PADTUN - using semantic technologies in tunnel diagnosis and maintenance domain

YesA Decision Support System (DSS) in tunnelling domain deals with identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key area in diagnosing pathologies is to identify regions of interest (ROI). In practice, tunnel experts intuitively abstract regions of interest by selecting tunnel portions that are susceptible to the same types of pathologies with some distance approximation. This complex diagnosis process is often subjective and poorly scales across cases and transport structures. In this paper, we introduce PADTUN system, a working prototype of a DSS in tunnelling domain using semantic technologies. Ontologies are developed and used to capture tacit knowledge from tunnel experts. Tunnel inspection data are annotated with ontologies to take advantage of inferring capabilities offered by semantic technologies. In addition, an intelligent mechanism is developed to exploit abstraction and inference capabilities to identify ROI. PADTUN is developed in real-world settings offered by the NeTTUN EU Project and is applied in a tunnel diagnosis use case with Société Nationale des Chemins de Fer Français (SNCF), France. We show how the use of semantic technologies allows addressing the complex issues of pathology and ROI inferencing and matching experts’ expectations of decision support

An ontological approach for pathology assessment and diagnosis of tunnels

Tunnel maintenance requires complex decision making, which involves pathology diagnosis and risk assessment, to ensure full safety while optimising maintenance and repair costs. A Decision Support System (DSS) can play a key role in this process by supporting the decision makers in identifying pathologies based on disorders present in various tunnel portions and contextual factors affecting a tunnel. Another key aspect is to identify which spatial stretches within a tunnel contain pathologies of similar kinds within neighbouring tunnel segments. This paper presents PADTUN, a novel intelligent decision support system that assists with pathology diagnosis and assessment of tunnels with respect to their disorders and diagnosis influencing factors. It utilises semantic web technologies for knowledge capture, representation, and reasoning. The core of PADTUN is a family of ontologies which represent the main concepts and relations associated with pathology assessment, and capture the decision process concerning tunnel maintenance. Tunnel inspection data is linked to these ontologies to take advantage of inference capabilities offered by semantic technologies. In addition, an intelligent mechanism is presented which exploits abstraction and inference capabilities. Thus PADTUN provides the world’s first semantically based intelligent DSS for tunnel maintenance. PADTUN was developed by an interdisciplinary team of tunnel experts and knowledge engineers in real-world settings offered by the NeTTUN EU Project. An evaluation of the PADTUN system is performed using real-world tunnel data and diagnosis tasks. We show how the use of semantic technologies allows addressing the complex issues of tunnel pathology inferencing, aiding in, and matching transportation experts’ expectations of decision support. The methodology is applicable to any linear transport structures, offering intelligent ways to aid with complex decision processes related to diagnosis and maintenance

User Interaction with Linked Data: An Exploratory Search Approach

NoIt is becoming increasingly popular to expose government and citywide sensor data as linked data. Linked data appears to offer a great potential for exploratory search in supporting smart city goals of helping users to learn and make sense of complex and heterogeneous data. However, there are no systematic user studies to provide an insight of how browsing through linked data can support exploratory search. This paper presents a user study that draws on methodological and empirical underpinning from relevant exploratory search studies. The authors have developed a linked data browser that provides an interface for user browsing through several datasets linked via domain ontologies. In a systematic study that is qualitative and exploratory in nature, they have been able to get an insight on central issues related to exploratory search and browsing through linked data. The study identifies obstacles and challenges related to exploratory search using linked data and draws heuristics for future improvements. The authors also report main problems experienced by users while conducting exploratory search tasks, based on which requirements for algorithmic support to address the observed issues are elicited. The approach and lessons learnt can facilitate future work in browsing of linked data, and points at further issues that have to be addressed

Examining citizens' perceived value of internet of things technologies in facilitating public sector services engagement

YesWith the advancement of disruptive new technologies, there has been a considerable focus on personalisation as an important component in nurturing users' engagement. In the context of smart cities, Internet of Things (IoT) offer a unique opportunity to help empower citizens and improve societies' engagement with their governments at both micro and macro levels. This study aims to examine the role of perceived value of IoT in improving citizens' engagement with public services. A survey of 313 citizens in the UK, engaging in various public services, enabled through IoT, found that the perceived value of IoT is strongly influenced by empowerment, perceived usefulness and privacy related issues resulting in significantly affecting their continuous use intentions. The study offers valuable insights into the importance of perceived value of IoT-enabled services, while at the same time, providing an intersectional perspective of UK citizens towards the use of disruptive new technologies in the public sector

A knowledge-based framework for information extraction and exploration

Harnessing insights from the colossal amount of online information requires the computerised processing of unstructured text in order to satisfy the information need of particular applications such as recommender systems and sentiment analysis. The increasing availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from Web data. In this thesis, a novel comprehensive knowledge-based framework is proposed to construct and exploit a domain-specific semantic knowledgebase. The proposed framework introduces a methodology for linking several components of different techniques and tools. It focuses on providing reusable and configurable data and application templates, which allow developers to apply it in diversity of domains. The objectives of this framework are: extracting information from unstructured data, constructing a semantic knowledgebase from the extracted information, enriching the resultant semantic knowledgebase by sourcing appropriate semi-structured and structured datasets, and consuming the resultant semantic knowledgebase to facilitate the intelligent exploration and search of information. For the purpose of investigating the challenges of extracting and modelling information in a specific domain, the financial domain was employed as a use-case in the context of a stock investment motivating scenario. The developed knowledge-based approach exploits the semantic and syntactic characteristics of the problem domain knowledge in implementing a hybrid approach of Rule-based and Machine Learning based relation classification. The rule-based approach is adopted in the Natural Language Processing tasks associated with linguistic and structural features, Named Entity Recognition, instances labelling and feature generation processes. The results of these tasks are used to classify the relations between the named entities by employing the Machine Learning based relation classification. In addition, the domain knowledge is analysed to benefit knowledge modelling by translating the domain key concepts into a formal ontology. This ontology is employed in constructing semantic knowledgebase from unstructured online data of a specific domain, enriching the resulting semantic knowledgebase by sourcing semi-structured and structured online data sources and applying advanced classifications and inference technologies to infer new and interesting facts to improve the decision-making and intelligent exploration activities. However, most relations are non-binary in the problem domain knowledge because of its specific characteristic hence an appropriate N-ary relation patterns technique were adopted and investigated. A serious of a novel experiments were conducted to implement and configure a Machine Learning based relation classification. The experimental evaluation evidenced that the developed knowledge-assisted ML relation classification model, which was further boosted by our implementation of GAs to reduce the feature space, has resulted in significant improvement in the process of relation extraction. The experimental results also indicate that amongst the implemented ML algorithms, SVM exhibited the best relation classification accuracy in the majority of the training datasets, while retaining acceptable levels of accuracy in the rest in the remaining training datasets. Web Ontology Language (OWL) reasoning and rule-based reasoning on the resultant semantic knowledgebase were applied to derive stock investment specific recommendations. In addition, SPARQL query language was employed to explore the semantic knowledgebase. Moreover, taking into consideration the problem domain's requirements for modelling non-binary relations, a relation-as-class N-ary relations pattern was implemented, and the reasoning axioms and query language were adjusted to fit the intermediate resources in the N-ary relations requirements. In this thesis also the experience on addressing the challenges of implementing the proposed knowledge-based framework for constructing and exploiting a semantic knowledgebase were summarised. These challenges can be considered by domain experts and knowledge engineers as a novel methodology for employing the Semantic Web Technologies for the knowledge user to intelligently exploit knowledge in similar problem domains. The evaluation of knowledge accessibility by utilising Semantic Web Technologies in the developed application includes the ability of data retrieval to obtain either the entire or some portion of the data from the semantic knowledgebase for a particular use-case scenario. Investigating the tasks of reasoning, accessing and querying the semantic knowledgebase evidences that Semantic Web Technologies can perform an accurate and complex knowledge representation to share Knowledge from a diversity of data sources and, improve the decision‑making process and the intelligent exploration of the semantic knowledgebase

From Data Modeling to Knowledge Engineering in Space System Design

The technologies currently employed for modeling complex systems, such as aircraft, spacecraft, or infrastructures, are sufficient for system description, but do not allow deriving knowledge about the modeled systems. This work provides the means to describe space systems in a way that allows automating activities such as deriving knowledge about critical parts of the system’s design, evaluation of test success, and identification of single points of failure