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Showing papers on "Upper ontology published in 2020"


Book
30 Sep 2020
TL;DR: This book discusses Ontologies and Applications of Ontologies in Biomedicine, a meta- Ontology for Data Organization, Integration, and Searching Computer Reasoning with Ontologies, and other topics.
Abstract: BASIC CONCEPTS Ontologies and Applications of Ontologies in Biomedicine What Is an Ontology? Ontologies and Bio-Ontologies Ontologies for Data Organization, Integration, and Searching Computer Reasoning with Ontologies Typical Applications of Bio-Ontologies Mathematical Logic and Inference Representation and Logic Propositional Logic First-Order Logic Sets Description Logic Probability Theory and Statistics for Bio-Ontologies Probability Theory Bayes' Theorem Introduction to Graphs Bayesian Networks Ontology Languages OBO RDF and RDFS OWL and the Semantic Web BIO-ONTOLOGIES The Gene Ontology A Tool for the Unification of Biology Three Subontologies Relations in GO GO Annotations GO Slims Upper-Level Ontologies Basic Formal Ontology The Big Divide: Continuants and Occurrents Universals and Particulars Relation Ontology Revisiting Gene Ontology Revisiting GO Annotations A Selective Survey of Bio-Ontologies OBO Foundry The National Center for Biomedical Ontology Bio-Ontologies What Makes a Good Ontology? GRAPH ALGORITHMS FOR BIO-ONTOLOGIES Overrepresentation Analysis Definitions Term-for-Term Multiple Testing Problem Term-for-Term Analysis: An Extended Example Inferred Annotations Lead to Statistical Dependencies in Ontology DAGs Parent-Child Algorithms Parent-Child Analysis: An Extended Example Topology-Based Algorithms Topology-elim: An Extended Example Other Approaches Summary Model-Based Approaches to GO Analysis A Probabilistic Generative Model for GO Enrichment Analysis A Bayesian Network Model MGSA: An Extended Example Summary Semantic Similarity Information Content in Ontologies Semantic Similarity of Genes and Other Items Annotated by Ontology Terms Statistical Significance of Semantic Similarity Scores Frequency-Aware Bayesian Network Searches in Attribute Ontologies Modeling Queries Probabilistic Inference for the Items Parameter-Augmented Network The Frequency-Aware Network Benchmark INFERENCE IN ONTOLOGIES Inference in the Gene Ontology Inference over GO Edges Cross-Products and Logical Definitions RDFS Semantics and Inference Definitions Interpretations RDF Entailment RDFS Entailment Entailment Rules Summary Inference in OWL Ontologies The Semantics of Equality The Semantics of Properties The Semantics of Classes The Semantics of the Schema Vocabulary Conclusions Algorithmic Foundations of Computational Inference The Tableau Algorithm Developer Libraries SPARQL SPARQL Queries Combining RDF Graphs Conclusions Appendix A: An Overview of R Appendix B: Information Content and Entropy Appendix C: W3C Standards: XML, URIs, and RDF Appendix D: W3C Standards: OWL Bibliography Index Exercises and Further Reading appear at the end of each chapter.

80 citations


Journal ArticleDOI
TL;DR: Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models and corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.
Abstract: Endowing ubiquitous robots with cognitive capabilities for recognizing emotions, sentiments, affects, and moods of humans in their context is an important challenge, which requires sophisticated and novel approaches of emotion recognition. Most studies explore data-driven pattern recognition techniques that are generally highly dependent on learning data and insufficiently effective for emotion contextual recognition. In this article, a hybrid model-based emotion contextual recognition approach for cognitive assistance services in ubiquitous environments is proposed. This model is based on: 1) a hybrid-level fusion exploiting a multilayer perceptron (MLP) neural-network model and the possibilistic logic and 2) an expressive emotional knowledge representation and reasoning model to recognize nondirectly observable emotions; this model exploits jointly the emotion upper ontology (EmUO) and the n-ary ontology of events HTemp supported by the NKRL language. For validation purposes of the proposed approach, experiments were carried out using a YouTube dataset, and in a real-world scenario dedicated to the cognitive assistance of visitors in a smart devices showroom. Results demonstrated that the proposed multimodal emotion recognition model outperforms all baseline models. The real-world scenario corroborates the effectiveness of the proposed approach in terms of emotion contextual recognition and management and in the creation of emotion-based assistance services.

9 citations


Book ChapterDOI
26 Jul 2020
TL;DR: The first release of OntoMath is presented, a new educational mathematical ontology that respects ontological distinctions provided by a foundational ontology; represents mathematical relationships as first-oder entities; and provides strong linguistic grounding for the represented mathematical concepts.
Abstract: We present the first release of OntoMath\({}^{Edu}\), a new educational mathematical ontology. The ontology is intended to be used as a Linked Open Data hub for mathematical education, a linguistic resource for intelligent mathematical language processing and an end-user reference educational database. The ontology is organized in three layers: a foundational ontology layer, a domain ontology layer and a linguistic layer. The domain ontology layer contains language-independent concepts, covering secondary school mathematics curriculum. The linguistic layer provides linguistic grounding for these concepts, and the foundation ontology layer provides them with meta-ontological an-notations. The concepts are organized in two main hierarchies: the hierarchy of objects and the hierarchy of reified relationships. For our knowledge, OntoMath\({}^{Edu}\) is the first Linked Open Data mathematical ontology, that respects ontological distinctions provided by a foundational ontology; represents mathematical relationships as first-oder entities; and provides strong linguistic grounding for the represented mathematical concepts.

8 citations


01 Jan 2020
TL;DR: CoModIDE is provided, an extension to the Protégé-based modular ontology engineering tool CoModIDE, to make it possible for ontology engineers to adhere to traditional ontology modeling processes based on upper or foundational ontologies.
Abstract: We provide an extension to the Protégé-based modular ontology engineering tool CoModIDE, in order to make it possible for ontology engineers to adhere to traditional ontology modeling processes based on upper or foundational ontologies. As a bridge between the more recently proposed modular ontology modeling approach and more classical ones based on foundational ontologies, it enables a best-of-both worlds approach for ontology engineering.

3 citations


Book ChapterDOI
01 Jan 2020
TL;DR: In this paper, the authors propose an architectural structure leading towards a framework to resolve most of the above-listed technical barriers and open doors to wider audiences in experiencing the benefits of the semantic web.
Abstract: With the current trends and developments in the information technology domain, there is a high enthusiasm for using semantic Web technologies and decision analysis mechanisms to solve numerous recurring issues in societies. There are plenty of existing knowledge models available on the Internet, developed for solving various problems. But, the reusability aspects of those are almost very low, due to main barriers, such as complexities associated with schema understanding, technical barriers associated with querying and comprehension of semantic representations. This will hinder the reusability of existing knowledge models and also knowledge dissemination associated with new and existing knowledge models. These consequences are obstructing the opportunities of experiencing the advancements of semantic technologies to both technical and non-technical audiences. This research is focusing on proposing an architectural structure leading towards a framework, to resolve most of the above-listed technical barriers and open doors to wider audiences in experiencing the benefits of the semantic Web. The proposed architectural structure is a combination of an instructional upper ontology and multiples of decision support systems integrated to the endpoints of the upper ontology. Crime domain is selected for the proposal of the high-level architectural design, leading towards a framework, as crime escalation has been a crucial concern which needs timely attention to under control the further spread.

1 citations