Automated UML-based ontology generation in OSLO²

In 2015, Flanders Information started the OSLO2 project, aimed at easing the exchange of data and increasing the interoperability of Belgian government services. RDF ontologies were developed to break apart the government data silos and stimulate data reuse. However, ontology design still encounters a number of difficulties. Since domain experts are generally unfamiliar with RDF, a design process is needed that allows these experts to efficiently contribute to intermediate ontology prototypes. We designed the OSLO2 ontologies using UML, a modeling language well known within the government, as a single source specification. From this source, the ontology and other relevant documents are generated. This paper describes the conversion tooling and the pragmatic approaches that were taken into account in its design. While this tooling is somewhat focused on the design principles used in the OSLO2 project, it can serve as the basis for a generic conversion tool. All source code and documentation are available online

Linked Data Notifications: A Resource-Centric Communication Protocol

In this article we describe the Linked Data Notifications (LDN) protocol, which is a W3C Candidate Recommendation. Notifications are sent over the Web for a variety of purposes, for example, by social applications. The information contained within a notification is structured arbitrarily, and typically only usable by the application which generated it in the first place. In the spirit of Linked Data, we propose that notifications should be reusable by multiple authorised applications. Through separating the concepts of senders, receivers and consumers of notifications, and leveraging Linked Data principles of shared vocabularies and URIs, LDN provides a building block for decentralised Web applications. This permits end users more freedom to switch between the online tools they use, as well as generating greater value when notifications from different sources can be used in combination. We situate LDN alongside related initiatives, and discuss additional considerations such as security and abuse prevention measures. We evaluate the protocol’s effectiveness by analysing multiple, independent implementations, which pass a suite of formal tests and can be demonstrated interoperating with each other. To experience the described features please open this document in your Web browser under its canonical URI: http://csarven.ca/linked-data-notifications

Time-Aware Probabilistic Knowledge Graphs

The emergence of open information extraction as a tool for constructing and expanding knowledge graphs has aided the growth of temporal data, for instance, YAGO, NELL and Wikidata. While YAGO and Wikidata maintain the valid time of facts, NELL records the time point at which a fact is retrieved from some Web corpora. Collectively, these knowledge graphs (KG) store facts extracted from Wikipedia and other sources. Due to the imprecise nature of the extraction tools that are used to build and expand KG, such as NELL, the facts in the KG are weighted (a confidence value representing the correctness of a fact). Additionally, NELL can be considered as a transaction time KG because every fact is associated with extraction date. On the other hand, YAGO and Wikidata use the valid time model because they maintain facts together with their validity time (temporal scope). In this paper, we propose a bitemporal model (that combines transaction and valid time models) for maintaining and querying bitemporal probabilistic knowledge graphs. We study coalescing and scalability of marginal and MAP inference. Moreover, we show that complexity of reasoning tasks in atemporal probabilistic KG carry over to the bitemporal setting. Finally, we report our evaluation results of the proposed model

RDF graph summarization: principles, techniques and applications (tutorial)

International audienceThe explosion in the amount of the RDF on the Web has lead to the need to explore, query and understand such data sources. The task is challenging due to the complex and heterogeneous structure of RDF graphs which, unlike relational databases, do not come with a structure-dictating schema. Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; the summarization goal, and the main computational tools employed for summarizing graphs, are the main factors behind this diversity. This tutorial presents a structured analysis and comparison existing works in the area of RDF summarization; it is based upon a recent survey which we co-authored with colleagues [3]. We present the concepts at the core of each approach, outline their main technical aspects and implementation. We conclude by identifying the most pertinent summarization method for different usage scenarios, and discussing areas where future effort is needed

Overview of XBRL Taxonomy Usage for Structured Sustainability Reporting in European Filings

The increasing requirement for businesses to disclose sustainability information digitally has prompted significant changes in the content and format of Environmental, Social, and Governance (ESG) disclosures. However, companies mandated to adapt to these changes face technological and information challenges regarding ‘what’ and ‘how’ to report. For European filers, the Corporate Sustainability Reporting Directive (CSRD) and its requirements, the European Sustainability Reporting Standards (ESRS), along with the International Financial Reporting Standards (IFRS S1 and S2), propose the use of the eXtensible Business Reporting Language (XBRL) as the anticipated technical solution for the digital data structure. The objective of this paper is to provide a methodological framework for effectively navigating the complex and interrelated concepts relevant to stakeholders. Rather than relying on cumbersome textual guides, this framework leverages an examination of existing taxonomies to offer readers insights into the essential glossary of disclosures and metrics considered crucial by official regulatory sources. Furthermore, the research discusses the emphasis on qualitative and narrative disclosures in ESG reporting and their feasibility of comparable results. Employing this methodology facilitates the implementation of corporate case studies and enables the analysis of mass amounts of future annual reports for comprehensive sustainability performance measurement

DisKnow: a social-driven disaster support knowledge extraction system

This research is aimed at creating and presenting DisKnow, a data extraction system with the capability of filtering and abstracting tweets, to improve community resilience and decision-making in disaster scenarios. Nowadays most people act as human sensors, exposing detailed information regarding occurring disasters, in social media. Through a pipeline of natural language processing (NLP) tools for text processing, convolutional neural networks (CNNs) for classifying and extracting disasters, and knowledge graphs (KG) for presenting connected insights, it is possible to generate real-time visual information about such disasters and affected stakeholders, to better the crisis management process, by disseminating such information to both relevant authorities and population alike. DisKnow has proved to be on par with the state-of-the-art Disaster Extraction systems, and it contributes with a way to easily manage and present such happenings.info:eu-repo/semantics/publishedVersio

Can Knowledge Graphs Simplify Text?

Knowledge Graph (KG)-to-Text Generation has seen recent improvements in generating fluent and informative sentences which describe a given KG. As KGs are widespread across multiple domains and contain important entity-relation information, and as text simplification aims to reduce the complexity of a text while preserving the meaning of the original text, we propose KGSimple, a novel approach to unsupervised text simplification which infuses KG-established techniques in order to construct a simplified KG path and generate a concise text which preserves the original input's meaning. Through an iterative and sampling KG-first approach, our model is capable of simplifying text when starting from a KG by learning to keep important information while harnessing KG-to-text generation to output fluent and descriptive sentences. We evaluate various settings of the KGSimple model on currently-available KG-to-text datasets, demonstrating its effectiveness compared to unsupervised text simplification models which start with a given complex text. Our code is available on GitHub.Comment: Accepted as a Main Conference Long Paper at CIKM 202