SSEO 2015 Abstracts

Full Papers
Paper Nr: 4

Does ‘Merging DEMO Models’ Satisfy the Associative Law? - Validation of Partial Models and Merge Operation


Tetsuya Suga and Junichi Iijima

Abstract: Partial models are small models produced by splitting a large full model into meaningful conceivable-sized fragments with each separate diagram. They are commonly used when the full model is too large and shared by several people who have their own scope of interest. Those partial models are subject to being manipulated— merged for instance. This context calls for discussion in Enterprise Ontology (EO) about the capability of business process modeling languages in handling partial models and manipulations on them. There, indeed, exists a lack of researches in the methodology of EO, namely Design & Engineering Methodology for Organizations (DEMO) for formal studies on its consistency in producing partial models and merging them. It stems from a deficiency of formal semantics in the specification of the notation. By formalizing the DEMO Construction Model (CM) with a concept of well-formed models and the merge operation from a set-theoretic approach, this paper clarifies that the closedness, commutativity, and associativity are guaranteed in merging partial models of DEMO CM. An example of EU-Rent accompanies the formalizations for validation and demonstration.

Paper Nr: 6

Learning Non-taxonomic Relationships of Financial Ontology


Omar El Idrissi Esserhrouchni, Bouchra Frikh and Brahim Ouhbi

Abstract: Finance ontology is, in most cases, manually addressed. This results in a tedious development process and error prone that delay their applicability. This is why there is a need of domain ontology learning methods that built the ontology automatically and without human intervention. However, in this learning process, the discovery of non-taxonomic relationships has been recognized as one of the most difficult problems. In this paper, we propose a new methodology for learning non-taxonomic relationships and building financial ontology from scratch. Our new technique is based on using and adapting Open Information Extraction algorithms to extract and label domain relations between concepts. To evaluate our new method effectiveness, we compare the extracted non-taxonomic relations of our algorithm with related works in the same finance corpus. The results showed that our system is more accurate and more effective.

Short Papers
Paper Nr: 1

Using DEMO to Objectify Metamodel Evolution


Nuno Silva, José Tribolet, Miguel Mira da Silva and Carlos Mendes

Abstract: A metamodel is an important aspect of defining a modeling language. It specifies the language’s syntax through a set of constructs as well as how the language models are ought to be composed. Modeling languages, and thus their metamodels, are subject of constant evolution due to changing language requirements as consequence of business changes. Therefore, perceiving the essential aspects responsible for altering the structure of metamodels when a change requirement arises can become an issue. The Enterprise Ontology theory and its methodology (DEMO) provide ontological knowledge about organizations resulting in organizational self-awareness. Applying this methodology to the context of metamodeling can be a starting point for uncovering the essential aspects, i.e., the ontological knowledge regarding metamodel evolution. For that purpose, we modeled two diagrams using the DEMO methodology. The input for both diagrams was a set of coupled operations defined in Herrmannsdörfer’s evolutionary metamodeling research. In the end we stated the main conclusions of our work and themes for future work.

Paper Nr: 5

LimeDS and the TraPIST Project: A Case Study - An OSGi-based Ontology-enabled Framework Targeted at Developers in Need of an Agile Solution for Building REST/JSON-based Server Applications


Stijn Verstichel, Wannes Kerckhove, Thomas Dupont, Bruno Volckaert, Femke Ongenae, Filip De Turck and Piet Demeester

Abstract: Real-Time Travel Information (RTTI) for rail commuters is still used inefficiently today and is rarely combined with other knowledge to come to a truly personalised and situation-aware multimodal travelling assistance. It is up to the travellers themselves to look for important info about their trip through static schedules or dedicated non-personalised applications. In a highly dynamic context such as that of public transportation, it would make life easier if one was able to consult the right information at the right time (removing superfluous information), for a variety of multimodal public transportation options, taking into account the context of the person travelling. In this paper we present the LimeDS framework, allowing application developers to rapidly define data workflows from a variety of data sources, deploy these workflows in a scalable and resilient manner and expose results to client applications as REST endpoints. A Proof-of-Concept (PoC) shows how our proposed framework can be used to tie together different open transportation data sources in order to create highly dynamic multimodal travel assistance applications by semantically enriching the data into knowledge, checking for ontological consistency and reason over the resulting knowledge.