Pragmatic Ontology Evolution: Reconciling User Requirements and Application Performance

Increasingly, organizations are adopting ontologies to describe their large catalogues of items. These ontologies need to evolve regularly in response to changes in the domain and the emergence of new requirements. An important step of this process is the selection of candidate concepts to include in the new version of the ontology. This operation needs to take into account a variety of factors and in particular reconcile user requirements and application performance. Current ontology evolution methods focus either on ranking concepts according to their relevance or on preserving compatibility with existing applications. However, they do not take in consideration the impact of the ontology evolution process on the performance of computational tasks – e.g., in this work we focus on instance tagging, similarity computation, generation of recommendations, and data clustering. In this paper, we propose the Pragmatic Ontology Evolution (POE) framework, a novel approach for selecting from a group of candidates a set of concepts able to produce a new version of a given ontology that i) is consistent with the a set of user requirements (e.g., max number of concepts in the ontology), ii) is parametrised with respect to a number of dimensions (e.g., topological considerations), and iii) effectively supports relevant computational tasks. Our approach also supports users in navigating the space of possible solutions by showing how certain choices, such as limiting the number of concepts or privileging trendy concepts rather than historical ones, would reflect on the application performance. An evaluation of POE on the real-world scenario of the evolving Springer Nature taxonomy for editorial classification yielded excellent results, demonstrating a significant improvement over alternative approaches

The elicitation of key performance indicators of e-government providers: A bottom-up approach

Copyright @ 2013 EMCIS.Delivering an adequate e-Government service (e-service) is becoming more of a necessity in today's digital world. In order to improve e-services and increase the engagement of both users' and providers' side, studies on the performance evaluation of such provided e-services are taking places. However a clear identification of the key performance indicators from the e-Government providers’ side is not well explored. This shortcoming hampers the conduct of a holistic evaluation of an e-service provision from the perspective of its stakeholders in order to improve e-services as well as to increase e-services take-ups. In this paper, a systematic process to identify indicators is implemented based on a bottom-up approach. The process used three focus-group meetings with providers, users, and academics in Qatar, Lebanon and UK to collect, identify and validate key indicators from the perspective of e-services’ providers. The approach resulted in the identification of five factors levels (service, technology, employees, policy and management and social responsibilities) with fifteen sub-categories of SMART variables. Hence, leading to the development of a new model, STEPS, that can fully explain and predict e-government success from the providers’ point of view. It will work as a strategic management tool to align various stakeholders on common goal and values based on evidence based evaluation of e-services using smart measurable indicators for the improvement of an e-service at the engagement level in the field of e-government. In addition, other fields can benefit from the outcome of this work, such as logistics service providers, who make their services available across new and existing relationships between the Internet commerce firms, their customers, and their vendors

Knowledge Base Evolution Analysis: A Case Study in the Tourism Domain

Stakeholders -- curator, consumer, etc. -- in the tourism domain routinely need to combine and compare statistical indicators about tourism. In this context, various Knowledge Bases (KBs) have been designed and developed in the Linked Open Data (LOD) cloud in order to support decision-making process in Tourism domain. Such KBs evolve over time: their data (instances) and schemes can be updated, extended, revised and refactored. However, unlike in more controlled types of knowledge bases, the evolution of KBs exposed in the LOD cloud is usually unrestrained, what may cause data to suffer from a variety of issues. This paper attempts to address the impact of KB evolution in tourism domain by showing how entity evolves over time using the 3cixty KB. We show that using multiple versions of the KB through time can help to understand inconsistency in the data collection process

Maintaining a Linked Data Cloud and Data Service for Second World War History

One of the great promises of Linked Data is to provide a shared data infrastructure into which new data can be imported and aligned with, forming a sustainable, ever growing Linked Data Cloud (LDC). This paper studies and evaluates this idea in the context of the WarSampo LDC that provides a data infrastructure for Second World War related ontologies and data in Finland, including several mutually linked graphs, totaling ca 12 million triples. Two data integration case studies are presented, where the original WarSampo LDC and the related semantic portal were first extended by a dataset of hundreds of war cemeteries and thousands of photographs of them, and then by another dataset of over 4450 Finnish prisoners of war. As a conclusion, lessons learned are explicated, based on hands-on experience in maintaining the WarSampo LDC in a production environment.Peer reviewe

The role preparedness of the Kenyan Pharmacist in Health Economics

Ontologies are one of the most used representations to model the domain knowledge. An ontology consists of a set of concepts connected by semantic relations. The construction and evolution of an ontology are complex and time-consuming tasks. This paper presents DYNAMO-MAS, an Adaptive Multi-Agent System (AMAS) that automates these tasks by co-constructing an ontology from texts with an ontologist. Terms and concepts of a given domain are agentified and they act, according to the AMAS approach, by solving the non cooperative situations they locally perceive at runtime. These agents cooperate to determine their position in the AMAS (that is the ontology) thanks to (i) lexical relations between terms, (ii) some adaptive mechanisms enabling addition, removing or moving of new terms, of concepts and of relations in the ontology as well as (iii) feedbacks from the ontologist about the propositions given by the AMAS. This paper focuses on the instantiation of the AMAS approach to this difficult problem. It presents the architecture of DYNAMO-MAS, and details the cooperative behaviors of the two types of agents we defined for ontology evolution. Finally evaluations made on three different ontologies are given in order to show the genericity of our solution