In this paper, we describe the fully dynamic semantic portal we implemented, integrating Semantic Web technologies and Service Oriented Architecture (SOA). The goals of the portal are twofold: first it helps administrators to easily propose new features in the portal using semantics to ease the orchestration process; secondly it automatically generates a customized user interface for these scenarios. This user interface takes into account different devices and assists end-users in the use of the portal taking benefit of context awareness. All the added-value of this portal is based on a core semantics defined by an ontology. We present here the main features of this portal and how it was implemented using state-of-the-art technologies and frameworks.

Master data refers to core business entities a company uses repeatedly across many business processes and systems (such as lists or hierarchies of customers, suppliers, accounts, products, or organizational units). Product information is the most important kind of master data and product information management (PIM) is becoming critical for modern enterprises because it provides a rich business context for various applications. Existing PIM systems are less flexible and scalable for on-demand business, as well as too weak to completely capture and use the semantics of master data. This paper explores how to use semantic web technologies to enhance a collaborative PIM system by simplifying modeling and representation while preserving enough dynamic flexibility. Furthermore, we build a semantic PIM system using one of the state-of-art ontology repositories and summarize the challenges we encountered based on our experimental results, especially on performance and scalability. We believe that our study and experiences are valuable for both semantic web community and master data management community.

In this paper, we particularly focused our attention on how to enhance expressivity of ontologies when used as organized space values in a catalogue request process. Using the Wishbone engine [Method for a data-driven adjustement of queries over a database and system for implementing the method], we describe our new contribution by using a semantic graph instead of a tree for the solution set clustering step. We perform a dynamic transformation in order to enhance the intension meaning, and implement a linear programming clustering algorithm. We also expose a rating system in order to evaluate our clustering process quality.