## 2 State-of-the-Art
### Matchmaking Solutions Using AI  
The application of artificial intelligence techniques to matchmaking systems has gathered significant attention in recent years. This section reviews key contributions in this domain, highlighting their methodologies, innovations, and limitations. [PERSON] et al. [13] introduce a method for matching experts to projects by employing domain ontologies to model expertise and project requirements. Using Protege [14], the authors formalize concepts into structured trees, facilitating the computation of semantic similarities. While this approach effectively organizes knowledge, it is susceptible to semantic heterogeneity, where different terms refer to the same concept, resulting in potential mismatches. Although partially mitigated, this limitation remains unresolved, affecting the method's overall reliability. In ref. [15], the authors propose a system for automating the pairing of researcher biosketches with funders' requests for proposals (RFPs) using advanced natural language processing techniques. The authors develop four deep neural network architectures based on a fine-tuned BERT model, comparing their performance with support vector machines and logistic regression as baselines. The DNNs utilize cross-encoding and Siamese encoding strategies, with CNNs or Bi-LSTMs as post-BERT layers. Among these, the cross-encoder BERT model with a Bi-LSTM layer and BC2 BT-based data augmentation achieves the highest accuracy. This work demonstrates the potential of sophisticated NLP methods for automating complex matchmaking tasks, emphasizing the benefits of effective data augmentation techniques. A matchmaking system for linking resumes with job descriptions to identify suitable candidates is presented in [16]. The approach integrates two models: a content-based recommender system and an NLP-based method using gensim for text summarization. The gensim model employs the TextRank algorithm and transformers to summarize resumes and job descriptions into comparable lengths, followed by k-nearest neighbors for similarity measurement. The TextRank algorithm focuses on keyword extraction, while transformer-based models paraphrase and abstract text. These complementary approaches enhance flexibility but underscore the challenge of balancing keyword-based and semantic representations in matchmaking. In [17], the authors introduce an AI-powered platform to connect industry experts with companies seeking specialized skills. The system relies on the Word2 Vec model with a Skip-Gram architecture to calculate semantic similarity between keywords provided by mentors and companies. By analyzing these keywords, the platform identifies and recommends experts whose skills align closely with organizational needs. However, the dependency on keyword-based inputs poses a limitation, requiring precise articulation of expertise and requirements to achieve effective matches. The reviewed studies collectively underscore the diverse methodologies applied to matchmaking, ranging from ontology-based approaches to deep learning and NLP techniques. While ontology-based methods excel in knowledge organization, they are prone to semantic mismatches. In contrast, deep learning and transformer-based models offer advanced semantic understanding but may require extensive computational resourcesand well-curated training data. These works pave the way for further research to address limitations such as semantic heterogeneity and reliance on structured input formats. Storing Information/Documents with Managed Access and Methods Employing Decentralized Storage and Blockchain Technology  
Controlling access to data in decentralized computing systems represents a manifold challenge in itself. Today, managing different user roles and access rights is complicated. This complexity arises from the variety of available sources for user authentication and validation. The second difficult task is handling the increasing mixture of data formats, representations and storage options that are available. [PERSON] et al.