What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- The Internet
- Software
His main research concerns Ontology, Information retrieval, World Wide Web, Ontology and Semantic Web.
Alexander Maedche usually deals with Ontology and limits it to topics linked to Knowledge management and Conceptualization.
His work deals with themes such as Artificial intelligence and Fuzzy clustering, which intersect with Information retrieval.
His Ontology research is multidisciplinary, relying on both Knowledge sharing and Knowledge engineering.
As a part of the same scientific family, Alexander Maedche mostly works in the field of Semantic Web, focusing on Meta Data Services and, on occasion, RDF.
His study in Ontology-based data integration is interdisciplinary in nature, drawing from both Upper ontology and Ontology Inference Layer.
His most cited work include:
- Ontology Learning for the Semantic Web (1628 citations)
- Measuring Similarity between Ontologies (696 citations)
- FCA-MERGE: bottom-up merging of ontologies (552 citations)
What are the main themes of his work throughout his whole career to date?
His primary areas of study are Knowledge management, Ontology, World Wide Web, Process and Information retrieval.
Ontology is closely attributed to Ontology in his research.
His Process research is multidisciplinary, incorporating elements of Data science, Artificial intelligence and Process management.
Alexander Maedche studies Information retrieval, focusing on OWL-S in particular.
Alexander Maedche works mostly in the field of Web standards, limiting it down to topics relating to Web modeling and, in certain cases, Web development.
He has researched Social Semantic Web in several fields, including Web intelligence and Data Web.
He most often published in these fields:
- Knowledge management (24.06%)
- Ontology (18.12%)
- World Wide Web (16.25%)
What were the highlights of his more recent work (between 2018-2021)?
- Process (15.62%)
- Design science research (8.44%)
- Chatbot (4.38%)
In recent papers he was focusing on the following fields of study:
His primary scientific interests are in Process, Design science research, Chatbot, Human–computer interaction and Artificial intelligence.
His Process research incorporates themes from Domain, Ambidexterity, Software development and Process management.
The various areas that he examines in his Design science research study include Engineering ethics, Key, Design knowledge and Data science.
His studies in Chatbot integrate themes in fields like Descriptive knowledge, Knowledge management, Social cue and Customer service.
His Descriptive knowledge research integrates issues from Ontology, Focus group, Leverage and Representation.
His Artificial intelligence research incorporates elements of Machine learning and Alpha.
Between 2018 and 2021, his most popular works were:
- A Taxonomy of Social Cues for Conversational Agents (48 citations)
- How effective is nudging? A quantitative review on the effect sizes and limits of empirical nudging studies (42 citations)
- AI-Based Digital Assistants – Opportunities, Threats, and Research Perspectives (36 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- The Internet
- Software
Alexander Maedche mainly focuses on Design science research, Chatbot, Design knowledge, Human–computer interaction and Engineering ethics.
His research in Design science research intersects with topics in Field, Systems engineering and Data science.
Alexander Maedche has included themes like Ontology, Executable and Knowledge base in his Field study.
His Chatbot study combines topics in areas such as Descriptive knowledge, Social cue, Requirements engineering, Process and Laddering.
The concepts of his Design knowledge study are interwoven with issues in Common ground, Interactivity and Management science.
His study in Human–computer interaction is interdisciplinary in nature, drawing from both Functional design, Requirements elicitation and User requirements document.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
