Ninai/Udiron, a living function-based natural language generation system, uses knowledge in Wikidata lexemes and items to transform abstract representations of factual statements into human-readable text. The combined system first produces syntax trees based on those abstract representations (Ninai) and then yields sentences from those syntax trees (Udiron). The system relies on information about individual lexical units and links to the concepts those units represent, as well as rules encoded in various types of functions to which users may contribute, to make decisions about words, phrases, and other morphemes to use and how to arrange them. Various system design choices work toward using the information in Wikidata lexemes and items efficiently and effectively, making different components individually contributable and extensible, and making the overall resultant outputs from the system expectable and analyzable. These targets accompany the intentions for Ninai/Udiron to ultimately power the Abstract Wikipedia project as well as be hosted on the Wikifunctions project.
{"title":"Using Wikidata lexemes and items to generate text from abstract representations","authors":"Mahir Morshed","doi":"10.3233/sw-243564","DOIUrl":"https://doi.org/10.3233/sw-243564","url":null,"abstract":"Ninai/Udiron, a living function-based natural language generation system, uses knowledge in Wikidata lexemes and items to transform abstract representations of factual statements into human-readable text. The combined system first produces syntax trees based on those abstract representations (Ninai) and then yields sentences from those syntax trees (Udiron). The system relies on information about individual lexical units and links to the concepts those units represent, as well as rules encoded in various types of functions to which users may contribute, to make decisions about words, phrases, and other morphemes to use and how to arrange them. Various system design choices work toward using the information in Wikidata lexemes and items efficiently and effectively, making different components individually contributable and extensible, and making the overall resultant outputs from the system expectable and analyzable. These targets accompany the intentions for Ninai/Udiron to ultimately power the Abstract Wikipedia project as well as be hosted on the Wikifunctions project.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141349302","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This special issue on the Interactive Semantic Web presents selected papers that contribute to the design, development, and refinement of all aspects of human interaction with the Semantic Web. The scope of this special issue is twofold: i) interactive techniques and visualizations that assist the human in tasks (e.g., browsing, inspecting, inferring) involving semantic data such as ontologies, linked data, knowledge graphs etc.; and ii) intelligent interfaces such as those that are driven by semantic technologies and other forms of machine intelligence, as well as those that empower users with personalizable and adaptive features. Given
{"title":"Editorial: Special issue on Interactive Semantic Web","authors":"Bo Fu, Patrick Lambrix, Catia Pesquita","doi":"10.3233/sw-243672","DOIUrl":"https://doi.org/10.3233/sw-243672","url":null,"abstract":"This special issue on the Interactive Semantic Web presents selected papers that contribute to the design, development, and refinement of all aspects of human interaction with the Semantic Web. The scope of this special issue is twofold: i) interactive techniques and visualizations that assist the human in tasks (e.g., browsing, inspecting, inferring) involving semantic data such as ontologies, linked data, knowledge graphs etc.; and ii) intelligent interfaces such as those that are driven by semantic technologies and other forms of machine intelligence, as well as those that empower users with personalizable and adaptive features. Given","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141106474","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Enrique Iglesias, Maria-Esther Vidal, D. Collarana, David Chaves-Fraga
The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.
{"title":"Empowering the SDM-RDFizer tool for scaling up to complex knowledge graph creation pipelines1","authors":"Enrique Iglesias, Maria-Esther Vidal, D. Collarana, David Chaves-Fraga","doi":"10.3233/sw-243580","DOIUrl":"https://doi.org/10.3233/sw-243580","url":null,"abstract":"The significant increase in data volume in recent years has prompted the adoption of knowledge graphs as valuable data structures for integrating diverse data and metadata. However, this surge in data availability has brought to light challenges related to standardization, interoperability, and data quality. Knowledge graph creation faces complexities from large data volumes, data heterogeneity, and high duplicate rates. This work addresses these challenges and proposes data management techniques to scale up the creation of knowledge graphs specified using the RDF Mapping Language (RML). These techniques are integrated into SDM-RDFizer, transforming it into a two-fold solution designed to address the complexities of generating knowledge graphs. Firstly, we introduce a reordering approach for RML triples maps, prioritizing the evaluation of the most selective maps first to reduce memory usage. Secondly, we employ an RDF compression strategy, along with optimized data structures and novel operators, to prevent the generation of duplicate RDF triples and optimize the execution of RML operators. We assess the performance of SDM-RDFizer through established benchmarks. The evaluation showcases the effectiveness of SDM-RDFizer compared to state-of-the-art RML engines, emphasizing the benefits of our techniques. Furthermore, the paper presents real-world projects where SDM-RDFizer has been utilized, providing insights into the advantages of declaratively defining knowledge graphs and efficiently executing these specifications using this engine.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140371182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bahar Aameri, M. Poveda-Villalón, Emilio M. Sanfilippo, Walter Terkaj
{"title":"Special Issue on Semantic Web for Industrial Engineering: Research and Applications","authors":"Bahar Aameri, M. Poveda-Villalón, Emilio M. Sanfilippo, Walter Terkaj","doi":"10.3233/sw-243623","DOIUrl":"https://doi.org/10.3233/sw-243623","url":null,"abstract":"","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140223539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Amr Azzam, A. Polleres, Javier D. Fernández, Maribel Acosta
RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.
{"title":"smart-KG: Partition-Based Linked Data Fragments for querying knowledge graphs","authors":"Amr Azzam, A. Polleres, Javier D. Fernández, Maribel Acosta","doi":"10.3233/sw-243571","DOIUrl":"https://doi.org/10.3233/sw-243571","url":null,"abstract":"RDF and SPARQL provide a uniform way to publish and query billions of triples in open knowledge graphs (KGs) on the Web. Yet, provisioning of a fast, reliable, and responsive live querying solution for open KGs is still hardly possible through SPARQL endpoints alone: while such endpoints provide a remarkable performance for single queries, they typically can not cope with highly concurrent query workloads by multiple clients. To mitigate this, the Linked Data Fragments (LDF) framework sparked the design of different alternative low-cost interfaces such as Triple Pattern Fragments (TPF), that partially offload the query processing workload to the client side. On the downside, such interfaces still come with the expense of unnecessarily high network load due to the necessary transfer of intermediate results to the client, leading to query performance degradation compared with endpoints. To address this problem, in the present work, we investigate alternative interfaces, refining and extending the original TPF idea, which also aims at reducing server-resource consumption, by shipping query-relevant partitions of KGs from the server to the client. To this end, first, we align formal definitions and notations of the original LDF framework to uniformly present existing LDF implements and such “partition-based” LDF approaches. These novel LDF interfaces retrieve, instead of the exact triples matching a particular query pattern, a subset of pre-materialized, compressed, partitions of the original graph, containing all answers to a query pattern, to be further evaluated on the client side. As a concrete representative of partition-based LDF, we present smart-KG+, extending and refining our prior work (In WWW ’20: The Web Conference 2020 (2020) 984–994 ACM / IW3C2) in several respects. Our proposed approach is a step forward towards a better-balanced share of the query processing load between clients and servers by shipping graph partitions driven by the structure of RDF graphs to group entities described with the same sets of properties and classes, resulting in significant data transfer reduction. Our experiments demonstrate that the smart-KG+ significantly outperforms existing Web SPARQL interfaces on both pre-existing benchmarks for highly concurrent query execution as well as an accustomed query workload inspired by query logs of existing SPARQL endpoints.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Valentina Anita Carriero, Paul Groth, Valentina Presutti
The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.
{"title":"Empirical ontology design patterns and shapes from Wikidata","authors":"Valentina Anita Carriero, Paul Groth, Valentina Presutti","doi":"10.3233/sw-243613","DOIUrl":"https://doi.org/10.3233/sw-243613","url":null,"abstract":"The ontology underlying the Wikidata knowledge graph (KG) has not been formalized. Instead, its semantics emerges bottom-up from the use of its classes and properties. Flexible guidelines and rules have been defined by the Wikidata project for the use of its ontology, however, it is still often difficult to reuse the ontology’s constructs. Based on the assumption that identifying ontology design patterns from a knowledge graph contributes to making its (possibly) implicit ontology emerge, in this paper we present a method for extracting what we term empirical ontology design patterns (EODPs) from a knowledge graph. This method takes as input a knowledge graph and extracts EODPs as sets of axioms/constraints involving the classes instantiated in the KG. These EODPs include data about the probability of such axioms/constraints happening. We apply our method on two domain-specific portions of Wikidata, addressing the music and art, architecture, and archaeology domains, and we compare the empirical ontology design patterns we extract with the current support present in Wikidata. We show how these patterns can provide guidance for the use of the Wikidata ontology and its potential improvement, and can give insight into the content of (domain-specific portions of) the Wikidata knowledge graph.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140227697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julián Arenas-Guerrero, Ana Iglesias-Molina, David Chaves-Fraga, Daniel Garijo, Óscar Corcho, Anastasia Dimou
RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.
{"title":"Declarative generation of RDF-star graphs from heterogeneous data","authors":"Julián Arenas-Guerrero, Ana Iglesias-Molina, David Chaves-Fraga, Daniel Garijo, Óscar Corcho, Anastasia Dimou","doi":"10.3233/sw-243602","DOIUrl":"https://doi.org/10.3233/sw-243602","url":null,"abstract":"RDF-star has been proposed as an extension of RDF to make statements about statements. Libraries and graph stores have started adopting RDF-star, but the generation of RDF-star data remains largely unexplored. To allow generating RDF-star from heterogeneous data, RML-star was proposed as an extension of RML. However, no system has been developed so far that implements the RML-star specification. In this work, we present Morph-KGCstar, which extends the Morph-KGC materialization engine to generate RDF-star datasets. We validate Morph-KGCstar by running test cases derived from the N-Triples-star syntax tests and we apply it to two real-world use cases from the biomedical and open science domains. We compare the performance of our approach against other RDF-star generation methods (SPARQL-Anything), showing that Morph-KGCstar scales better for large input datasets, but it is slower when processing multiple smaller files.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140225655","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Roberto Confalonieri, Oliver Kutz, Diego Calvanese, J. Alonso-Moral, Shang-Ming Zhou
science. These papers introduced domain-specific ontologies, providing a structured framework to facilitate understanding and explanation of the systems within each domain. The other group of papers took a more foundational approach by presenting logic-based methodologies that fostered the development of explainable-by-design systems. These papers emphasized the use of logical reasoning techniques to achieve explainability and offered frameworks for constructing systems that inherently prioritize interpretability. In summary, the accepted papers demonstrated the utilization of ontologies, knowledge graphs, and knowledge representation and reasoning in advancing the field of XAI. In the following, we provide a broad overview of all the accepted papers.
{"title":"The role of ontologies and knowledge in Explainable AI","authors":"Roberto Confalonieri, Oliver Kutz, Diego Calvanese, J. Alonso-Moral, Shang-Ming Zhou","doi":"10.3233/sw-243529","DOIUrl":"https://doi.org/10.3233/sw-243529","url":null,"abstract":"science. These papers introduced domain-specific ontologies, providing a structured framework to facilitate understanding and explanation of the systems within each domain. The other group of papers took a more foundational approach by presenting logic-based methodologies that fostered the development of explainable-by-design systems. These papers emphasized the use of logical reasoning techniques to achieve explainability and offered frameworks for constructing systems that inherently prioritize interpretability. In summary, the accepted papers demonstrated the utilization of ontologies, knowledge graphs, and knowledge representation and reasoning in advancing the field of XAI. In the following, we provide a broad overview of all the accepted papers.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140243905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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.
电子商务和金融正逐步支持并包括去中心化、共享和公共分类账，如区块链。这正在重塑传统的商业活动，将其推向去中心化金融（DeFi）和商业 3.0，从而支持后者超越中央权力控制者和法律制定者障碍的潜力。在这种不断发展的情况下，要成为积极的参与者，必须寻找和管理的信息的数量和熵都在稳步增加。例如，这些信息包括资产或服务说明、一般游戏规则以及去中心化所涉及的具体技术。此外，相关信息应在无数异构的利益相关者之间共享，如生产者、购买者、数字身份提供者、估价服务和运输服务等。对这样一个基于区块链的复杂而多面的电子商务生态系统进行清晰的语义表述，将大大有助于提高其可用性，即几乎任何想要扮演利益相关者角色的人都能更自动地访问该系统，从而减少程序员的工作量。然而，我们认为，要实现这一目标，仍需要在定制语义网技术方面付出大量努力，因此，本文开始了这样一条路线，并为去中心化电子商务的语义描述推进了OWL 2本体堆栈。该堆栈包括许多相关特征，从适用的利益相关者到资产产品的供应链，直至以太坊区块链、其代币和智能合约。受代理理论（Theory of Agents）和相关心智学概念的启发，本体的定义采用了行为学方法，将不同的参与者作为代理来表示他们的行为。通过适当的指标和 SPARQL 查询实现适当的能力问题，对堆栈进行验证，然后通过表示真实世界的使用案例（即 iExec 市场）进行演示。
{"title":"A behaviouristic semantic approach to blockchain-based e-commerce","authors":"Giampaolo Bella, Domenico Cantone, Gianpietro Castiglione, Marianna Nicolosi Asmundo, Daniele Francesco Santamaria","doi":"10.3233/sw-243543","DOIUrl":"https://doi.org/10.3233/sw-243543","url":null,"abstract":"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.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140242901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Matthieu Bellucci, Nicolas Delestre, Nicolas Malandain, Cecilia Zanni-Merk
Debugging and repairing Web Ontology Language (OWL) ontologies has been a key field of research since OWL became a W3C recommendation. One way to understand errors and fix them is through explanations. These explanations are usually extracted from the reasoner and displayed to the ontology authors as is. In the meantime, there has been a recent call in the eXplainable AI (XAI) field to use expert knowledge in the form of knowledge graphs and ontologies. In this paper, a parallel between explanations for machine learning and for ontologies is drawn. This link enables the adaptation of XAI methods to explain ontologies and their entailments. Counterfactual explanations have been identified as a good candidate to solve the explainability problem in machine learning. The CEO (Counterfactual Explanations for Ontologies) method is thus proposed to explain inconsistent ontologies using counterfactual explanations. A preliminary user study is conducted to ensure that using XAI methods for ontologies is relevant and worth pursuing.
{"title":"Towards counterfactual explanations for ontologies","authors":"Matthieu Bellucci, Nicolas Delestre, Nicolas Malandain, Cecilia Zanni-Merk","doi":"10.3233/sw-243566","DOIUrl":"https://doi.org/10.3233/sw-243566","url":null,"abstract":"Debugging and repairing Web Ontology Language (OWL) ontologies has been a key field of research since OWL became a W3C recommendation. One way to understand errors and fix them is through explanations. These explanations are usually extracted from the reasoner and displayed to the ontology authors as is. In the meantime, there has been a recent call in the eXplainable AI (XAI) field to use expert knowledge in the form of knowledge graphs and ontologies. In this paper, a parallel between explanations for machine learning and for ontologies is drawn. This link enables the adaptation of XAI methods to explain ontologies and their entailments. Counterfactual explanations have been identified as a good candidate to solve the explainability problem in machine learning. The CEO (Counterfactual Explanations for Ontologies) method is thus proposed to explain inconsistent ontologies using counterfactual explanations. A preliminary user study is conducted to ensure that using XAI methods for ontologies is relevant and worth pursuing.","PeriodicalId":48694,"journal":{"name":"Semantic Web","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140257629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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