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Randolpho Rubenso Goldmano pateikta kritika Kurto Gödelio ontologiniam įrodymui pabrėžė ligi tol praktiškai nepastebėtą šio įrodymo silpnybę. Anot Goldmano, tam, kad samprotavimas būtų iš tiesų sėkmingas, reikalinga sukurti jam taikomąją semantiką. Kadangi Dievą suprantame priskirdami jam konkrečias savybes, viena iš taikomosios semantikos užduočių kaip tik ir yra formalios įrodymo pateikties susiejimas su šiais teologiškai reikšmingais turiniais. Vis dėlto, Goldmano teigimu, mėginimai tai padaryti kelia rimtų iššūkių: bandant išreikšti įrodymą natūralia bei teologiškai tinkama kalba daugybė sakinių joje tampa beprasmiai, todėl pažeidžiamas dvireikšmiškumo principas. Šiame straipsnyje analizuojami skirtingi būdai spręsti šį sunkumą, kaip galutinį siūlymą pateikiant sprendimą, kurio orientyras yra Bertrand’o Russello apibrėžiamųjų deskripcijų teorija: sakinius, figūruojančius šioje natūralios kalbos interpretacijoje, siūloma traktuoti kaip konjunkcijas – tai leidžia atsikratyti beprasmių sakinių bei išsaugoti dvireikšmiškumo principą.

The early phase of the COVID-19 pandemic caused a lot of pressure to scientists due to the novel nature of the coronavirus. As experts in the field, they were expected to produce only reliable information. Owing to the limited data available at the time, there were many uncertainties surrounding the virus. However, studies that looked into how the uncertainties were navigated are scarce. This corpus-based study investigates this issue using the system value of modal operators by Halliday and Matthiessen (2014), along with Dong et al.’s (2020) classification of COVID-19 research themes to explore levels of scientific researchers’ certainty in presenting information about coronavirus. Specifically, their choices of modal auxiliaries as epistemic devices are analysed. Results demonstrate that researchers mainly conduct studies on epidemiology with the lowest degree of certainty by utilising modals such as may, could and might. Furthermore, while some of the propositions expressed do display researchers’ assumptions of possibilities, they however, are presented with insufficient evidence. These findings help to untangle scientific researchers’ expressions of uncertainty and certainty through their use of epistemic devices and contribute to a better understanding of their intentions in conveying vital information.

In today’s age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case.

In today's age of modern information technology, large amounts of data are generated every second to enable subsequent data aggregation and analysis. However, the IT infrastructures that have been set up over the last few decades and which should now be used for this purpose are very heterogeneous and complex. As a result, tasks for analyzing data, such as collecting, searching, understanding and processing data, become very time-consuming. This makes it difficult to realize visions, such as the Internet of Production, which pursues the goal of guaranteeing the availability of real-time information at any time and place in an industrial setting. To reduce the time to analytics in such scenarios, we present a data ingestion, integration and processing approach consisting of a flexible and configurable data ingestion pipeline as well as a semantic data platform named ESKAPE. The ingestion pipeline provides an abstraction to all tasks related to data acquisition. The main goal is, therefore, the controllable access to data and meta information contained in machines and other systems on the shop floor. Additionally, it provides the possibility to forward the collected data to a configurable endpoint, such as a data lake. ESKAPE acts as one of those endpoints enabling semantic data integration and processing. By annotating data sets with semantic models originating from the Semantic Web, data analysts are able to understand, process and discover these data sets more efficiently. ESKAPE features a three-layered information storage architecture consisting of a data layer for storing integrated raw data sets, a layer containing user-defined semantic models to describe the contextual knowledge necessary to interpret the stored data and a top layer formed by a continuously evolving knowledge graph, combining semantic information from all present semantic models. Based on this storage system, ESKAPE enables the flexible annotation as well as efficient search and processing of data sources without losing the ability of analyzing and querying the underlying raw data with analytic tools. We present and discuss our approach and its benefits and limitations based on a real-world industrial use case. [ABSTRACT FROM AUTHOR]