Journal of Web Semantics最新文献

Geospatial data constitutes a considerable part of Semantic Web data, but so far, its sources are inadequately interlinked in the Linked Open Data cloud. Geospatial Interlinking aims to cover this gap by associating geometries with topological relations like those of the Dimensionally Extended 9-Intersection Model. Due to its quadratic time complexity, various algorithms aim to carry out Geospatial Interlinking efficiently. We present JedAI-spatial, a novel, open-source system that organizes these algorithms according to three dimensions: (i) Space Tiling, which determines the approach that reduces the search space, (ii) Budget-awareness, which distinguishes interlinking algorithms into batch and progressive ones, and (iii) Execution mode, which discerns between serial algorithms, running on a single CPU-core, and parallel ones, running on top of Apache Spark. We analytically describe JedAI-spatial’s architecture and capabilities and perform thorough experiments to provide interesting insights about the relative performance of its algorithms.

Infusing autonomous artificial systems with knowledge about the physical world they inhabit is a critical and long-held aim for the Artificial Intelligence community. Training systems with relevant data is a typical approach; however, finding the data required is not always possible, especially when much of this knowledge is commonsense. In this paper, we present a comparison of topology-based and semantics-based methods for extracting information about object-action and object-state association relations from knowledge graphs, such as ConceptNet, WordNet, ATOMIC, YAGO, WebChild and DBpedia. Moreover, we propose a novel method for extracting information about object-action and object-state associations from knowledge graphs. Our method is composed of a set of techniques for locating, enriching, evaluating, cleaning and exposing knowledge from such resources, relying on semantic similarity methods. Some important aspects of our method are the flexibility in deciding how to deal with the noise that exists in the data, and the capability to determine the importance of a path through training, rather than through manual annotation.

Knowledge graphs store and link semantically annotated data about real-world entities from a variety of domains and on a large scale. The World Avatar is based on a dynamic decentralised knowledge graph and on semantic technologies to realise complex cross-domain scenarios. Accurate computational results for such scenarios require the availability of complete, high-quality data. This work focuses on instance matching — one of the subtasks of automatically populating the knowledge graph with data from a wide spectrum of external sources. Instance matching compares two data sets and seeks to identify instances (data, records) referring to the same real-world entity. We introduce AutoCal, a new instance matcher which does not require labelled data and runs out of the box for a wide range of domains without tuning method-specific parameters. AutoCal achieves results competitive to recently proposed unsupervised matchers from the field of Machine Learning. We also select an unsupervised state-of-the-art matcher from the field of Deep Learning for a thorough comparison. Our results show that neither AutoCal nor the state-of-the-art matcher is superior regarding matching quality while AutoCal has only moderate hardware requirements and runs 2.7 to 60 times faster. In summary, AutoCal is specifically well-suited to be used in an automated environment. We present its prototypical integration into the World Avatar and apply AutoCal to the domain of power plants which is relevant for practical environmental scenarios of the World Avatar.

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.

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.

Ontology alignment is essential for data integration and interoperability across multiple applications across diverse disciplines. In recent decades, significant advancements have been made in the development of advanced methods and systems for ontology alignment. Empirical results have suggested that ontological semantics can be effectively employed to enhance the alignment process. Besides, structural information is crucial for ontology alignment as it reflects the relations among adjacent concepts in the ontology. Previous works are mainly based on external lexicon and predefined rules based on ontological structure. Recently, deep learning has imposed positive impacts on ontology alignment and obtained substantial improvement.

This paper proposes a new method based on ontology embedding incorporating the semantic and structural features. It utilizes the distance between the embedding of two ontological concepts to be aligned as the criterion for alignment. The proposed method is used to align two widely used food ontologies and three Chinese food classification ontologies. The experimental results show that our method enhances the performance compared to several state-of-the-art alignment systems, demonstrating the importance of learning semantic representation and structural representation. Furthermore, the proposed method is evaluated on several different tracks of the Ontology Alignment Evaluation Initiative (OAEI), and experimental results show that our method outperforms other baselines in effectiveness. The data and code can be obtained from: https://github.com/haozhigang1111/Ontology-Alignment.git.

We study discussions in Wikidata, the world’s largest open-source collaborative knowledge graph (KG). This is important because it helps KG community managers understand how discussions are used and inform the design of collaborative practices and support tools. We follow a mixed-methods approach with descriptive statistics, thematic analysis, and statistical tests to investigate how much discussions in Wikidata are used, what they are used for, and how they support knowledge engineering (KE) activities. The study covers three core sources of discussion, the talk pages that accompany Wikidata items and properties, and a general-purpose communication page. Our findings show low use of discussion capabilities and a power-law distribution similar to other KE projects such as Schema.org. When discussions are used, they are mostly about KE activities, including activities that span across the entire KE lifecycle from conceptualisation and implementation to maintenance and taxonomy building. We hope that the findings will help Wikidata devise improved practices and capabilities to encourage the use of discussions as a tool to collaborate, improve editor engagement, and engineer better KGs.

Enterprise RDF knowledge graphs are often built using extraction data pipelines that are fed by several heterogeneous sources (relational databases, CSV files or even unstructured textual data). As a direct consequence, the construction of these KGs undergoes a number of changes in the early stages of their life cycle, which are initiated by a human developer and therefore need to be done interactively and efficiently. Driven by such needs, in this paper, we present a solution for the incremental maintenance of KGs given user-prescribed changes. A key feature of the proposed solution is the support of provenance collection that can be used to assist the developer in the analysis and debugging of the KG. Specifically, we strive to compute and maintain the provenance of asserted and inferred facts in the knowledge graph incrementally (and thus efficiently). The evaluation exercises we have conducted show the effectiveness of our solution and highlight the parameters that impact performance.

Hadith is an essential and much-celebrated resource for the Islamic domain. It is one of the two primary sources of Islamic legislation. The hadith corpus is quite large, consisting of the collection of sayings, actions and silent approval of the Prophet Muhammad. Minimal efforts have been made to date, towards unified semantic modelling, and knowledge representation of the hadith structure for enhanced interlinking and knowledge discovery. This paper presents the design, development and publishing of the hadith corpus as a knowledge graph. First, we design the SemanticHadith ontology to describe and relate core structural concepts from the hadith. We then publish the six prominent hadith collections as an RDF-Based hadith knowledge graph, which is an effort towards making the available hadith both human and machine-readable. This is the first step in the annotation and linking process of the hadith corpus aimed at enabling semantic search capabilities to support scholars, students, and researchers in the creation, evolution, and consultation of a digital representation of Islamic knowledge. The SemanticHadith knowledge graph is freely accessible at http://www.semantichadith.com.

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 (ECII) were presented as a word list consisting of the concept names occurring in the highest rated system responses.