Using Ontologies for the Design of Data Warehouses

Obtaining an implementation of a data warehouse is a complex task that forces designers to acquire wide knowledge of the domain, thus requiring a high level of expertise and becoming it a prone-to-fail task. Based on our experience, we have detected a set of situations we have faced up with in real-world projects in which we believe that the use of ontologies will improve several aspects of the design of data warehouses. The aim of this article is to describe several shortcomings of current data warehouse design approaches and discuss the benefit of using ontologies to overcome them. This work is a starting point for discussing the convenience of using ontologies in data warehouse design.Comment: 15 pages, 2 figure

Conceptual Modeling Applied to Data Semantics

In software system design, one of the purposes of diagrammatic modeling is to explain something (e.g., data tables) to others. Very often, syntax of diagrams is specified while the intended meaning of diagrammatic constructs remains intuitive and approximate. Conceptual modeling has been developed to capture concepts and their interactions with each other in the intended domain and to represent structural and behavioral features of the modeled system. This paper is a venture into diagrammatic approaches to the semantics of modeling notations, with a focus on data and graph semantics. The first decade of the new millennium has seen several new world-changing businesses spring to life (e.g., Google and Twitter), that have put connected data at the center of their trade. Harnessing such data requires significant effort and expertise, and it quickly becomes prohibitively expensive. One solution involves building graph-based data models, which is a challenging problem. In many applications, the utilized software is managing not just objects as well as isolated and discrete data items but also the connections between them. Data semantics is a key ingredient to construct a model that explicitly describes the relationships between data objects. In this paper, we claim that current ad hoc graphs that attempt to provide semantics to data structures (e.g., relational tables and tabular SQL) are problematic. These graphs mix static abstract concepts with dynamic specification of objects (particulars). Such a claim is supported by analysis that applies the thinging machine (TM) model to provide diagrammatic representations of data (e.g., Neo4J graphs). The study s results show that to take advantage of graph algorithms and simultaneously achieve appropriate data semantics, the data graphs should be developed as simplified forms of TM.Comment: 12 pages, 27 figure

Using philosophy to improve the coherence and interoperability of applications ontologies: A field report on the collaboration of IFOMIS and L&C

The collaboration of Language and Computing nv (L&C) and the Institute for Formal Ontology and Medical Information Science (IFOMIS) is guided by the hypothesis that quality constraints on ontologies for software ap-plication purposes closely parallel the constraints salient to the design of sound philosophical theories. The extent of this parallel has been poorly appreciated in the informatics community, and it turns out that importing the benefits of phi-losophical insight and methodology into application domains yields a variety of improvements. L&C’s LinKBase® is one of the world’s largest medical domain ontologies. Its current primary use pertains to natural language processing ap-plications, but it also supports intelligent navigation through a range of struc-tured medical and bioinformatics information resources, such as SNOMED-CT, Swiss-Prot, and the Gene Ontology (GO). In this report we discuss how and why philosophical methods improve both the internal coherence of LinKBase®, and its capacity to serve as a translation hub, improving the interoperability of the ontologies through which it navigates

Towards a clinical trial ontology using a concern-oriented approach

Not yet availablePer ridurre i costi e migliorare la qualita\u27 della ricerca nei trial clinici (CT) e\u27 necessario un approccio piu\u27 sistematico all\u27automazione dei CT per rinforzare l\u27interoperabilita\u27 a vari livelli del processo di ricerca. Per questo scopo e\u27 stato sviluppato un modello concettuale di CT. Alla base di ogni approccio di modellizzazione ci sono criteri di partizione che ci permettono di dominare la complessita\u27 dell\u27universo da modellare. In questo rapporto noi introduciamo un metodo originale di analisi basato sui concern degli stakeholder per partizionare il domino concettuale dei CT in sotto-domini orientati agli stakeholder. Le rappresentazioni mentali degli stakeholder relative a ciascun concern sono identificati come cluster di concetti collegati ad altri concetti. Noi consideriamo ciascun cluster come una base razionale per il relativo concern. I concetti trovati nelle basi razionali popolano l\u27universo del discorso specifico per ogni stakeholder e compongono il vocabolario degli stakeholder. Alcuni concetti sono condivisi con altri stakeholder, mentre altri sono specifici di uno stakehoder; alcuni concetti sono specifici dei CT, mentre altri sono concetti medici o generali. In questo modo un\u27ontologia orientata ai concern per i CT puo\u27 essere creata. Il metodo e\u27 illustrato utilizzando i criteri di selezione dei soggetti, una componente di un progetto di CT, ma puo\u27 essere usato per ogni altra componente del protocollo del CT. La tassonomia del vocabolario dei concetti dei CT e la rete delle relative basi razionali ci fornisce una struttura possibile per lo sviluppo del software specialmente se si adotta una soluzione basata su architetture orientate ai servizi

Ontologies for Industry 4.0

The current fourth industrial revolution, or ‘Industry 4.0’ (I4.0), is driven by digital data, connectivity, and cyber systems, and it has the potential to create impressive/new business opportunities. With the arrival of I4.0, the scenario of various intelligent systems interacting reliably and securely with each other becomes a reality which technical systems need to address. One major aspect of I4.0 is to adopt a coherent approach for the semantic communication in between multiple intelligent systems, which include human and artificial (software or hardware) agents. For this purpose, ontologies can provide the solution by formalizing the smart manufacturing knowledge in an interoperable way. Hence, this paper presents the few existing ontologies for I4.0, along with the current state of the standardization effort in the factory 4.0 domain and examples of real-world scenarios for I4.0.Peer ReviewedPostprint (published version