A5034642813- Semantic Web and Ontologies
- Data Quality and Management
- Metaheuristic Optimization Algorithms Research
- Evolutionary Algorithms and Applications
- Advanced Graph Neural Networks
- Topic Modeling
- Natural Language Processing Techniques
- Scientific Computing and Data Management
- Service-Oriented Architecture and Web Services
- Advanced Database Systems and Queries
- Advanced Multi-Objective Optimization Algorithms
- Graph Theory and Algorithms
- Anomaly Detection Techniques and Applications
- E-Government and Public Services
- Big Data and Business Intelligence
- Biomedical Text Mining and Ontologies
- Big Data Technologies and Applications
- Bioinformatics and Genomic Networks
- Network Security and Intrusion Detection
- Machine Learning and Data Classification
- Bayesian Modeling and Causal Inference
- Cognitive Computing and Networks
- Data Mining Algorithms and Applications
- Evolution and Genetic Dynamics
- Research Data Management Practices
GESIS - Leibniz-Institute for the Social Sciences
2022-2024
University of Cologne
2020-2021
Cluster of Excellence on Plant Sciences
2020-2021
University of Bonn
2017-2020
Fraunhofer Institute for Intelligent Analysis and Information Systems
2017-2019
Iqra University
2010-2017
Leipzig University
2016
Abasyn University
2013
National University of Computer and Emerging Sciences
2009-2010
Since its inception, the Internet of Things (IoT) has witnessed mushroom growth as a breakthrough technology. In nutshell, IoT is integration devices and data such that processes are automated centralized to certain extent. revolutionizing way business done transforming society whole. As this technology advances further, need exploit detection weakness awareness increases prevent unauthorized access critical resources functions, thereby rendering system unavailable. Denial Service (DoS)...
Large Language Models (LLMs) have taken Knowledge Representation -- and the world by storm. This inflection point marks a shift from explicit knowledge representation to renewed focus on hybrid of both parametric knowledge. In this position paper, we will discuss some common debate points within community LLMs (parametric knowledge) Graphs (explicit speculate opportunities visions that brings, as well related research topics challenges.
Particle Swarm Optimization, a population based optimization technique has been used in wide number of application areas to solve problems. This paper presents new algorithm for initialization standard PSO called Opposition Optimization (O-PSO). The performance proposed is compared with the existing variants on several benchmark functions and experimental results reveal that O-PSO outperforms approaches large extent.
Security threat from senseless terrorist attacks on unarmed civilians is a major concern in today's society. The recent developments data technology allow us to have scalable and flexible capture, storage, processing analytics. We can utilize these capabilities help dealing with our security related problems. This paper gives new meaning behavioral analytics introduces opportunity for typical university setting using that already present being utilized environment. propose the basics of...
Abstract Summary Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability predict links and create dense representations for graphs’ nodes edges. However, the software ecosystem application bioinformatics remains limited inaccessible users without expertise programing machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) PyKEEN (Python facilitate easy use through an interactive command line interface. Finally,...
The amount of multilingual data on the Web proliferates; therefore, developing ontologies in various natural languages is attracting considerable attention. In order to achieve semantic interoperability for Web, cross-lingual ontology matching techniques are highly required. This paper proposes a Multilingual Ontology Matching (MoMatch) approach different languages. MoMatch uses machine translation and string similarity identify correspondences across ontologies. Furthermore, we propose...
The recent achievements and availability of Large Language Models have paved the road to a new range applications use-cases. Pre-trained language models are now being involved at-scale in many fields where they were until absent from. More specifically, progress made by causal generative has open door using them through textual instructions aka. prompts. Unfortunately, performances these prompts highly dependent on exact phrasing used therefore practitioners need adopt fail-retry strategies....
Copyright protection and authentication of digital content has become a major concern in the current era. Plain text is widely used means information exchange on Internet it essential to verify authenticity any form communication. There are very limited techniques available for plain watermarking, authentication, tamper detection. This paper presents novel zero-watermarking algorithm detection documents. The generates watermark based contents which can be extracted later using extraction...
The availability of structured data has increased significantly over the past decade and several approaches to learn from have been proposed. These logic-based, inductive learning methods are often conceptually similar, which would allow a comparison among them even if they stem fro m different research communities. However, so far no efforts were made define an environment for running tasks on variety tools, covering multiple knowledge representation languages. With SML-Bench, we propose...
Differential evolution (DE) is a powerful global optimization algorithm which has been studied intensively by many researchers in the recent years. A number of variants have established for that makes DE more applicable. However, most are suffering from problems convergence speed and local optima. novel tournament based parent selection variant proposed this research. The enhances searching capability improves algorithm. This paper also presents statistical comparison existing mutation...
SPARQL is a W3C standard for querying the data stored as Resource Description Framework (RDF). The queries are represented using triple-patterns, and process searches these patterns in given RDF. Most of existing evaluators provide centralized, DBMS inspired solutions consuming high resources offering limited flexibility. To deal with increasing size RDF data, it important to develop scalable efficient distributed query evaluation. In this paper, we present DISE - an open-source...
Large Language Models (LLMs), with their advanced architectures and training on massive language datasets, contain unexplored knowledge. One method to infer this knowledge is through the use of cloze-style prompts. Typically, these prompts are manually designed because phrasing impacts retrieval performance, even if LLM encodes desired information. In paper, we study impact prompt syntax capacity LLMs. We a template-based approach paraphrase simple into more complex grammatical structure....
Squerall is a tool that allows the querying of heterogeneous, large-scale data sources by leveraging state-of-the-art Big Data processing engines: Spark and Presto. Queries are posed on-demand against Lake, i.e., directly on original without requiring prior transformation. We showcase Squerall's ability to query five different sources, including inter alia popular Cassandra MongoDB. In particular, we demonstrate how it can jointly heterogeneous interested developers easily extend support...
Increasing data volumes have extensively increased application possibilities. However, accessing this in an ad hoc manner remains unsolved problem due to the diversity of management approaches, formats and storage frameworks, resulting need effectively access process distributed heterogeneous at scale. For years, Semantic Web techniques addressed integration challenges with practical knowledge representation models ontology-based mappings. Leveraging these techniques, we provide a solution...
Generative AI e.g. Large Language Models (LLMs) can be used to generate new recipes. However, LLMs struggle with more complex aspects like recipe semantics and process comprehension. Furthermore, have limited ability account for user preferences since they are based on statistical patterns. As a result, these recipes may invalid. Evolutionary algorithms inspired by the of natural selection optimization that use stochastic operators solutions. These large number solutions from set possible...
Particle Swarm Optimization (PSO) algorithm has shown good performance in many optimization problems, but PSO suffers from the problem of early convergence into a local minima. Introduction opposition based initialization and mutation operators have played an important role to overcome function optimization. In this study we reviewed different variants for Researchers proposed modifications prevent it getting stuck optima. At end, variant better conversion.