A5043338889- Semantic Web and Ontologies
- Rough Sets and Fuzzy Logic
- Logic, Reasoning, and Knowledge
- Advanced Graph Neural Networks
- Bayesian Modeling and Causal Inference
- Data Management and Algorithms
- Biomedical Text Mining and Ontologies
- Scientific Computing and Data Management
- Research Data Management Practices
- Machine Learning in Materials Science
- Machine Learning and Algorithms
- Topic Modeling
- Topological and Geometric Data Analysis
- Manufacturing Process and Optimization
- Logic, programming, and type systems
- Advanced Database Systems and Queries
- Constraint Satisfaction and Optimization
- Model-Driven Software Engineering Techniques
- Advanced Algebra and Logic
- Natural Language Processing Techniques
- Text and Document Classification Technologies
University of Lübeck
2020-2024
Free University of Bozen-Bolzano
2024
Smart materials react to physical fields (e.g., electric, magnetic, and thermal fields) can be used as sensors, actuators, generators due their bidirectional behavior. Easy multiscale access material data models enables efficient research development with regard the selection of appropriate optimization towards specific applications. However, different working principles, measurement analysis methods, well storage approaches lead heterogeneous partly inconsistent datasets. The ontology‐based...
This article describes advancements in the ongoing digital transformation materials science and engineering. It is driven by domain‐specific successes development of specialized data spaces. There an evident increasing need for standardization across various subdomains to support exchange entities. The MaterialDigital Initiative, funded German Federal Ministry Education Research, takes on a key role this context, fostering collaborative efforts establish unified space. implementation...
This paper presents an embedding of ontologies expressed in the ALC description logic into a real-valued vector space, comprising restricted existential and universal quantifiers, as well concept negation disjunction. Our main result states that ontology is satisfiable classical sense iff it by partial faithful geometric model based on cones. The line work to which we contribute aims integrate knowledge representation techniques machine learning. new cone-model proposed this gives rise conic...
This paper is concerned with knowledge graph embedding background knowledge, taking the formal perspective of logics. In embedding, knowledge— expressed as a set triples form (a R b) (“a R-related to b”)—is embedded into real-valued vector space. The helps exploiting geometrical regularities space in order tackle typical inductive tasks machine learning such link prediction. Recent approaches also consider incorporating which intended meanings symbols a, R, b are further constrained via...
Smart Materials (SMat) promise to open new opportunities in the area of Intelligent Environments (IE), whether as part dedicated smart devices or fabric constituting everyday appliances and building infrastructure. Through use ontologies both IE engineers IEs themselves can be aware of, predict, how novel configurable changing materials react under different conditions. In contrast conventional Objects, however, computational software/hardware-systems, lending object-oriented perspective...
Various types of semantic artifacts play a vital role in developing software systems, for example, information systems materials scientists that adhere to the findability, accessibility, interoperability, reusability principles digital assets. Among them, integrity constraints (ICs) are essential as they orthogonally add representation capabilities ontologies means enforce consistency and completeness given data. An IC language recommended by worldwide web consortium (W3C) use with linked...
Dielectric Elastomer (DE) transducers are characterized by their geometrical dimensions and in particular the properties of elastomer electrode materials. Therefore, addition to dimensions, it is advantageous consider optimization material fulfill transducer requirements, such as blocking force, free stroke, or response time. A big challenge describing DE materials deals with utilizing different but commonly used hyperelastic models parameters, which differ complexity corresponding model...
An agent in pursuit of a task repeatedly perceives its environment through sensors, updates state based on observations, and then decides which action to take, given the current environment. Observations have common that they are made at time point thus referred as temporal data. Usually, such data is provided stream if continuously receives data, or it historic stored in, for instance, database has access to. DBMSs especially designed process static (i.e. non-temporal data) declarative...
In applications that use knowledge representation (KR) techniques, in particular those combine data-driven and logic methods, the domain of objects is not an abstract unstructured domain, but it exhibits a dedicated, deep structure geometric objects. One example class convex sets used to model natural concepts conceptual spaces, which also links via optimization techniques machine learning. this paper we study logics for such structures. Using machinery lattice theory, describe extension...