connaissances à partir de textes, et en particulier l’extraction de relations maladie-phénotype (M-P); (2) l’identification d’entité nommées complexes, en particulier de phénotypes de MR; et (3) l’enrichissement d’une ontologie en considérant les connaissances extraites à partir de texte. Tout d’abord, nous avons fouillé une collection de résumés d’articles scientifiques représentés sous la forme graphes pour un extraire des connaissances sur les MR. Nous nous sommes concentrés sur la complétion de la description des MR, en extrayant les relations M-P. Cette trouve des applications dans la mise à jour des bases de données de MR telles que Orphanet. Pour cela, nous avons développé un système appelé SPARE* qui extrait les relations M-P à partir des résumés PubMed, où les phénotypes et les MR sont annotés au préalable par un système de reconnaissance des entités nommées. SPARE* suit une approche hybride qui combine une méthode basée sur des patrons syntaxique, appelée SPARE, et une méthode d’apprentissage automatique (les machines à vecteurs de support ou SVM). SPARE* bénéficié à la fois de la précision relativement bonne de SPARE et du bon rappel des SVM. Ensuite, SPARE* a été utilisé pour identifier des phénotypes candidats à partir de textes. Pour cela, nous avons sélectionné des patrons syntaxiques qui sont spécifiques aux relations M-P uniquement. Ensuite, ces patrons sont relaxés au niveau de leur contrainte sur le phénotype pour permettre l’identification de phénotypes candidats qui peuvent ne pas être références dans les bases de données ou les ontologies. Ces candidats sont vérifiés et validés par une comparaison avec les classes de phénotypes définies dans une ontologie de domaine comme HPO. Cette comparaison repose sur une modèle sémantique et un ensemble de règles de mises en correspondance définies manuellement pour cartographier un phénotype candidate extrait de texte avec une classe de l’ontologie. Nos expériences illustrent la capacité de SPARE* à des phénotypes de MR déjà répertoriés ou complètement inédits. Nous avons appliqué SPARE* à un ensemble de résumés PubMed pour extraire les phénotypes associés à des MR, puis avons mis ces phénotypes en correspondance avec ceux déjà répertoriés dans l’encyclopédie Orphanet et dans Orphadata ; ceci nous a permis d’identifier de nouveaux phénotypes associés à la maladie selon les articles, mais pas encore listés dans Orphanet ou Orphadata.Enfin, nous avons appliqué les structures de patrons pour classer les MR et enrichir une ontologie préexistante. Tout d’abord, nous avons utilisé SPARE* pour compléter les descriptions en terme de phénotypes de MR disponibles dans Orphadata. Ensuite, nous proposons de compter et grouper les MR au regard de leur description phénotypique, et ce en utilisant les structures de patron. [...]
Even if they are uncommon, Rare Diseases (RDs) are numerous and generally sever, what makes their study important from a health-care point of view. Few databases provide information about RDs, such as Orphanet and Orphadata. Despite their laudable effort, they are incomplete and usually not up-to-date in comparison with what exists in the literature. Indeed, there are millions of scientific publications about these diseases, and the number of these publications is increasing in a continuous manner. This makes the manual extraction of this information painful and time consuming and thus motivates the development of semi-automatic approaches to extract information from texts and represent it in a format suitable for further applications. This thesis aims at extracting information from texts and using the result of the extraction to enrich existing ontologies of the considered domain. We studied three research directions (1) extracting relationships from text, i.e., extracting Disease-Phenotype (D-P) relationships; (2) identifying new complex entities, i.e., identifying phenotypes of a RD and (3) enriching an existing ontology on the basis of the relationship previously extracted, i.e., enriching a RD ontology. First, we mined a collection of abstracts of scientific articles that are represented as a collection of graphs for discovering relevant pieces of biomedical knowledge. We focused on the completion of RD description, by extracting D-P relationships. This could find applications in automating the update process of RD databases such as Orphanet. Accordingly, we developed an automatic approach named SPARE*, for extracting D-P relationships from PubMed abstracts, where phenotypes and RDs are annotated by a Named Entity Recognizer. SPARE* is a hybrid approach that combines a pattern-based method, called SPARE, and a machine learning method (SVM). It benefited both from the relatively good precision of SPARE and from the good recall of the SVM. Second, SPARE* has been used for identifying phenotype candidates from texts. We selected high-quality syntactic patterns that are specific for extracting D-P relationships only. Then, these patterns are relaxed on the phenotype constraint to enable extracting phenotype candidates that are not referenced in databases or ontologies. These candidates are verified and validated by the comparison with phenotype classes in a well-known phenotypic ontology (e.g., HPO). This comparison relies on a compositional semantic model and a set of manually-defined mapping rules for mapping an extracted phenotype candidate to a phenotype term in the ontology. This shows the ability of SPARE* to identify existing and potentially new RD phenotypes. We applied SPARE* on PubMed abstracts to extract RD phenotypes that we either map to the content of Orphanet encyclopedia and Orphadata; or suggest as novel to experts for completing these two resources. Finally, we applied pattern structures for classifying RDs and enriching an existing ontology. First, we used SPARE* to compute the phenotype description of RDs available in Orphadata. We propose comparing and grouping RDs in regard to their phenotypic descriptions, and this by using pattern structures. The pattern structures enable considering both domain knowledge, consisting in a RD ontology and a phenotype ontology, and D-P relationships from various origins. The lattice generated from this pattern structures suggests a new classification of RDs, which in turn suggests new RD classes that do not exist in the original RD ontology. As their number is large, we proposed different selection methods to select a reduced set of interesting RD classes that we suggest for experts for further analysis
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Kriegel, Francesco [Verfasser], Franz [Akademischer Betreuer] Baader, Franz [Gutachter] Baader, and Sergei O. [Gutachter] Kuznetsov. "Constructing and Extending Description Logic Ontologies using Methods of Formal Concept Analysis / Francesco Kriegel ; Gutachter: Franz Baader, Sergei O. Kuznetsov ; Betreuer: Franz Baader." Dresden : Technische Universität Dresden, 2019. http://d-nb.info/1226942601/34.

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Tsatsaronis, George, Yue Ma, Alina Petrova, Maria Kissa, Felix Distel, Franz Baader, and Michael Schroeder. "Formalizing biomedical concepts from textual definitions." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-192186.

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Background Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. Results We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations’ domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations’ domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. Conclusions The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.
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Petrova, Alina, Yue Ma, George Tsatsaronis, Maria Kissa, Felix Distel, Franz Baader, and Michael Schroeder. "Formalizing biomedical concepts from textual definitions." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2016. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-191181.

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BACKGROUND: Ontologies play a major role in life sciences, enabling a number of applications, from new data integration to knowledge verification. SNOMED CT is a large medical ontology that is formally defined so that it ensures global consistency and support of complex reasoning tasks. Most biomedical ontologies and taxonomies on the other hand define concepts only textually, without the use of logic. Here, we investigate how to automatically generate formal concept definitions from textual ones. We develop a method that uses machine learning in combination with several types of lexical and semantic features and outputs formal definitions that follow the structure of SNOMED CT concept definitions. RESULTS: We evaluate our method on three benchmarks and test both the underlying relation extraction component as well as the overall quality of output concept definitions. In addition, we provide an analysis on the following aspects: (1) How do definitions mined from the Web and literature differ from the ones mined from manually created definitions, e.g., MeSH? (2) How do different feature representations, e.g., the restrictions of relations' domain and range, impact on the generated definition quality?, (3) How do different machine learning algorithms compare to each other for the task of formal definition generation?, and, (4) What is the influence of the learning data size to the task? We discuss all of these settings in detail and show that the suggested approach can achieve success rates of over 90%. In addition, the results show that the choice of corpora, lexical features, learning algorithm and data size do not impact the performance as strongly as semantic types do. Semantic types limit the domain and range of a predicted relation, and as long as relations' domain and range pairs do not overlap, this information is most valuable in formalizing textual definitions. CONCLUSIONS: The analysis presented in this manuscript implies that automated methods can provide a valuable contribution to the formalization of biomedical knowledge, thus paving the way for future applications that go beyond retrieval and into complex reasoning. The method is implemented and accessible to the public from: https://github.com/alifahsyamsiyah/learningDL.
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Nasiri, Khoozani Ehsan. "An ontological framework for the formal representation and management of human stress knowledge." Thesis, Curtin University, 2011. http://hdl.handle.net/20.500.11937/2220.

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There is a great deal of information on the topic of human stress which is embedded within numerous papers across various databases. However, this information is stored, retrieved, and used often discretely and dispersedly. As a result, discovery and identification of the links and interrelatedness between different aspects of knowledge on stress is difficult. This restricts the effective search and retrieval of desired information. There is a need to organize this knowledge under a unifying framework, linking and analysing it in mutual combinations so that we can obtain an inclusive view of the related phenomena and new knowledge can emerge. Furthermore, there is a need to establish evidence-based and evolving relationships between the ontology concepts.Previous efforts to classify and organize stress-related phenomena have not been sufficiently inclusive and none of them has considered the use of ontology as an effective facilitating tool for the abovementioned issues.There have also been some research works on the evolution and refinement of ontology concepts and relationships. However, these fail to provide any proposals for an automatic and systematic methodology with the capacity to establish evidence-based/evolving ontology relationships.In response to these needs, we have developed the Human Stress Ontology (HSO), a formal framework which specifies, organizes, and represents the domain knowledge of human stress. This machine-readable knowledge model is likely to help researchers and clinicians find theoretical relationships between different concepts, resulting in a better understanding of the human stress domain and its related areas. The HSO is formalized using OWL language and Protégé tool.With respect to the evolution and evidentiality of ontology relationships in the HSO and other scientific ontologies, we have proposed the Evidence-Based Evolving Ontology (EBEO), a methodology for the refinement and evolution of ontology relationships based on the evidence gleaned from scientific literature. The EBEO is based on the implementation of a Fuzzy Inference System (FIS).Our evaluation results showed that almost all stress-related concepts of the sample articles can be placed under one or more category of the HSO. Nevertheless, there were a number of limitations in this work which need to be addressed in future undertakings.The developed ontology has the potential to be used for different data integration and interoperation purposes in the domain of human stress. It can also be regarded as a foundation for the future development of semantic search engines in the stress domain.
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Books on the topic "Formal ontologies"

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Cuel, Roberta, and Robert Young, eds. Formal Ontologies Meet Industry. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7.

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Stefano, Borgo, Lesmo Leonardo, and International Workshop on Formal Ontologies Meet Industry (3rd : 2008 : Turin, Italy), eds. Formal ontologies meet industry. Amsterdam: IOS Press, 2008.

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International Workshop on Formal Ontologies Meet Industry (4th 2009 Vicenza, Italy). Formal ontologies meet industry. Amsterdam: IOS Press, 2009.

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Zhang, Guo-Qiang, Rashmie Abeysinghe, and Licong Cui. Formal Methods for the Analysis of Biomedical Ontologies. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-12131-9.

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Simões, Maria da Graça. Da abstração à complexidade formal: Relações conceptuais num tesauro. Coimbra: Almedina, 2008.

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Netherlands) International Workshop on Formal Ontologies Meet Industry (5th 2011 Delft. Formal ontologies meet industry: Proceedings of the fifth international workshop (FOMI 2011). Amsterdam: IOS Press, 2011.

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Austria) FOIS (Conference) (7th 2012 Graz. Formal ontology in information systems: Proceedings of the seventh International Conference (FOIS 2012). Amsterdam: IOS Press, 2012.

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service), SpringerLink (Online, ed. On the Mathematics of Modelling, Metamodelling, Ontologies and Modelling Languages. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012.

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Ching-man, Au Yeung, and Leung Ho-Fung, eds. Fuzzy computational ontologies in contexts: Formal models of knowledge representation with membership degree and typicality of objects, and their applications. Beijing: Higher Education Press, 2012.

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Poli, Roberto. Ontologia formale. Genova: Marietti, 1992.

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Book chapters on the topic "Formal ontologies"

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Stumme, Gerd. "Formal Concept Analysis." In Handbook on Ontologies, 177–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92673-3_8.

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Wyssusek, Boris. "Can Ontology Inform Ontologies?" In Formal Concept Analysis, 82. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-01815-2_7.

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Frixione, Marcello, and Antonio Lieto. "Formal Ontologies, Exemplars, Prototypes." In Advances in Conceptual Modeling. Recent Developments and New Directions, 210–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24574-9_27.

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Paschke, Adrian, Tara Athan, Davide Sottara, Elisa Kendall, and Roy Bell. "A Representational Analysis of the API4KP Metamodel." In Formal Ontologies Meet Industry, 1–12. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_1.

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Palmer, Claire, Esmond Neil Urwin, Ester Palacios Rodríguez, Francisco Sanchez Cid, Jose Miguel Pinazo-Sánchez, Sonja Pajkovska-Goceva, and Robert Young. "An Ontology for Global Production Network Design and Reconfiguration." In Formal Ontologies Meet Industry, 113–25. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_10.

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El Kadiri, Soumaya, Walter Terkaj, Esmond Neil Urwin, Claire Palmer, Dimitris Kiritsis, and Robert Young. "Ontology in Engineering Applications." In Formal Ontologies Meet Industry, 126–37. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_11.

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Lewańska, Elżbieta, and Monika Kaczmarek. "Ontologies for Business Networks Identification." In Formal Ontologies Meet Industry, 13–24. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_2.

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Chui, Carmen, Michael Grüninger, and Mark van Berkel. "Ontology Mapping in an e-Commerce Application." In Formal Ontologies Meet Industry, 25–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_3.

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Kaczmarek, Monika. "Ontologies in the Realm of Enterprise Modeling – A Reality Check." In Formal Ontologies Meet Industry, 39–50. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_4.

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Pittet, Perrine, and Jérôme Barthélémy. "Experience of Formal Application Ontology Development to Enhance User Understanding in a Geo Business Intelligence SaaS Platform." In Formal Ontologies Meet Industry, 51–62. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21545-7_5.

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Conference papers on the topic "Formal ontologies"

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Yu, Sun, Xia Youming, and Zhiping Li. "Formal contexts in ontologies." In Education (ICCSE 2011). IEEE, 2011. http://dx.doi.org/10.1109/iccse.2011.6028620.

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Cristani, Marco, and Roberta Ferrario. "Statistical Pattern Recognition Meets Formal Ontologies." In the 2014 Workshop. New York, New York, USA: ACM Press, 2014. http://dx.doi.org/10.1145/2666253.2666254.

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Frixione, Marcello, and Antonio Lieto. "Representing and reasoning on typicality in formal ontologies." In the 7th International Conference. New York, New York, USA: ACM Press, 2011. http://dx.doi.org/10.1145/2063518.2063534.

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Poernomo, Iman, Timur Umarov, and Fuad Hajiyev. "Formal ontologies for data-centric business process management." In 2011 5th International Conference on Application of Information and Communication Technologies (AICT). IEEE, 2011. http://dx.doi.org/10.1109/icaict.2011.6110897.

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Cross, Valerie V., and Wenting Yi. "Formal concept analysis for ontologies and their annotation files." In 2008 IEEE 16th International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2008. http://dx.doi.org/10.1109/fuzzy.2008.4630646.

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Chebieb, Abdelkrim, and Yamine Ait-Ameur. "Formal Verification of Plastic User Interfaces Exploiting Domain Ontologies." In 2015 9th International Symposium on Theoretical Aspects of Software Engineering (TASE). IEEE, 2015. http://dx.doi.org/10.1109/tase.2015.25.

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Scafoglieri, Federico Maria, and Domenico Lembo. "A Formal Framework for Coupling Document Spanners with Ontologies." In 2019 IEEE Second International Conference on Artificial Intelligence and Knowledge Engineering (AIKE). IEEE, 2019. http://dx.doi.org/10.1109/aike.2019.00036.

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"THE COMPUTATIONAL REPRESENTATION OF CONCEPTS IN FORMAL ONTOLOGIES - Some General Considerations." In International Conference on Knowledge Engineering and Ontology Development. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0003095903960403.

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Rodríguez, Jose María Alvarez, Valentín Moreno, and Juan Llorens. "Formal ontologies and data shapes within the Software Engineering development lifecycle." In The 31st International Conference on Software Engineering and Knowledge Engineering. KSI Research Inc. and Knowledge Systems Institute Graduate School, 2019. http://dx.doi.org/10.18293/seke2019-114.

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"FORMAL METHOD FOR AUTOMATIC AND SEMANTIC MAPPING OF DISTRIBUTED SERVICE-ONTOLOGIES." In 2nd International Conference on Software and Data Technologies. SciTePress - Science and and Technology Publications, 2007. http://dx.doi.org/10.5220/0001329602590263.

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Reports on the topic "Formal ontologies"

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Baader, Franz, Stefan Borgwardt, and Barbara Morawska. A Goal-Oriented Algorithm for Unification in ELHR+ w.r.t. Cycle-Restricted Ontologies. Technische Universität Dresden, 2012. http://dx.doi.org/10.25368/2022.189.

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Unification in Description Logics (DLs) has been proposed as an inference service that can, for example, be used to detect redundancies in ontologies. For the DL EL, which is used to define several large biomedical ontologies, unification is NP-complete. A goal-oriented NP unification algorithm for EL that uses nondeterministic rules to transform a given unification problem into solved form has recently been presented. In this report, we extend this goal-oriented algorithm in two directions: on the one hand, we add general concept inclusion axioms (GCIs), and on the other hand, we add role hierarchies (H) and transitive roles (R+). For the algorithm to be complete, however, the ontology consisting of the GCIs and role axioms needs to satisfy a certain cycle restriction.
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Thost, Veronika, Jan Holste, and Özgür Özçep. On Implementing Temporal Query Answering in DL-Lite. Technische Universität Dresden, 2015. http://dx.doi.org/10.25368/2022.218.

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Ontology-based data access augments classical query answering over fact bases by adopting the open-world assumption and by including domain knowledge provided by an ontology. We implemented temporal query answering w.r.t. ontologies formulated in the Description Logic DL-Lite. Focusing on temporal conjunctive queries (TCQs), which combine conjunctive queries via the operators of propositional linear temporal logic, we regard three approaches for answering them: an iterative algorithm that considers all data available; a window-based algorithm; and a rewriting approach, which translates the TCQs to be answered into SQL queries. Since the relevant ontological knowledge is already encoded into the latter queries, they can be answered by a standard database system. Our evaluation especially shows that implementations of both the iterative and the window-based algorithm answer TCQs within a few milliseconds, and that the former achieves a constant performance, even if data is growing over time.
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