Semantic Web最新文献_第2页

d2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts d2kg：基于知识图谱表示政府决策和行为的综合本体论

Semantic Web Pub Date : 2024-02-13 DOI: 10.3233/sw-243535

Konstantinos Serderidis, Ioannis Konstantinidis, G. Meditskos, Vassilios Peristeras, Nick Bassiliades

{"title":"d2kg: An integrated ontology for knowledge graph-based representation of government decisions and acts","authors":"Konstantinos Serderidis, Ioannis Konstantinidis, G. Meditskos, Vassilios Peristeras, Nick Bassiliades","doi":"10.3233/sw-243535","DOIUrl":"https://doi.org/10.3233/sw-243535","url":null,"abstract":"To implement Open Governance a crucial element is the efficient use of the big amounts of open data produced in the public domain. Public administration is a rich source of data and potentially new knowledge. It is a data intensive sector producing vast amounts of information encoded in government decisions and acts, published nowadays on the World Wide Web. The knowledge shared on the Web is mostly made available via semi-structured documents written in natural language. To exploit this knowledge, technologies such as Natural Language Processing, Information Extraction, Data mining and the Semantic Web could be used, embedding into documents explicit semantics based on formal knowledge representations such as ontologies. Knowledge representation can be made possible by the deployment of Knowledge Graphs, collections of interlinked representations of entities, events or concepts, based on underlying ontologies. This can assist data analysts to achieve a higher level of situational awareness, facilitating automated reasoning towards different objectives, such as for knowledge management, data maintenance, transparency and cybersecurity. This paper presents a new ontology d2kg [d(iavgeia) 2(to) k(nowledge) g(raph)] integrating in a unique way standard EU ontologies, core and controlled vocabularies to enable exploitation of publicly available data from government decisions and acts published on the Greek platform Diavgeia with the aim to facilitate data sharing, re-usability and interoperability. It demonstrates a characteristic example of a Knowledge Graph based representation of government decisions and acts, highlighting its added value to respond to real practical use cases for the promotion of transparency, accountability and public awareness. The developed d2kg ontology in owl is accessible at: http://w3id.org/d2kg, as well as documented at: http://w3id.org/d2kg/documentation.","PeriodicalId":506307,"journal":{"name":"Semantic Web","volume":"59 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139839402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Ontology-based GraphQL server generation for data access and data integration 为数据访问和数据集成生成基于本体的 GraphQL 服务器

Semantic Web Pub Date : 2024-01-09 DOI: 10.3233/sw-233550

Huanyu Li, Olaf Hartig, R. Armiento, Patrick Lambrix

{"title":"Ontology-based GraphQL server generation for data access and data integration","authors":"Huanyu Li, Olaf Hartig, R. Armiento, Patrick Lambrix","doi":"10.3233/sw-233550","DOIUrl":"https://doi.org/10.3233/sw-233550","url":null,"abstract":"In a GraphQL Web API, a so-called GraphQL schema defines the types of data objects that can be queried, and so-called resolver functions are responsible for fetching the relevant data from underlying data sources. Thus, we can expect to use GraphQL not only for data access but also for data integration, if the GraphQL schema reflects the semantics of data from multiple data sources, and the resolver functions can obtain data from these data sources and structure the data according to the schema. However, there does not exist a semantics-aware approach to employ GraphQL for data integration. Furthermore, there are no formal methods for defining a GraphQL API based on an ontology. In this work, we introduce a framework for using GraphQL in which a global domain ontology informs the generation of a GraphQL server that answers requests by querying heterogeneous data sources. The core of this framework consists of an algorithm to generate a GraphQL schema based on an ontology and a generic resolver function based on semantic mappings. We provide a prototype, OBG-gen, of this framework, and we evaluate our approach over a real-world data integration scenario in the materials design domain and two synthetic benchmark scenarios (Linköping GraphQL Benchmark and GTFS-Madrid-Bench). The experimental results of our evaluation indicate that: (i) our approach is feasible to generate GraphQL servers for data access and integration over heterogeneous data sources, thus avoiding a manual construction of GraphQL servers, and (ii) our data access and integration approach is general and applicable to different domains where data is shared or queried via different ways.","PeriodicalId":506307,"journal":{"name":"Semantic Web","volume":"5 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139443566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

A study of concept similarity in Wikidata 维基数据中的概念相似性研究

Semantic Web Pub Date : 2024-01-08 DOI: 10.3233/sw-233520

Filip Ilievski, K. Shenoy, Hans Chalupsky, Nicholas Klein, Pedro Szekely

{"title":"A study of concept similarity in Wikidata","authors":"Filip Ilievski, K. Shenoy, Hans Chalupsky, Nicholas Klein, Pedro Szekely","doi":"10.3233/sw-233520","DOIUrl":"https://doi.org/10.3233/sw-233520","url":null,"abstract":"Robust estimation of concept similarity is crucial for applications of AI in the commercial, biomedical, and publishing domains, among others. While the related task of word similarity has been extensively studied, resulting in a wide range of methods, estimating concept similarity between nodes in Wikidata has not been considered so far. In light of the adoption of Wikidata for increasingly complex tasks that rely on similarity, and its unique size, breadth, and crowdsourcing nature, we propose that conceptual similarity should be revisited for the case of Wikidata. In this paper, we study a wide range of representative similarity methods for Wikidata, organized into three categories, and leverage background information for knowledge injection via retrofitting. We measure the impact of retrofitting with different weighted subsets from Wikidata and ProBase. Experiments on three benchmarks show that the best performance is achieved by pairing language models with rich information, whereas the impact of injecting knowledge is most positive on methods that originally do not consider comprehensive information. The performance of retrofitting is conditioned on the selection of high-quality similarity knowledge. A key limitation of this study, similar to prior work lies in the limited size and scope of the similarity benchmarks. While Wikidata provides an unprecedented possibility for a representative evaluation of concept similarity, effectively doing so remains a key challenge.","PeriodicalId":506307,"journal":{"name":"Semantic Web","volume":"11 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139445798","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

InterpretME: A tool for interpretations of machine learning models over knowledge graphs InterpretME：在知识图谱上解释机器学习模型的工具

Semantic Web Pub Date : 2024-01-05 DOI: 10.3233/sw-233511

Yashrajsinh Chudasama, Disha Purohit, Philipp D. Rohde, Julian Gercke, Maria-Esther Vidal

{"title":"InterpretME: A tool for interpretations of machine learning models over knowledge graphs","authors":"Yashrajsinh Chudasama, Disha Purohit, Philipp D. Rohde, Julian Gercke, Maria-Esther Vidal","doi":"10.3233/sw-233511","DOIUrl":"https://doi.org/10.3233/sw-233511","url":null,"abstract":"In recent years, knowledge graphs (KGs) have been considered pyramids of interconnected data enriched with semantics for complex decision-making. The potential of KGs and the demand for interpretability of machine learning (ML) models in diverse domains (e.g., healthcare) have gained more attention. The lack of model transparency negatively impacts the understanding and, in consequence, interpretability of the predictions made by a model. Data-driven models should be empowered with the knowledge required to trace down their decisions and the transformations made to the input data to increase model transparency. In this paper, we propose InterpretME, a tool that using KGs, provides fine-grained representations of trained ML models. An ML model description includes data – (e.g., features’ definition and SHACL validation) and model-based characteristics (e.g., relevant features and interpretations of prediction probabilities and model decisions). InterpretME allows for defining a model’s features over data collected in various formats, e.g., RDF KGs, CSV, and JSON. InterpretME relies on the SHACL schema to validate integrity constraints over the input data. InterpretME traces the steps of data collection, curation, integration, and prediction; it documents the collected metadata in the InterpretME KG. InterpretME is published in GitHub11 https://github.com/SDM-TIB/InterpretME and Zenodo22 https://doi.org/10.5281/zenodo.8112628. The InterpretME framework includes a pipeline for enhancing the interpretability of ML models, the InterpretME KG, and an ontology to describe the main characteristics of trained ML models; a PyPI library of InterpretME is also provided33 https://pypi.org/project/InterpretME/. Additionally, a live code44 https://github.com/SDM-TIB/InterpretME_Demo, and a video55 https://www.youtube.com/watch?v=Bu4lROnY4xg demonstrating InterpretME in several use cases are also available.","PeriodicalId":506307,"journal":{"name":"Semantic Web","volume":"79 9","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139381598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0