Information related to the COVID-19 pandemic ranges from biological to bibliographic, from geographical to genetic and beyond. The structure of the raw data is highly complex, so converting it to ...meaningful insight requires data curation, integration, extraction and visualization, the global crowdsourcing of which provides both additional challenges and opportunities. Wikidata is an interdisciplinary, multilingual, open collaborative knowledge base of more than 90 million entities connected by well over a billion relationships. It acts as a web-scale platform for broader computer-supported cooperative work and linked open data, since it can be written to and queried in multiple ways in near real time by specialists, automated tools and the public. The main query language, SPARQL, is a semantic language used to retrieve and process information from databases saved in Resource Description Framework (RDF) format. Here, we introduce four aspects of Wikidata that enable it to serve as a knowledge base for general information on the COVID-19 pandemic: its flexible data model, its multilingual features, its alignment to multiple external databases, and its multidisciplinary organization. The rich knowledge graph created for COVID-19 in Wikidata can be visualized, explored, and analyzed for purposes like decision support as well as educational and scholarly research.
In 2012 the Australian Bureau of Meteorology published a dataset, ACORN-SAT, containing the homogenised daily temperature observations of 112 locations throughout Australia for the last 100 years. ...The dataset employs the latest analysis techniques and takes advantage of newly digitised observational data to monitor climate variability and change in Australia. The observations in ACORN-SAT were initially published only as comma separated values, whereas the metadata was published in a PDF report. In 2013 we converted the metadata and the observation data into RDF and published the result as Linked Open Data, accessible online via a pilot government linked data service built on the Linked Data API. In this article we describe the process of transforming the original tabular data into a Linked Sensor Data Cube in: Proc. of the 5th International Workshop on Semantic Sensor Networks, SSN12, CEUR-WS.org, 2012, pp. 1–16 based on the W3C Semantic Sensor Network ontology Web Semantics: Science, Services and Agents on the World Wide Web 17 (2012), 25–32 and the W3C RDF Data Cube vocabulary The RDF Data Cube Vocabulary, W3C Recommendation, 16 January 2014. We further discuss how the dataset has since been used and interlinked with near-real time weather observations for the 112 sensing locations of the ACORN-SAT that are published by the Bureau of Meteorology. Both the original ACORN-SAT dataset and the weather observation data are accessible online at lab.environment.data.gov.au.
Data owners are creating an ever richer set of information resources online, and these are being used for more and more applications. Spatial data on the Web is becoming ubiquitous and voluminous ...with the rapid growth of location-based services, spatial technologies, dynamic location-based data and services published by different organizations. However, the heterogeneity and the peculiarities of spatial data, such as the use of different coordinate reference systems, make it difficult for data users, Web applications, and services to discover, interpret and use the information in the large and distributed system that is the Web. To make spatial data more effectively available, this paper summarizes the work of the joint W3C/OGC Working Group on Spatial Data on the Web that identifies 14 best practices for publishing spatial data on the Web. The paper extends that work by presenting the identified challenges and rationale for selection of the recommended best practices, framed by the set of principles that guided the selection. It describes best practices that are employed to enable publishing, discovery and retrieving (querying) spatial data on the Web, and identifies some areas where a best practice has not yet emerged.
The continuous growth of the Linked Data Web brings us closer to the original vision of the Web, as an interconnected network of machine-readable resources. However, the ability to easily manipulate ...Linked Open Data (LOD) is still an open issue. In this demonstration we will show through a lifecycle model how to enable a domain-agnostic read/write interaction with LOD. Our solution uses an ontology to build a binding front-end for a given RDF model, in addition to RDFa to maintain the semantics of the resulting form/widget components. On the processing side, a RESTful Web service is provided to seamlessly manage semantic widgets and their associated data, and hence enable the read/write data interaction mechanism.
The cloud infrastructure services landscape advances steadily leaving users in the agony of choice. Therefore, we present CloudRecommender, a new declarative approach for selecting Cloud-based ...infrastructure services. CloudRecommender automates the mapping of users’ specified application requirements to cloud service configurations. We formally capture cloud service configurations in ontology and provide its implementation in a structured data model which can be manipulated through both regular expressions and SQL. By exploiting the power of a visual programming language (widgets), CloudRecommender further enables simplified and intuitive cloud service selection. We describe the design and a prototype implementation of CloudRecommender, and demonstrate its effectiveness and scalability through a service configuration selection experiment on most of today’s prominent cloud providers including Amazon, Azure, and GoGrid.
Log-based transactional workflow mining Gaaloul, Walid; Gaaloul, Khaled; Bhiri, Sami ...
Distributed and parallel databases : an international journal,
06/2009, Letnik:
25, Številka:
3
Journal Article
Recenzirano
A continuous evolution of business process parameters, constraints and needs, hardly foreseeable initially, requires a continuous design from the business process management systems. In this article ...we are interested in developing a reactive design through process log analysis ensuring process re-engineering and execution reliability. We propose to analyse workflow logs to discover workflow transactional behaviour and to subsequently improve and correct related recovery mechanisms. Our approach starts by collecting workflow logs. Then, we build, by statistical analysis techniques, an intermediate representation specifying elementary dependencies between activities. These dependencies are refined to mine the transactional workflow model. The analysis of the discrepancies between the discovered model and the initially designed model enables us to detect design gaps, concerning particularly the recovery mechanisms. Thus, based on this mining step, we apply a set of rules on the initially designed workflow to improve workflow reliability.
This paper proposes a novel rule-based topic classification tool for questions on Q&A platforms mediated by the Wikidata ontology – an open and accessible multilingual ontology curated by a large ...community of online users. Q&A platforms are important sources of information on the Web and often appear as part of Web search results. By adopting Wikidata taxonomic relations as references, our tool can categories the Web content from different platforms in a unified coarse-to-fine mode based on their domain coverage. To validate and demonstrate the potential applicability of our tool, a set of use cases and experiments are carried out on two popular Q&A platforms – Zhihu and Quora, where the impact of topic categories on question lifecycles is explored. Furthermore, we compare our results with the output generated by GPT-3 classifier. This tool sheds light on how structured knowledge bases can enable data interoperability and serve as a filtering functionality to mitigate classification bias of OpenAI.
Recent advancements in AI have coincided with ever-increasing efforts in the research community to investigate, classify and evaluate various methods aimed at making AI models explainable. However, ...most of existing attempts present a method-centric view of eXplainable AI (XAI) which is typically meaningful only for domain experts. There is an apparent lack of a robust qualitative and quantitative performance framework that evaluates the suitability of explanations for different types of users. We survey relevant efforts, and then, propose a unified, inclusive and user-centred taxonomy for XAI based on the principles of General System's Theory, which serves us as a basis for evaluating the appropriateness of XAI approaches for all user types, including both developers and end users.
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