首页期刊导航|Journal of web semantics: Science, services and agents on the world wide web
期刊信息/Journal information
Journal of web semantics: Science, services and agents on the world wide web
Elsevier
Elsevier
不定期
1570-8268
Journal of web semantics: Science, services and agents on the world wide web/Journal Journal of web semantics: Science, services and agents on the world wide webEIISTPAHCISCI
查看更多>>摘要：Artificial Intelligence applications gradually move outside the safe walls of research labs and invade our daily lives. This is also true for Machine Learning methods on Knowledge Graphs, which has led to a steady increase in their application since the beginning of the 21st century. However, in many applications, users require an explanation of the Artificial Intelligence's decision. This led to increased demand for Comprehensible Artificial Intelligence. Knowledge Graphs epitomize fertile soil for Comprehensible Artificial Intelligence, due to their ability to display connected data, i.e. knowledge, in a human- as well as machine-readable way. This survey gives a short history to Comprehensible Artificial Intelligence on Knowledge Graphs. Furthermore, we contribute by arguing that the concept Explainable Artificial Intelligence is overloaded and overlapping with Interpretable Machine Learning. By introducing the parent concept Comprehensible Artificial Intelligence, we provide a clear-cut distinction of both concepts while accounting for their similarities. Thus, we provide in this survey a case for Comprehensible Artificial Intelligence on Knowledge Graphs consisting of Interpretable Machine Learning on Knowledge Graphs and Explainable Artificial Intelligence on Knowledge Graphs. This leads to the introduction of a novel taxonomy for Comprehensible Artificial Intelligence on Knowledge Graphs. In addition, a comprehensive overview of the research on Comprehensible Artificial Intelligence on Knowledge Graphs is presented and put into the context of the taxonomy. Finally, research gaps in the field of Comprehensible Artificial Intelligence on Knowledge Graphs are identified for future research.
Cara Leigh WidmerMd Kamruzzaman SarkerSrikanth NadellaJoshua Fiechter...
100807.1-100807.11页
查看更多>>摘要：Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base data (ABox) graph. In this paper, we show that it can also be used to explain data differentials, for example in the context of Explainable AI (XAI), and we show that it can in fact be done in a way that is meaningful to a human observer. Our approach utilizes a large class hierarchy, curated from the Wikipedia category hierarchy, as background knowledge. To make the explanations easily understandable for non-specialists, the complex description logic explanations generated by our concept induction system (ECU) were presented as a word list consisting of the concept names occurring in the highest rated system responses.
查看更多>>摘要：Although the maturity of technologies based on Artificial Intelligence (AI) is rather advanced nowadays, their adoption, deployment and application are not as wide as it could be expected. This could be attributed to many barriers, among which the lack of trust of users stands out. Accountability is a relevant factor to progress in this trustworthiness aspect, as it allows to determine the causes that derived a given decision or suggestion made by an AI system. This article focuses on the accountability of a specific branch of AI, statistical machine learning (ML), based on a semantic approach. FIDES, an ontology-based approach towards achieving the accountability of ML systems is presented, where all the relevant information related to a ML-based model is semantically annotated, from the dataset and model parametrisation to deployment aspects, to be exploited later to answer issues related to reproducibility, replicability, definitely, accountability. The feasibility of the proposed approach has been demonstrated in two scenarios, real-world energy efficiency and manufacturing, and it is expected to pave the way towards raising awareness about the potential of Semantic Technologies in different factors that may be key in the trustworthiness of AI-based systems.
Klevis ShkembiPetar KochovskiThanasis G. PapaioannouCaroline Barelle...
100809.1-100809.17页
查看更多>>摘要：In recent years, on the one hand, we have witnessed the rise of blockchain technology, which has led to better transparency, traceability, and therefore, trustworthy exchange of digital assets among different actors. On the other hand, achieving trustworthy content exchange has been one of the primary objectives of the Semantic Web, part of the World Wide Web Consortium. Semantic Web and blockchain technologies are the fundamental building blocks of Web3 (the third version of the Internet), which aims to link data through a decentralized approach. Blockchain provides a decentralized and secure framework for users to safeguard their data and take control over their data and Web3 experiences. However, developing trustworthy decentralized applications (Dapps) is a challenge because many blockchain-based functionalities must be developed from scratch, and combined with data semantics to open new innovative opportunities. In this survey paper, we explore the cross-cutting domain of the Semantic Web and blockchain and identify the critical building blocks required to achieve trust in the Next-Generation Internet. The application domains that could benefit from these technologies are also investigated. We developed a deep analysis of the published literature between 2015 and 2023. We performed our analysis in different digital libraries (e.g., Elsevier, IEEE, ACM), and as a result of our research, we retrieved 137 papers, of which 97 were retrieved as relevant to include in the paper. Furthermore, we studied several aspects (e.g., network type, transactions per second) of existing blockchain platforms. Semantic Web and blockchain technologies can be used to realize a verification and certification process for data quality. Examples of mechanisms to achieve this are the Decentralized Identities of the Semantic Web or the various blockchain consensus protocols that help achieve decentralization and realize democratic principles. Therefore, Semantic Web and blockchain technologies should be combined to achieve trust in the highly decentralized, semantically complex, and dynamic environments needed to build smart applications of the future.
NETL
NSTL
Elsevier