A Cultural Heritage Forum Celebrating Technological Innovation at Station X

We aim to encourage and support public participation in heritage through the development of Cultural Heritage Forums, a kind of cultural web portal that enables active participation of communities of interest in a way that complements rather than replaces visits to physical cultural institutions. The cultural heritage forum described here (Station X) is concerned with promoting an understanding of technology innovation in the areas of computing and cryptography. We propose a number of scenarios concerning how the forum can be designed, drawing on our earlier work in using knowledge modelling and text analysis to support the exploration of digital resources

Ontology Winnowing: A Case Study on the AKT Reference Ontology

Many ontologies are built for the main purpose of representing a domain, rather than to meet the requirements of a specific application. When applications and services are deployed over an ontology, it is sometimes the case that only few parts of the ontology are queried and used. Identifying which parts of an ontology are being used could useful for realising the necessary fragments of the ontology to run the applications. Such information could be used to winnow an ontology, i.e., simplifying or shrinking the ontology to smaller, more fit for purpose sizes. This paper presents a study on the use of the AKT Reference Ontology by a number of applications and services, and investigate the possibility of using this information to winnow that ontology

Digitization Principles for Application Scenarios towards Digital Twins of Organizations

In today's agile business ecosystems, digital twins (DTs) and especially digital twins of organizations (DTOs) allow for adaption through dynamically evolving models depicting organizational aspects such as production processes, data flows, human actors and interactions. A hybrid modelling approach is utilized, as the establishment of such DTOs either considered on their own or as part of a DT ecosystem is not trivial. Meta modelling and meta model merging patterns are applied to integrate heterogeneous perspectives and domain models. Two main research questions with respect to digitization towards digital twinning are discussed: First, which digitization principles/patterns are appropriate for DTOs? Patterns ranging from 'counting' to 'estimation' are introduced to fill digital models serving as a foundation for DTs with data. As a starting point, potential digitization principles for relevant characteristics of BPMN ­ 'Modelling Method for Business Processes' and KPI ­ 'Modelling Method for Key Performance Indicators' models are considered. Second, which principle/pattern is appropriate for which organizational structure? In order to ease the selection of suitable patterns for specific application scenarios, those will be associated with organizational structures like but not limited to construction processes, assembly processes or production processes each of them with domain-specific characteristics. A prototype consisting of three phases ­ use case requirements collection, model design and digitization assistance ­ builds upon (a) physical experimentations in the OMiLAB Innovation Corner using physical assets such as edge devices or sensors, (b) domain specific services considering software related aspects such as timeseries databases or simulation algorithms, and (c) modelling methods enabling the integration of physical and digital components. The paint production pilot from the European Change2Twin project serves as an application scenario evaluation use case. A notion of what the use case company intends to achieve by digital twinning and what is possible by introducing digital services is touched. The outlook presents how artificial intelligence may be introduced for the prototype to leverage the paint production use case and further application scenarios

Knowledge Authoring and Question Answering via Controlled Natural Language

Knowledge acquisition from text is the process of automatically acquiring, organizing and structuring knowledge from text which can be used to perform question answering or complex reasoning. However, current state-of-the-art systems are limited by the fact that they are not able to construct the knowledge base with high quality as knowledge representation and reasoning (KRR) has a keen requirement for the accuracy of data. Controlled Natural Languages (CNLs) emerged as a technology to author knowledge using a restricted subset of English. However, they still fail to do so as sentences that express the same information may be represented by different forms. Current CNL systems have limited power to standardize sentences that express the same meaning into the same logical form. We solved this problem by building the Knowledge Authoring Logic Machine (KALM), which is a technology for domain experts who are not familiar with logic to author knowledge using CNL. The system performs semantic analysis of English sentences and achieves superior accuracy of standardizing sentences that express the same meaning to the same logical representation. Besides, we developed the query part of KALM to perform question answering, which also achieves very high accuracy in query understanding

Clustering of Bi-Dimensional and Heterogeneous Times Series: Application to Social Sciences Data

We present an application of bi-dimensional and heterogeneous time series clustering in order to resolve a Social Sciences study issue. The dataset is the result of a survey involving more than eight thousand handicapped persons. Sociologists need to know if there are in this dataset some homogeneous classes of people according to two attributes: the idea that handicapped people have about the quality of their life and their couple status (i.e. if they have a partner or not). These two attributes are time series so we had to adapt the k-Means clustering algorithm in order to be efficient with this kind of data. For this purpose, we use the Longest Common Subsequence time series distance for its efficiency to manage time stretching and we extend it to the bidimensional and heterogeneous case. The results of our unsupervised process give some pertinent and surprising clusters that can be easily analyzed by sociologists.Présentation d'une application d'un "bi-dimensional and heterogeneous time series clustering" pour résoudre un problème en sciences sociales. Les données concernent plus de huit mille personnes en situation de handicap. Le problème est de savoir s'il existe de groupes homogènes vis-à-vis de la qualité de vie ressentie et de la vie de couple déclarée. A ces deux séries temporelles, un algorithme de k-Means clustering a été adapté. Nous avons utilisé the Longest Common Subsequence time series distance et nous l'avons étendue au cas bi-dimensionnel et hétérogène. Le résultat a été pertinent et surprenant, utile à l'analyse sociologique

Semantic Modelling of e-Solutions Using a View Formalism with Conceptual and Logical Extensions

In industrial informatics, there exists a requirement to model and design views at a higher level of abstraction. Since the classical view definitions are only available at the query or instance level, modelling and maintaining such views for complex enterprise information systems (EIS) is a challenging task. Further, the introduction of semi-structured data (namely XML) and its rapid adaptation by the commercial and industrial systems increased the complexity for view design and specification. To address such and issue, in this paper we present; (a) a layered view model for XML, (b) a design methodology for such views and (c) some real-world industrial applications of the view model. The XML view formalism is defined at the conceptual level and the design methodology is based on the XML semantic (XSemantic) nets, a high-level object-oriented (OO) modelling language for XML domains

Achieving High Quality Knowledge Acquisition using Controlled Natural Language

Controlled Natural Languages (CNLs) are efficient languages for knowledge acquisition and reasoning. They are designed as a subset of natural languages with restricted grammar while being highly expressive. CNLs are designed to be automatically translated into logical representations, which can be fed into rule engines for query and reasoning. In this work, we build a knowledge acquisition machine, called KAM, that extends Attempto Controlled English (ACE) and achieves three goals. First, KAM can identify CNL sentences that correspond to the same logical representation but expressed in various syntactical forms. Second, KAM provides a graphical user interface (GUI) that allows users to disambiguate the knowledge acquired from text and incorporates user feedback to improve knowledge acquisition quality. Third, KAM uses a paraconsistent logical framework to encode CNL sentences in order to achieve reasoning in the presence of inconsistent knowledge

A Global Perspective of Privacy Protection Practices

The global economy of today, boosted by propagation of e-commerce, has elevated the privacy and security issues to a worldwide platform. E-commerce growth is strongest in the US and the European Union. Recently India and China have also become significant players in the global commercial setting. This research is exploratory in nature and attempts to examine privacy protection practices in the United States, Europe, India and China. The results indicate that information privacy protection practices are prevalent in the USA. On the other side of the spectrum is China with a completely different view of personal privacy reflecting the nations’ treatment of information privacy