Ontology-based faceted semantic search with automatic sense disambiguation for bioenergy domain

WordNet is a lexicon widely known and used as an ontological resource hosting comparatively large collection of semantically interconnected words. Use of such resources produces meaningful results and improves users’ search experience through the increased precision and recall. This paper presents our facet-enabled WordNet powered semantic search work done in the context of the bioenergy domain. The main hurdle to achieving the expected result was sense disambiguation further complicated by the occasional fine-grained distinction of meanings of the terms in WordNet. To overcome this issue, this paper proposes a sense disambiguation methodology that uses bioenergy domain related ontologies (extracted from WordNet automatically), WordNet concept hierarchy and term sense rank

Model-based documentation

Knowledge acquisition is becoming an integral part of the manufacturing industries, which rely on domain experts in various phases of product life cycle including design, analysis, manufacturing, operation and maintenance. It has the potential to enable knowledge reuse, however, poorly managed knowledge can cause information loss and inefficiency. If technical documentation is managed well in the manufacturing industries, intended piece of knowledge can easily be located, used and reused for purpose and as a result, the corresponding industry can be benefited. Some examples of technical documentation are design specification, operating manual and maintenance manual. Model-based Documentation (MBD) is a documentation approach that uses model to provide structure to the data of the documents. MBD can be thought of as a way to better organize knowledge thereby knowledge identification and retrieval become easier, faster and efficient. In this paper, we propose MBD and its extension as a potential solution to overcome the issues involved in the typical technical documentation approaches

Domain-aware ontology matching

During the last years, technological advances have created new ways of communication, which have motivated governments, companies and institutions to digitalise the data they have in order to make it accessible and transferable to other people. Despite the millions of digital resources that are currently available, their diversity and heterogeneous knowledge representation make complex the process of exchanging information automatically. Nowadays, the way of tackling this heterogeneity is by applying ontology matching techniques with the aim of finding correspondences between the elements represented in different resources. These approaches work well in some cases, but in scenarios when there are resources from many different areas of expertise (e.g. emergency response) or when the knowledge represented is very specialised (e.g. medical domain), their performance drops because matchers cannot find correspondences or find incorrect ones. In our research, we have focused on tackling these problems by allowing matchers to take advantage of domain-knowledge. Firstly, we present an innovative perspective for dealing with domain-knowledge by considering three different dimensions (specificity - degree of specialisation -, linguistic structure - the role of lexicon and grammar -, and type of knowledge resource - regarding generation methodologies). Secondly, domain-resources are classified according to the combination of these three dimensions. Finally, there are proposed several approaches that exploit each dimension of domain-knowledge for enhancing matchers’ performance. The proposals have been evaluated by matching two of the most used classifications of diseases (ICD-10 and DSM-5), and the results show that matchers considerably improve their performance in terms of f-measure. The research detailed in this thesis can be used as a starting point to delve into the area of domain-knowledge matching. For this reason, we have also included several research lines that can be followed in the future to enhance the proposed approaches

Adaptive and Reactive Rich Internet Applications

In this thesis we present the client-side approach of Adaptive and Reactive Rich Internet Applications as the main result of our research into how to bring in time adaptivity to Rich Internet Applications. Our approach leverages previous work on adaptive hypermedia, event processing and other research disciplines. We present a holistic framework covering the design-time as well as the runtime aspects of Adaptive and Reactive Rich Internet Applications focusing especially on the run-time aspects

Using microalgae in the circular economy to valorise anaerobic digestate::Challenges and Opportunities

Managing organic waste streams is a major challenge for the agricultural industry. Anaerobic digestion (AD) of organicwastes is a preferred option in the waste management hierarchy, as this processcangenerate renewableenergy, reduce emissions from wastestorage, andproduce fertiliser material.However, Nitrate Vulnerable Zone legislation and seasonal restrictions can limit the use of digestate on agricultural land. In this paper we demonstrate the potential of cultivating microalgae on digestate as a feedstock, either directlyafter dilution, or indirectlyfromeffluent remaining after biofertiliser extraction. Resultant microalgal biomass can then be used to produce livestock feed, biofuel or for higher value bio-products. The approach could mitigate for possible regional excesses, and substitute conventional high-impactproducts with bio-resources, enhancing sustainability withinacircular economy. Recycling nutrients from digestate with algal technology is at an early stage. We present and discuss challenges and opportunities associated with developing this new technology

An evaluation of the challenges of Multilingualism in Data Warehouse development

In this paper we discuss Business Intelligence and define what is meant by support for Multilingualism in a Business Intelligence reporting context. We identify support for Multilingualism as a challenging issue which has implications for data warehouse design and reporting performance. Data warehouses are a core component of most Business Intelligence systems and the star schema is the approach most widely used to develop data warehouses and dimensional Data Marts. We discuss the way in which Multilingualism can be supported in the Star Schema and identify that current approaches have serious limitations which include data redundancy and data manipulation, performance and maintenance issues. We propose a new approach to enable the optimal application of multilingualism in Business Intelligence. The proposed approach was found to produce satisfactory results when used in a proof-of-concept environment. Future work will include testing the approach in an enterprise environmen

Artificial Intelligence in Landscape Architecture: A Survey of Theory, Culture, and Practice

This dissertation explores the role of artificial intelligence (AI) in shaping the landscape architecture profession. It looks at how AI has evolved in the field, its current influence, and its potential to change research, teaching, and professional practice. The research includes a detailed review of existing literature to identify trends in AI applications and gaps in knowledge. It also examines landscape architects\u27 attitudes towards AI, revealing a mix of enthusiasm for its benefits and concerns about its impact on creativity and design processes, and proposes new ways of thinking about and working with AI. The work brings a unique perspective on AI in the field and gives valuable insights for future research and practice