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Stefan Schulz - One of the best experts on this subject based on the ideXlab platform.
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ontological representation of laboratory test observables challenges and perspectives in the snomed ct observable entity model adoption
Artificial Intelligence in Medicine in Europe, 2017Co-Authors: Melissa Mary, Lina Fatima Soualmia, Xavier Gansel, Stefan Jacques Darmoni, Daniel Karlsson, Stefan SchulzAbstract:The emergence of electronic health records has highlighted the need for semantic standards for representation of observations in laboratory medicine. Two such standards are LOINC, with a focus on detailed encoding of lab tests, and SNOMED CT, which is more general, including the representation of qualitative and ordinal test results. In this paper we will discuss how lab observation entries can be represented using SNOMED CT. We use resources provided by the Regenstrief Institute and SNOMED International collaboration, which formalize LOINC terms as SNOMED CT post-coordinated expressions. We demonstrate the benefits brought by SNOMED CT to classify lab tests. We then propose a SNOMED CT based model for lab observation entries aligned with the BioTopLite2 (BTL2) upper Level Ontology. We provide examples showing how a model designed with no ontological foundation can produce misleading interpretations of inferred observation results. Our solution based on a BTL2 conformant formal interpretation of SNOMED CT concepts allows representing lab test without creating unintended models. We argue in favour of an ontologically explicit bridge between compositional clinical terminologies, in order to safely use their formal representations in intelligent systems.
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Ontological interpretation of biomedical database content
BMC, 2017Co-Authors: Filipe Santana Da Silva, Ludger Jansen, Fred Freitas, Stefan SchulzAbstract:Abstract Background Biological databases store data about laboratory experiments, together with semantic annotations, in order to support data aggregation and retrieval. The exact meaning of such annotations in the context of a database record is often ambiguous. We address this problem by grounding implicit and explicit database content in a formal-ontological framework. Methods By using a typical extract from the databases UniProt and Ensembl, annotated with content from GO, PR, ChEBI and NCBI Taxonomy, we created four ontological models (in OWL), which generate explicit, distinct interpretations under the BioTopLite2 (BTL2) upper-Level Ontology. The first three models interpret database entries as individuals (IND), defined classes (SUBC), and classes with dispositions (DISP), respectively; the fourth model (HYBR) is a combination of SUBC and DISP. For the evaluation of these four models, we consider (i) database content retrieval, using ontologies as query vocabulary; (ii) information completeness; and, (iii) DL complexity and decidability. The models were tested under these criteria against four competency questions (CQs). Results IND does not raise any ontological claim, besides asserting the existence of sample individuals and relations among them. Modelling patterns have to be created for each type of annotation referent. SUBC is interpreted regarding maximally fine-grained defined subclasses under the classes referred to by the data. DISP attempts to extract truly ontological statements from the database records, claiming the existence of dispositions. HYBR is a hybrid of SUBC and DISP and is more parsimonious regarding expressiveness and query answering complexity. For each of the four models, the four CQs were submitted as DL queries. This shows the ability to retrieve individuals with IND, and classes in SUBC and HYBR. DISP does not retrieve anything because the axioms with disposition are embedded in General Class Inclusion (GCI) statements. Conclusion Ambiguity of biological database content is addressed by a method that identifies implicit knowledge behind semantic annotations in biological databases and grounds it in an expressive upper-Level Ontology. The result is a seamless representation of database structure, content and annotations as OWL models
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[09:53 15/5/2009 Bioinformatics-btp194.tex] Page: i69 i69–i76 BIOINFORMATICS Vol. 25 ISMB 2009, pages i69–i76doi:10.1093/bioinformatics/btp194 Alignment of the UMLS semantic network with BioTop: methodology and assessment
2015Co-Authors: Stefan Schulz, Elena Beisswanger, László Van Den Hoek, Olivier Bodenreider, Erik M. Van MulligenAbstract:Motivation: For many years, the Unified Medical Language System (UMLS) semantic network (SN) has been used as an upper-Level semantic framework for the categorization of terms from terminological resources in biomedicine. BioTop has recently been developed as an upper-Level Ontology for the biomedical domain. In contrast to the SN, it is founded upon strict ontological principles, using OWL DL as a formal representation language, which has become standard in the semantic Web. In order to make logic-based reasoning available for the resources annotated or categorized with the SN, a mapping Ontology was developed aligning the SN with BioTop. Methods: The theoretical foundations and the practical realization of the alignment are being described, with a focus on the design decisions taken, the problems encountered and the adaptations of BioTop that became necessary. For evaluation purposes, UMLS concept pairs obtained from MEDLINE abstracts by a named entity recognition system were tested for possible semantic relationships. Furthermore, all semantic-type combinations that occur in the UMLS Metathesaurus were checked for satisfiability. Results: The effort-intensive alignment process required major design changes and enhancements of BioTop and brought up several design errors that could be fixed. A comparison between a human curator and the Ontology yielded only a low agreement. Ontology reasoning was also used to successfully identify 133 inconsistent semantic-type combinations
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Proposed actions are no actions: re-modeling an Ontology design pattern with a realist top-Level Ontology
Journal of Biomedical Semantics, 2012Co-Authors: Djamila Seddig-raufie, Ludger Jansen, Daniel Schober, Martin Boeker, Niels Grewe, Stefan SchulzAbstract:Background Ontology Design Patterns (ODPs) are representational artifacts devised to offer solutions for recurring Ontology design problems. They promise to enhance the Ontology building process in terms of flexibility, re-usability and expansion, and to make the result of Ontology engineering more predictable. In this paper, we analyze ODP repositories and investigate their relation with upper-Level ontologies. In particular, we compare the BioTop upper Ontology to the Action ODP from the NeOn an ODP repository. In view of the differences in the respective approaches, we investigate whether the Action ODP can be embedded into BioTop. We demonstrate that this requires re-interpreting the meaning of classes of the NeOn Action ODP in the light of the precepts of realist ontologies. Results As a result, the re-design required clarifying the ontological commitment of the ODP classes by assigning them to top-Level categories. Thus, ambiguous definitions are avoided. Classes of real entities are clearly distinguished from classes of information artifacts. The proposed approach avoids the commitment to the existence of unclear future entities which underlies the NeOn Action ODP. Our re-design is parsimonious in the sense that existing BioTop content proved to be largely sufficient to define the different types of actions and plans. Conclusions The proposed model demonstrates that an expressive upper-Level Ontology provides enough resources and expressivity to represent even complex ODPs, here shown with the different flavors of Action as proposed in the NeOn ODP. The advantage of ODP inclusion into a top-Level Ontology is the given predetermined dependency of each class, an existing backbone structure and well-defined relations. Our comparison shows that the use of some ODPs is more likely to cause problems for Ontology developers, rather than to guide them. Besides the structural properties, the explanation of classification results were particularly hard to grasp for 'self-sufficient' ODPs as compared with implemented and 'embedded' upper-Level structures which, for example in the case of BioTop, offer a detailed description of classes and relations in an axiomatic network. This ensures unambiguous interpretation and provides more concise constraints to leverage on in the Ontology engineering process.
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the Ontology of biological taxa
Intelligent Systems in Molecular Biology, 2008Co-Authors: Stefan Schulz, Holger Stenzhorn, Martin BoekerAbstract:Motivation: The classification of biological entities in terms of species and taxa is an important endeavor in biology. Although a large amount of statements encoded in current biomedical ontologies is taxon-dependent there is no obvious or standard way for introducing taxon information into an integrative Ontology architecture, supposedly because of ongoing controversies about the ontological nature of species and taxa. Results: In this article, we discuss different approaches on how to represent biological taxa using existing standards for biomedical ontologies such as the description logic OWL DL and the Open Biomedical Ontologies Relation Ontology. We demonstrate how hidden ambiguities of the species concept can be dealt with and existing controversies can be overcome. A novel approach is to envisage taxon information as qualities that inhere in biological organisms, organism parts and populations. Availability: The presented methodology has been implemented in the domain top-Level Ontology BioTop, openly accessible at http://purl.org/biotop. BioTop may help to improve the logical and ontological rigor of biomedical ontologies and further provides a clear architectural principle to deal with biological taxa information. Contact: stschulz@uni-freiburg.de
Giancarlo Guizzardi - One of the best experts on this subject based on the ideXlab platform.
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multi Level Ontology based conceptual modeling
Data and Knowledge Engineering, 2017Co-Authors: Victorio Albani De Carvalho, Joao Paulo A Almeida, Claudenir M Fonseca, Giancarlo GuizzardiAbstract:Abstract Since the late 1980s, there has been a growing interest in the use of foundational ontologies to provide a sound theoretical basis for the discipline of conceptual modeling. This has led to the development of Ontology-based conceptual modeling techniques whose modeling primitives reflect the conceptual categories defined in a foundational Ontology. The Ontology-based conceptual modeling language OntoUML, for example, incorporates the distinctions underlying the taxonomy of types in the Unified Foundational Ontology (UFO) (e.g., kinds, phases, roles, mixins, etc.). This approach has focused so far on the support to types whose instances are individuals in the subject domain, with no provision for types of types (or categories of categories). In this paper we address this limitation by extending the Unified Foundational Ontology with the MLT multi-Level theory. The UFO-MLT combination serves as a foundation for conceptual models that can benefit from the ontological distinctions of UFO as well as MLT's basic concepts and patterns for multi-Level modeling. We discuss the impact of the extended foundation to multi-Level conceptual modeling.
Atanas Kiryakov - One of the best experts on this subject based on the ideXlab platform.
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mapping the central lod ontologies to proton upper Level Ontology
International Conference on Ontology Matching, 2010Co-Authors: Mariana Damova, Atanas Kiryakov, Kiril Simov, Svetoslav PetrovAbstract:Linking Open Data (LOD) facilitates the emergence of a web of linked data by publishing and interlinking open data on the web in RDF. One can explore linked data across servers by following the links in the graph. The LOD cloud has 203 datasets and more than 14 billion RDF triples (http://lod-cloud.net). This paper describes an approach to access these data by means of a single Ontology, matched to the schemata describing several of the most common LOD datasets. They are presented in a reason-able view - FactForge (http://factforge.net) - the biggest and most heterogeneous body of factual knowledge on which inference is performed. Techniques of (a) making matching rules with "Ontology expressions", (b) adding new instances with inference rules, and (c) extending the upper Level Ontology with classes and properties are employed. They succeed to align ontologies designed according to different principles and displaying conceptual and structural mismatches.
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opjk into proton legal domain Ontology integration into an upper Level Ontology
International Conference on Move to Meaningful Internet Systems, 2005Co-Authors: Nuria Casellas, Atanas Kiryakov, Mercedes Blazquez, Pompeu Casanovas, Marta Poblet, Richard BenjaminsAbstract:The SEKT Project aims at developing and exploiting the knowledge technologies which underlie the Next Generation Knowledge Management, connecting complementary know-how of key European centers in three areas: Ontology Management Technology, Knowledge Discovery and Human Language Technology. This paper describes the development of PROTON, an upper-Level Ontology developed by Ontotext, and of the Ontology of Professional Judicial Knowledge (OPJK), modeled by a team of legal experts form the Institute of Law and Technology (IDT-UAB) for the Iuriservice prototype (a webbased intelligent FAQ for the Spanish judges on their first appointment designed by iSOCO). The paper focuses on the work done towards the integration of the OPJK built using a middle-out strategy into the system and top modules of PROTON, illustrating the flexibility of this independent upper-Level Ontology.
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d1 8 1 base upper Level Ontology bulo guidance 1
2005Co-Authors: Ivan Terziev, Atanas Kiryakov, Dimitar ManovAbstract:An important practical approach to Ontology generation is the use of background or pre-existing knowledge in the form of a basic upper-Level Ontology. Such an Ontology can also be used for metadata generation and as a groundwork for the overall knowledge modelling and integration strategy of a KM environment. The essential contribution of this deliverable is a basic upper-Level Ontology called PROTON (PROTo Ontology), which is hereby introduced and documented. It contains about 300 classes and 100 properties, providing coverage of the general concepts necessary for a wide range of tasks, including semantic annotation, indexing, and retrieval of documents. The design principles can be summarized as follows (i) domain-independence; (ii) light-weight logical definitions; (iii) alignment with popular standards; (iv) good coverage of named entities and concrete domains (i.e. people, organizations, locations, numbers, dates, addresses). The Ontology is originally encoded in a fragment of OWL Lite and split into four modules: System, Top, Upper, and KM (Knowledge Management).
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kim a semantic platform for information extraction and retrieval
Natural Language Engineering, 2004Co-Authors: Borislav Popov, Atanas Kiryakov, Damyan Ognyanoff, Dimitar Manov, Angel KirilovAbstract:The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-Level Ontology. Based on the Ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, Ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.
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semantic annotation indexing and retrieval
International Semantic Web Conference, 2003Co-Authors: Atanas Kiryakov, Borislav Popov, Damyan Ognyanoff, Dimitar Manov, Angel Kirilov, Miroslav GoranovAbstract:The Semantic Web realization depends on the availability of critical mass of metadata for the web content, linked to formal knowledge about the world. This paper presents our vision about a holistic system allowing annotation, indexing, and retrieval of documents with respect to real-world entities. A system (called KIM), partially implementing this concept is shortly presented and used for evaluation and demonstration. Our understanding is that a system for semantic annotation should be based upon specific knowledge about the world, rather than indifferent to any ontological commitments and general knowledge. To assure efficiency and reusability of the metadata we introduce a simplistic upper-Level Ontology which starts with some basic philosophic distinctions and goes down to the most popular entity types (people, companies, cities, etc.), thus providing many of the inter-domain common sense concepts and allowing easy domain-specific extensions. Based on the Ontology, an extensive knowledge base of entities descriptions is maintained. Semantically enhanced information extraction system providing automatic annotation with references to classes in the Ontology and instances in the knowledge base is presented. Based on these annotations, we perform IR-like indexing and retrieval, further extended using the Ontology and knowledge about the specific entities.
Dimitar Manov - One of the best experts on this subject based on the ideXlab platform.
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d1 8 1 base upper Level Ontology bulo guidance 1
2005Co-Authors: Ivan Terziev, Atanas Kiryakov, Dimitar ManovAbstract:An important practical approach to Ontology generation is the use of background or pre-existing knowledge in the form of a basic upper-Level Ontology. Such an Ontology can also be used for metadata generation and as a groundwork for the overall knowledge modelling and integration strategy of a KM environment. The essential contribution of this deliverable is a basic upper-Level Ontology called PROTON (PROTo Ontology), which is hereby introduced and documented. It contains about 300 classes and 100 properties, providing coverage of the general concepts necessary for a wide range of tasks, including semantic annotation, indexing, and retrieval of documents. The design principles can be summarized as follows (i) domain-independence; (ii) light-weight logical definitions; (iii) alignment with popular standards; (iv) good coverage of named entities and concrete domains (i.e. people, organizations, locations, numbers, dates, addresses). The Ontology is originally encoded in a fragment of OWL Lite and split into four modules: System, Top, Upper, and KM (Knowledge Management).
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kim a semantic platform for information extraction and retrieval
Natural Language Engineering, 2004Co-Authors: Borislav Popov, Atanas Kiryakov, Damyan Ognyanoff, Dimitar Manov, Angel KirilovAbstract:The KIM platform provides a novel Knowledge and Information Management framework and services for automatic semantic annotation, indexing, and retrieval of documents. It provides a mature and semantically enabled infrastructure for scalable and customizable information extraction (IE) as Our understanding is that a system for semantic annotation should be based upon a simple model of real-world entity concepts, complemented with quasi-exhaustive instance knowledge. To ensure efficiency, easy sharing, and reusability of the metadata we introduce an upper-Level Ontology. Based on the Ontology, a large-scale instance base of entity descriptions is maintained. The knowledge resources involved are handled by use of state-of-the-art Semantic Web technology and standards, including RDF(S) repositories, Ontology middleware and reasoning. From a technical point of view, the platform allows KIM-based applications to use it for automatic semantic annotation, for content retrieval based on semantic queries, and for semantic repository access. As a framework, KIM also allows various IE modules, semantic repositories and information retrieval engines to be plugged into it. This paper presents the KIM platform, with an emphasis on its architecture, interfaces, front-ends, and other technical issues.
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semantic annotation indexing and retrieval
International Semantic Web Conference, 2003Co-Authors: Atanas Kiryakov, Borislav Popov, Damyan Ognyanoff, Dimitar Manov, Angel Kirilov, Miroslav GoranovAbstract:The Semantic Web realization depends on the availability of critical mass of metadata for the web content, linked to formal knowledge about the world. This paper presents our vision about a holistic system allowing annotation, indexing, and retrieval of documents with respect to real-world entities. A system (called KIM), partially implementing this concept is shortly presented and used for evaluation and demonstration. Our understanding is that a system for semantic annotation should be based upon specific knowledge about the world, rather than indifferent to any ontological commitments and general knowledge. To assure efficiency and reusability of the metadata we introduce a simplistic upper-Level Ontology which starts with some basic philosophic distinctions and goes down to the most popular entity types (people, companies, cities, etc.), thus providing many of the inter-domain common sense concepts and allowing easy domain-specific extensions. Based on the Ontology, an extensive knowledge base of entities descriptions is maintained. Semantically enhanced information extraction system providing automatic annotation with references to classes in the Ontology and instances in the knowledge base is presented. Based on these annotations, we perform IR-like indexing and retrieval, further extended using the Ontology and knowledge about the specific entities.
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experiments with geographic knowledge for information extraction
North American Chapter of the Association for Computational Linguistics, 2003Co-Authors: Dimitar Manov, Atanas Kiryakov, Borislav Popov, Kalina Bontcheva, Diana Maynard, Hamish CunninghamAbstract:Here we present work on using spatial knowledge in conjunction with information extraction (IE). Considerable volume of location data was imported in a knowledge base (KB) with entities of general importance used for semantic annotation, indexing, and retrieval of text. The Semantic Web knowledge representation standards are used, namely RDF(S). An extensive upper-Level Ontology with more than two hundred classes is designed. With respect to the locations, the goal was to include the most important categories considering public and tasks not specially related to geography or related areas. The locations data is derived from number of publicly available resources and combined to assure best performance for domain-independent named-entity recognition in text. An evaluation and comparison to high performance IE application is given.
Victorio Albani De Carvalho - One of the best experts on this subject based on the ideXlab platform.
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multi Level Ontology based conceptual modeling
Data and Knowledge Engineering, 2017Co-Authors: Victorio Albani De Carvalho, Joao Paulo A Almeida, Claudenir M Fonseca, Giancarlo GuizzardiAbstract:Abstract Since the late 1980s, there has been a growing interest in the use of foundational ontologies to provide a sound theoretical basis for the discipline of conceptual modeling. This has led to the development of Ontology-based conceptual modeling techniques whose modeling primitives reflect the conceptual categories defined in a foundational Ontology. The Ontology-based conceptual modeling language OntoUML, for example, incorporates the distinctions underlying the taxonomy of types in the Unified Foundational Ontology (UFO) (e.g., kinds, phases, roles, mixins, etc.). This approach has focused so far on the support to types whose instances are individuals in the subject domain, with no provision for types of types (or categories of categories). In this paper we address this limitation by extending the Unified Foundational Ontology with the MLT multi-Level theory. The UFO-MLT combination serves as a foundation for conceptual models that can benefit from the ontological distinctions of UFO as well as MLT's basic concepts and patterns for multi-Level modeling. We discuss the impact of the extended foundation to multi-Level conceptual modeling.