Abstract: In the era of digital innovation, Artificial Intelligence (AI) has emerged as a pivotal catalyst, unlocking new avenues for the evolution of Web 3.0. Web 3.0 signifies the next phase of the internet, characterized by decentralized structures, peer-topeer networks, and cutting-edge technologies like blockchain and smart contracts. This research provides an in-depth exploration of the role played by AI in shaping Web 3.0, delving into both its opportunities and challenges. AI proves instrumental in processing and analysing vast datasets with increased efficiency, fostering intelligent decision-making and insightful outcomes. The paper extensively covers essential Web 3.0 concepts and technologies, encompassing the Semantic Web and ontologies, and underscores AI's transformative potential across diverse industries such as healthcare, finance, and education. An analysis of the challenges posed by AI in the Web 3.0 landscape, including issues of data privacy, bias, trust, and ethics, is presented. Furthermore, the research examines the broader societal implications of AI in Web 3.0. Conclusively, the paper outlines future directions and implications of AI within the Web 3.0 framework, proposing potential areas for subsequent research. By contributing to a comprehensive understanding of AI's impact on web development and its broader societal implications, this research aims to guide future endeavours in this dynamic field.
Open access
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Electronic commerce and finance are progressively supporting and including decentralized, shared and public ledgers such as the blockchain. This is reshaping traditional commercial activities by advancing them towards Decentralized Finance (DeFi) and Commerce 3.0, thereby supporting the latter’s potential to outpace the hurdles of central authority controllers and lawgivers. The quantity and entropy of the information that must be sought and managed to become active participants in such a relentlessly evolving scenario are increasing at a steady pace. For example, that information comprises asset or service description, general rules of the game, and specific technologies involved for decentralization. Moreover, the relevant information ought to be shared among innumerable and heterogeneous stakeholders, such as producers, buyers, digital identity providers, valuation services, and shipment services, to just name a few. A clear semantic representation of such a complex and multifaceted blockchain-based e-Commerce ecosystem would contribute dramatically to make it more usable, namely more automatically accessible to virtually anyone wanting to play the role of a stakeholder, thereby reducing programmers’ effort. However, we feel that reaching that goal still requires substantial effort in the tailoring of Semantic Web technologies, hence this article sets out on such a route and advances a stack of OWL 2 ontologies for the semantic description of decentralized e-commerce. The stack includes a number of relevant features, ranging from the applicable stakeholders through the supply chain of the offerings for an asset, up to the Ethereum blockchain, its tokens and smart contracts. Ontologies are defined by taking a behaviouristic approach to represent the various participants as agents in terms of their actions, inspired by the Theory of Agents and the related mentalistic notions. The stack is validated through appropriate metrics and SPARQL queries implementing suitable competency questions, then demonstrated through the representation of a real world use case, namely, the iExec marketplace.
Ryan Zarick, Bryan Pellegrino, Isaac Zhang, Thomas Kim · 5 authors
We present ColorFloat, a family of O(1) space complexity algorithms that solve the problem of attributing (coloring) fungible tokens to the entity that minted them (minter). Tagging fungible tokens with metadata is not a new problem and was first formalized in the Colored Coins protocol. In certain contexts, practical solutions to this challenge have been implemented and deployed such as NFT. We define the fungible token coloring problem, one specific aspect of the Colored Coins problem, to be the problem of retaining fungible characteristics of the underlying token while accurately tracking the attribution of fungible tokens to their respective minters. Fungible token coloring has a wide range of Web3 applications. One application which we highlight in this paper is the onchain yield-sharing collateral-based stablecoin.
Bernhard Fisseni, Deniz Sarikaya, Bernhard Schröder
Abstract We discuss conceptual change and progress within mathematics, in particular how tools, structural concepts and representations are transferred between fields that appear to be unconnected or remote from each other. The theoretical background is provided by the frame concept, which is used in linguistics, cognitive science and artificial intelligence to model how explicitly given information is combined with expectations deriving from background knowledge. In mathematical proofs, we distinguish two kinds of frames, namely structural frames and ontological frames. The interaction between both kinds of frames can drive mathematical interpretation. We first discuss two examples where structural frames (formulaic notation) drive ontological development (the discovery or exploration of mathematical objects). The development of Boole’s Boolean algebra may at first appear as a metaphorical treatment of the (then) new area of logic. In the analysis, we discuss how different (aspects of) certain algebraic frames change in the transfer, how arising difficulties are solved and overall argue that Boole uses the numerical algebra frame as a research template for the discovery of a system for calculations in logic. Following Ifrah, we analyse the discovery of zero as an extension to the number ontology as driven by the development of notation. Both structural and ontological frames are extended and simplified as notation progresses. Finally, we discuss two examples from infinite combinatorics, viz. topological graph theory, and one foundational issue. In both examples, the two simultaneous frames about one object are maintained independently. They motivate different research questions, but may also fruitfully interact: shifting between multiple synchronously maintained perspectives acts as a motor of innovation. The analysis shows how a frame-based approach allows to model how different perspectives drive mathematical innovation because they highlight different aspects, questions and heuristics.
Julião Braga, Francisco Regateiro, Itana Stiubiener, Juliana Cristina Braga
This work presents a manually built ontology to aggregate and knowledge systematization of Decentralized Autonomous Organizations (DAOs) obtained from web pages. An ontology is a formal description of knowledge as a set of concepts within a domain and the relationships between them, providing a common vocabulary for researchers to share information. Ontology construction from text involves analyzing collected text, identifying relevant terms and concepts, and representing the ontology using representation languages such as OWL, RDF, or RDFS. Manual ontology construction can be performed using applications such as Protege. This work describes the methodology used, how to use the ontology created through Protege using SPARQL, and presents future work proposals, including creating the same ontology using Deep Learning from Machine Learning techniques.
The adoption of the General Data Protection Regulation (GDPR) has resulted in a significant shift in how the data of European Union citizens is handled. A variety of data sharing challenges in scenarios such as smart cities have arisen, especially when attempting to semantically represent GDPR legal bases, such as consent, contracts and the data types and specific sources related to them. Most of the existing ontologies that model GDPR focus mainly on consent. In order to represent other GDPR bases, such as contracts, multiple ontologies need to be simultaneously reused and combined, which can result in inconsistent and conflicting knowledge representation. To address this challenge, we present the smashHitCore ontology. smashHitCore provides a unified and coherent model for both consent and contracts, as well as the sensor data and data processing associated with them. The ontology was developed in response to real-world sensor data sharing use cases in the insurance and smart city domains. The ontology has been successfully utilised to enable GDPR-complaint data sharing in a connected car for insurance use cases and in a city feedback system as part of a smart city use case.
Ioannis Kourouklides, Novak I. Zukowski, Kleitos Alexandrou
This whitepaper presents the vision, mission, and overarching role of the GUT-AI Foundation. The Foundation aims to promote the research, development and eventually the deployment of user-friendly, human-centred and developer-friendly Artificial Intelligence (AI) systems for the betterment of humanity through an Ecosystem of Concepts and Implementations (ECI). The Foundation recognizes the potential of AI to revolutionize numerous industries, ranging from Healthcare and Education to Financial Services and Self-Driving Cars. However, the development of such AI systems poses significant challenges and impediments, such as multiple single points of failure, lack of interoperability, and lack of user adoption. Therefore, this whitepaper proposes a multidimensional approach to promote a whole ecosystem that has the ability to overcome such challenges. Primarily, the Foundation will encourage research into AI systems that are accessible, intuitive, and ready-to-use. The research will focus on proposing AI system architectures that meet the needs of the users, while they address their pain points in order to enhance both the User Experience (UX) and Developer Experience (DX). Furthermore, the Foundation will focus on promoting the adoption of best practices and Optional Open Standards for AI development and deployment that is automated, cost-effective, and scalable. For instance, these best practices will include the use of modular architectures, microservices, and containerization. By adopting these practices, the Foundation aims to enable interoperability and reuse of AI components. In addition, the Foundation envisions creating a marketplace, which will enable buyers to discover and adopt AI Solutions that meet their needs and preferences. The marketplace will also provide sellers with opportunities to showcase their AI Solutions, reach out to potential buyers and receive feedback from them. Finally, the Foundation will leverage emerging technologies such as Blockchain and Decentralized Autonomous Organizations (DAOs) to enhance the transparency, security, and trustworthiness of AI systems, while incentivizing innovation and collaboration among humans.
This PhD project proposes the theoretical and technological foundations of an approach for decentralized processing of streaming knowledge graphs, where autonomous reasoners may combine individual and collective processing of continuous data. These decentralized stream processors shall be capable of sharing not only data stream knowledge, but also processing duties, using collaboration and negotiation protocols. Moreover, commonly agreed semantic vocabularies will be used to address the high dynamicity of reasoners' knowledge and goals. The approach proposed in this project goes beyond previous works on stream reasoning, enabling the self-organization and coordination among distributed stream reasoners, based on techniques and principles inspired by Multi-Agent systems. On the one hand, it adds the ability to explicate processing goals, capabilities and knowledge, while on the other it exploits potential ways of interconnecting them in ways that expand their combined capacity/efficacy for managing highly dynamic flows of streaming knowledge. Through this approach, efficient local stream processors can establish cooperative processing schemes, respecting data privacy restrictions and data locality requirements through the exchange of streaming Knowledge Graphs.
Fabian Kirstein, Anton Altenbernd, Sonja Schimmler, Manfred Hauswirth
The Data Catalogue Vocabulary (DCAT) standard is a popular RDF vocabulary for publishing metadata about data catalogs and a valuable foundation for creating Knowledge Graphs. It has widespread application in the (Linked) Open Data and scientific communities. However, DCAT does not specify a robust mechanism to create and maintain persistent identifiers for the datasets. It relies on Internationalized Resource Identifiers (IRIs), that are not necessarily unique, resolvable and persistent. This impedes findability, citation abilities, and traceability of derived and aggregated data artifacts. As a remedy, we propose a decentralized identifier registry where persistent identifiers are managed by a set of collaborative distributed nodes. Every node gives full access to all identifiers, since an unambiguous state is shared across all nodes. This facilitates a common view on the identifiers without the need for a (virtually) centralized directory. To support this architecture, we propose a data model and network methodology based on a distributed ledger and the W3C recommendation for Decentralized Identifiers (DID). We implemented our approach as a working prototype on a five-peer test network based on Hyperledger Fabric.
Sotiris P. Gayialis, Evripidis P. Kechagias, Georgios Α. Papadopoulos
Undoubtedly, supply chain operations management is becoming more demanding for product tracing due to regulatory frameworks and fraudulent incidents. Therefore, rapidly increasing challenges arise in transparency, tracking, and data storage in many product supply chains. Especially in the case of wine supply chains, timely and accurate traceability is vital, as this industry is plagued by frequent counterfeiting activities that pose significant risks to both consumer health and business viability and prosperity. Blockchain technology can serve as a crucial aid for constructing modern wine supply chains by offering increased security and trust between all supply chain stakeholders. This paper presents a framework for developing Ethereum-based Blockchain distributed applications and demonstrates a use case for creating such an application for a wine traceability system. The developed distributed application enables the participants of the wine supply chain to track wines by adding and monitoring data about each individual wine bottle's production, fermentation, aging, bottling, and distribution, providing full supervision of its production and distribution. The demonstrated use case shows the different types of users and their interactions with the system to fully comprehend the advantages it can offer to all supply chain stakeholders as well as consumers and controlling authorities.
Ingineria ontologică, împreună cu tehnologiile Web semantice, permit modelarea și dezvoltarea semantică a fluxului operațional necesar pentru proiectarea TB. Cel mai utilizat sistem de modelare blockchain prin reprezentarea abstractă, descrierea și definirea structurii, a proceselor, a informațiilor și a resurselor, este modelarea intreprinderilor. Modelarea intreprinderii utilizează ontologiile de domeniu folosind limbaje de reprezentare a modelului. Bitcoin este principalul sistem de plată peer-to-peer şi monedă digitală care folosește tehnologia blockchain.
Abstract An emerging approach that addresses data heterogeneity challenges is the mediator-based architecture allowing transparent access to the data stored in many sources. Due to the growing diversity of data sources, the data integration process becomes a performance and administrative bottleneck. When dealing with decentralized heterogeneous data sources, the mediator-based technique is typically used to integrate the data. It describes a collection of applications that follow a number of data sources. The importance of analyzing and synthesizing the collected data has lately increased for academics researching autonomous and heterogeneous software systems. This study uses a mediator-based information integration model to improve pattern-based reasoning and overcome grammatical problems with integrating diverse information from web sources (IATs). The main goal of this study is to improve the mediator design for usage in the mediator-based information integration paradigm in order to address the problems caused by syntactic heterogeneity. Using our recommended methodology and enhancement strategy, the proposed technique would choose the pertinent domain from a variety of vendor-related data sources and antiquated file systems and deliver the necessary information set from heterogeneous data sources. Our suggested extended design functions well in the online bookstore as well, where there are several data sources and antiquated file systems. Future applications of this research include providing a thorough syntactic method that must be used to integrate data from various sources within the same organization into any Executive Support System (ESS).
Mario Scrocca, Marco Comerio, Alessio Carenini, Irene Celino
The blockchain technology provides integrity and reliability of the information, thus offering a suitable solution to guarantee trustability in a multi-stakeholder scenario that involves actors defining business agreements. The Ride2Rail project investigated the use of the blockchain to record as smart contracts the agreements between different stakeholders defined in a multimodal transportation domain. Modelling an ontology to represent the smart contracts enables the possibility of having a machine-readable and interoperable representation of the agreements. On one hand, the underlying blockchain ensures trust in the execution of the contracts, on the other hand, their ontological representation facilitates the retrieval of information within the ecosystem. The paper describes the development of the Ride2Rail Ontology for Agreements to showcase how the concept of an ontological smart contract, defined in the OASIS ontology, can be applied to a specific domain. The usage of the designed ontology is discussed by describing the modelling as ontological smart contracts of business agreements defined in a ride-sharing scenario.
Timotej Knez, Domen Gašperlin, Marko Bajec, Slavko Žitnik
Knowledge graphs are commonly represented by ontology-based databases. Tracking the provenance of ontological changes and ensuring ontology consistency is important. In this work, we propose a transaction manager for ontology-based database manipulation that combines blockchain and Semantic Web technologies. The latter is used for the efficient querying and modification of data, whereas the blockchain is used for the secure storage and tracking of changes. The blockchain enables a decentralized setup and data restoration. We evaluate our solution by measuring cost and time. Our solution introduces some overhead for updates whereas querying works at the same speed as the underlying ontology database.
Célio Márcio Soares Ferreira, Charles Tim Batista Garrocho, Carlos Frederico Marcelo da Cunha Cavalcanti, Jorge Sá Silva · 5 authors
Blockchain is already advancing in journeys beyond cryptocurrency applications, and Ethereum, already called the world's computer, is going on a path that intends to reinvent the internet or Web 3.0, currently the leading platform for deploying so-called distributed applications (DApp). The growth of these Dapps in different spheres of society, as Smart Cities and Industry 4.0 IoT applications demand new proposals and models that integrate Ethereum and its ecosystem data with existing datasets on the traditional Web. This work presents our efforts to apply ontologies representing an Ethereum network and ecosystem using the Semantic Web model to extract and link its data. We show EthExtras a new ontology that extends and simplifies the EthOn, and as proof of concept and sample of use, we design a middleware web that exposed as RDF graphs the Ethereum data in soft real-time.
Blockchains are gaining momentum due to the interest of industries and people in \emph{decentralized applications} (Dapps), particularly in those for trading assets through digital certificates secured on blockchain, called tokens. As a consequence, providing a clear unambiguous description of any activities carried out on blockchains has become crucial, and we feel the urgency to achieve that description at least for trading. This paper reports on how to leverage the \emph{Ontology for Agents, Systems, and Integration of Services} ("\ONT{}") as a general means for the semantic representation of smart contracts stored on blockchain as software agents. Special attention is paid to non-fungible tokens (NFTs), whose management through the ERC721 standard is presented as a case study.
Abstract We introduce the probabilistic two-agent justification logic $\textsf {IPJ}$, a logic in which we can reason about agents that perform interactive proofs. In order to study the growth rate of the probabilities in $\textsf {IPJ}$, we present a new method of parametrizing $\textsf {IPJ}$ over certain negligible functions. Further, our approach leads to a new notion of zero-knowledge proofs.
We focus on a type of linguistic formal reasoning where the goal is to reason\nover explicit knowledge in the form of natural language facts and rules (Clark\net al., 2020). A recent work, named PRover (Saha et al., 2020), performs such\nreasoning by answering a question and also generating a proof graph that\nexplains the answer. However, compositional reasoning is not always unique and\nthere may be multiple ways of reaching the correct answer. Thus, in our work,\nwe address a new and challenging problem of generating multiple proof graphs\nfor reasoning over natural language rule-bases. Each proof provides a different\nrationale for the answer, thereby improving the interpretability of such\nreasoning systems. In order to jointly learn from all proof graphs and exploit\nthe correlations between multiple proofs for a question, we pose this task as a\nset generation problem over structured output spaces where each proof is\nrepresented as a directed graph. We propose two variants of a proof-set\ngeneration model, multiPRover. Our first model, Multilabel-multiPRover,\ngenerates a set of proofs via multi-label classification and implicit\nconditioning between the proofs; while the second model, Iterative-multiPRover,\ngenerates proofs iteratively by explicitly conditioning on the previously\ngenerated proofs. Experiments on multiple synthetic, zero-shot, and\nhuman-paraphrased datasets reveal that both multiPRover models significantly\noutperform PRover on datasets containing multiple gold proofs.\nIterative-multiPRover obtains state-of-the-art proof F1 in zero-shot scenarios\nwhere all examples have single correct proofs. It also generalizes better to\nquestions requiring higher depths of reasoning where multiple proofs are more\nfrequent. Our code and models are publicly available at\nhttps://github.com/swarnaHub/multiPRover\n
Numerous indexing databases keep track of the number of publications, citations, etc. in order to maintain the progress of science and individual. However, the choice of journals and articles varies among these indexing databases, hence the number of citations and h-index varies. There is no common platform exists that can provide a single count for the number of publications, citations, h-index, etc. To overcome this limitation, we have proposed a weighted unified informetrics, named "conflate". The proposed system takes into account the input from multiple indexing databases and generates a single output. Here, we have used the data from Scopus and WoS to generate a conflate dataset. Further, a comparative analysis of conflate has been performed with Scopus and WoS at three levels: author, organization, and journal. Finally, a mapping is proposed between research publications and distributed ledger technology in order to provide a transparent and distributed view to its stakeholders.
Juan Cano-Benito, Andrea Cimmino, Raúl García‐Castro
Blockchain has become a pervasive technology in a wide number of sectors like industry, research, and academy. With the emergence of blockchain, new solutions with this technology to existing problems were devised, leading to the introduction of smart contracts. Smart contracts are similar to traditional contracts with the benefits provided by blockchain, such as immutability, privacy, and decentralisation. These contracts are usually defined based on a specific domain, and this domain knowledge can be represented through an ontology. Researches have explored the benefits of using domain ontologies with smart contracts, such as code generation, discovering other contracts in the network, or interaction with other contracts. Notwithstanding, the representation of smart contract languages themselves has not been studied. In this paper, we present an ontology for a well-known smart contract language, Solidity, defining all entities needed to cover the whole language and aligning it to other standardised ontologies such as EthOn, in a way to improve the knowledge of the ontology developed. Furthermore, the ontology has also been validated with already deployed contracts in the Ethereum blockchain. Thus, Solidity will be able to benefit from the advantages provided by ontologies, such as interoperability and the use of semantic web technologies.