A5062807811- Semantic Web and Ontologies
- Service-Oriented Architecture and Web Services
- Advanced Database Systems and Queries
- Natural Language Processing Techniques
- Business Process Modeling and Analysis
- Biomedical Text Mining and Ontologies
- Topic Modeling
- Gaze Tracking and Assistive Technology
- Peer-to-Peer Network Technologies
- Web Data Mining and Analysis
- Scientific Computing and Data Management
- Advanced Graph Neural Networks
- Data Management and Algorithms
- Data Quality and Management
- Complex Network Analysis Techniques
- Image Retrieval and Classification Techniques
- Logic, Reasoning, and Knowledge
- Advanced Text Analysis Techniques
- Video Analysis and Summarization
- Opinion Dynamics and Social Influence
- Recommender Systems and Techniques
- Model-Driven Software Engineering Techniques
- Ethics and Social Impacts of AI
- Graph Theory and Algorithms
- Rough Sets and Fuzzy Logic
University of Stuttgart
2020-2025
University of Southampton
2016-2025
University of Koblenz and Landau
2012-2021
Universität Koblenz
2012-2021
Koblenz University of Applied Sciences
2005-2020
L3S Research Center
2020
Leibniz University Hannover
2020
Landau Institute for Theoretical Physics
2004-2018
GESIS - Leibniz-Institute for the Social Sciences
2015
Karlsruhe University of Education
1999-2011
The Semantic Web relies heavily on formal ontologies to structure data for comprehensive and transportable machine understanding. Thus, the proliferation of factors largely in Web's success. authors present an ontology learning framework that extends typical engineering environments by using semiautomatic construction tools. encompasses import, extraction, pruning, refinement evaluation.
In this article, we present an approach for ontology-based knowledge management (KM) that includes a tool suite and methodology developing KM systems. It builds on the distinction between processes metaprocesses, is illustrated by CHAR (Corporate History AnalyzeR), system corporate history analysis.
Abstract Artificial Intelligence (AI)‐based systems are widely employed nowadays to make decisions that have far‐reaching impact on individuals and society. Their might affect everyone, everywhere, anytime, entailing concerns about potential human rights issues. Therefore, it is necessary move beyond traditional AI algorithms optimized for predictive performance embed ethical legal principles in their design, training, deployment ensure social good while still benefiting from the huge of...
We present a novel approach to the automatic acquisition of taxonomies or concept hierarchies from text corpus. The is based on Formal Concept Analysis (FCA), method mainly used for analysis data, i.e. investigating and processing explicitly given information. follow Harris distributional hypothesis model context certain term as vector representing syntactic dependencies which are automatically acquired corpus with linguistic parser. On basis this information, FCA produces lattice that we...
The success of the Semantic Web depends on availability ontologies as well proliferation web pages annotated with metadata conforming to these ontologies. Thus, a crucial question is where acquire from. In this paper wepropose PANKOW (Pattern-based Annotation through Knowledge theWeb), method which employs an unsupervised, pattern-based approach categorize instances regard ontology. evaluated against manual annotations two human subjects. implemented in OntoMat, annotation tool for and shows...
Social networks have interesting properties. They influence our lives enormously without us being aware of the implications they raise. The authors investigate following areas concerning social networks: how to exploit unprecedented wealth data and we can mine for purposes such as marketing campaigns; a particular form influence, i.e.., way that people agree on terminology this phenomenon's build ontologies Semantic Web; something discover from data; use network information offer new...
Text document clustering plays an important role in providing intuitive navigation and browsing mechanisms by organizing large sets of documents into a small number meaningful clusters. The bag words representation used for these methods is often unsatisfactory as it ignores relationships between terms that do not cooccur literally. In order to deal with the problem, we integrate core ontologies background knowledge process text documents. Our experimental evaluations compare techniques...