Domain ontology usage analysis framework

The Semantic Web (also known as Web of Data) is growing fast and becoming a decentralized knowledge platform for publishing and sharing information. The web ontologies promote the establishment of a shared understanding between data providers and data consumers, allowing for automated information processing and effective and efficient information retrieval. The majority of existing research efforts is focused around ontology engineering, ontology evaluation and ontology evolution. This work goes a step further and evaluates theontology usage. In this paper, we present an Ontology Usage Analysis Framework (OUSAF) and a set of metrics used to measure the ontology usage. The implementation of the proposed framework is illustrated using the example of GoodRelations ontology (GRO). GRO has been well adopted by the semantic ecommerce community, and the OUSAF approach has been used to analyse GRO usage in the dataset comprised of RDF data collected from the web

A semantic framework for ontology usage analysis

The Semantic Web envisions a Web where information is accessible and processable by computers as well as humans. Ontologies are the cornerstones for realizing this vision of the Semantic Web by capturing domain knowledge by defining the terms and the relationship between these terms to provide a formal representation of the domain with machine-understandable semantics. Ontologies are used for semantic annotation, data interoperability and knowledge assimilation and dissemination.In the literature, different approaches have been proposed to build and evolve ontologies, but in addition to these, one more important concept needs to be considered in the ontology lifecycle, that is, its usage. Measuring the “usage” of ontologies will help us to effectively and efficiently make use of semantically annotated structured data published on the Web (formalized knowledge published on the Web), improve the state of ontology adoption and reusability, provide a usage-based feedback loop to the ontology maintenance process for a pragmatic conceptual model update, and source information accurately and automatically which can then be utilized in the other different areas of the ontology lifecycle. Ontology Usage Analysis is the area which evaluates, measures and analyses the use of ontologies on the Web. However, in spite of its importance, no formal approach is present in the literature which focuses on measuring the use of ontologies on the Web. This is in contrast to the approaches proposed in the literature on the other concepts of the ontology lifecycle, such as ontology development, ontology evaluation and ontology evolution. So, to address this gap, this thesis is an effort in such a direction to assess, analyse and represent the use of ontologies on the Web.In order to address the problem and realize the abovementioned benefits, an Ontology Usage Analysis Framework (OUSAF) is presented. The OUSAF Framework implements a methodological approach which is comprised of identification, investigation, representation and utilization phases. These phases provide a complete solution for usage analysis by allowing users to identify the key ontologies, and investigate, represent and utilize usage analysis results. Various computation components with several methods, techniques, and metrics for each phase are presented and evaluated using the Semantic Web data crawled from the Web. For the dissemination of ontology-usage-related information accessible to machines and humans, The U Ontology is presented to formalize the conceptual model of the ontology usage domain. The evaluation of the framework, solution components, methods, and a formalized conceptual model is presented, indicating the usefulness of the overall proposed solution

Discovering the Impact of Knowledge in Recommender Systems: A Comparative Study

Recommender systems engage user profiles and appropriate filtering techniques to assist users in finding more relevant information over the large volume of information. User profiles play an important role in the success of recommendation process since they model and represent the actual user needs. However, a comprehensive literature review of recommender systems has demonstrated no concrete study on the role and impact of knowledge in user profiling and filtering approache. In this paper, we review the most prominent recommender systems in the literature and examine the impression of knowledge extracted from different sources. We then come up with this finding that semantic information from the user context has substantial impact on the performance of knowledge based recommender systems. Finally, some new clues for improvement the knowledge-based profiles have been proposed.Comment: 14 pages, 3 tables; International Journal of Computer Science & Engineering Survey (IJCSES) Vol.2, No.3, August 201

Utilising semantic technologies for decision support in dementia care

The main objective of this work is to discuss our experience in utilising semantic technologies for building decision support in Dementia care systems that are based on the non-intrusive on the non-intrusive monitoring of the patient’s behaviour. Our approach adopts context-aware modelling of the patient’s condition to facilitate the analysis of the patient’s behaviour within the inhabited environment (movement and room occupancy patterns, use of equipment, etc.) with reference to the semantic knowledge about the patient’s condition (history of present of illness, dependable behaviour patterns, etc.). The reported work especially focuses on the critical role of the semantic reasoning engine in inferring medical advice, and by means of practical experimentation and critical analysis suggests important findings related to the methodology of deploying the appropriate semantic rules systems, and the dynamics of the efficient utilisation of complex event processing technology in order to the meet the requirements of decision support for remote healthcare systems

The role of linked data and the semantic web in building operation

Effective Decision Support Systems (DSS) for building service managers require adequate performance data from many building data silos in order to deliver a complete view of building performance. Current performance analysis techniques tend to focus on a limited number of data sources, such as BMS measured data (temperature, humidity, C02), excluding a wealth of other data sources increasingly available in the modern building, including weather data, occupant feedback, mobile sensors & feedback systems, schedule information, equipment usage information. This paper investigates the potential for using Linked Data and Semantic Web technologies to improve interoperability across AEC domains, overcoming many of the roadblocks hindering information transfer currently

A core ontology for business process analysis

Business Process Management (BPM) aims at supporting the whole life-cycle necessary to deploy and maintain business processes in organisations. An important step of the BPM life-cycle is the analysis of the processes deployed in companies. However, the degree of automation currently achieved cannot support the level of adaptation required by businesses. Initial steps have been performed towards including some sort of automated reasoning within Business Process Analysis (BPA) but this is typically limited to using taxonomies. We present a core ontology aimed at enhancing the state of the art in BPA. The ontology builds upon a Time Ontology and is structured around the process, resource, and object perspectives as typically adopted when analysing business processes. The ontology has been extended and validated by means of an Events Ontology and an Events Analysis Ontology aimed at capturing the audit trails generated by Process-Aware Information Systems and deriving additional knowledge

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements