Towards aligning IoT data with domain-specific ontologies through Semantic Web technologies and NLP

Internet of Things (IoT) data has the potential to be utilized in many domain-specific applications to enable smart sensing in areas that were not initially covered during the conceptualization phase of these applications. Typically, data collected in IoT scenarios serve a specific purpose and follow heterogeneous data models and domain-specific ontologies. Therefore, IoT data could not easily be integrated into domain-specific applications, as it requires ontology alignment of diverse data models with the end application. This poses a big challenge to semantic interoperability during the integration of IoT data into a pre-established system. In this line, the alignment process is cumbersome and challenging for an ontology engineer, since it requires a manual review of the relevant ontologies that could be aligned with the IoT data. Additionally, before aligning each term used in the IoT data with the concepts defined in the domain-specific ontologies, all similar/related terms in the given ontologies must be considered. In this paper, we propose a solution that supports the alignment process by utilizing semantic web technologies and Natural Language Processing (NLP). Our novel solution proposes an NLP-based term alignment with a similarity score that supports identifying the relevant terms used in IoT data and ontologies and stores the similarity scores among terms based on different similarity algorithms. We showcase our solution by aligning IoT sensor data with the water and IoT domain ontologies

Ontology-Based Cloud Manufacturing Framework in Industrialized Construction

Cloud manufacturing is an emerging manufacturing paradigm to enable rapid production for mass customization. Industrialized construction shares a similar production environment with manufacturing products, so it has a great potential to utilize the paradigm. Previous studies never examined cloud manufacturing in the construction context. This work takes the industrial difference into account and proposes a cloud manufacturing framework by ontology modeling. Three ontologies, including ifcOWL, OPW, and OWL-S, are linked to support the design to the manufacturing process of a building project. The framework benefits the design data and manufacturing data integration, and enhances the resource sharing by semantic web service

Utilisation of Open Intent Recognition Models for Customer Support Intent Detection

Businesses have sought out new solutions to provide support and improve customer satisfaction as more products and services have become interconnected digitally. There is an inherent need for businesses to provide or outsource fast, efficient and knowledgeable support to remain competitive. Support solutions are also advancing with technologies, including use of social media, Artificial Intelligence (AI), Machine Learning (ML) and remote device connectivity to better support customers. Customer support operators are trained to utilise these technologies to provide better customer outreach and support for clients in remote areas. Interconnectivity of products and support systems provide businesses with potential international clients to expand their product market and business scale. This paper reports the possible AI applications in customer support, done in collaboration with the Knowledge Transfer Partnership (KTP) program between Birmingham City University and a company that handles customer service systems for businesses outsourcing customer support across a wide variety of business sectors. This study explored several approaches to accurately predict customers' intent using both labelled and unlabelled textual data. While some approaches showed promise in specific datasets, the search for a single, universally applicable approach continues. The development of separate pipelines for intent detection and discovery has led to improved accuracy rates in detecting known intents, while further work is required to improve the accuracy of intent discovery for unknown intents

A review of natural language processing in contact centre automation

Contact centres have been highly valued by organizations for a long time. However, the COVID-19 pandemic has highlighted their critical importance in ensuring business continuity, economic activity, and quality customer support. The pandemic has led to an increase in customer inquiries related to payment extensions, cancellations, and stock inquiries, each with varying degrees of urgency. To address this challenge, organizations have taken the opportunity to re-evaluate the function of contact centres and explore innovative solutions. Next-generation platforms that incorporate machine learning techniques and natural language processing, such as self-service voice portals and chatbots, are being implemented to enhance customer service. These platforms offer robust features that equip customer agents with the necessary tools to provide exceptional customer support. Through an extensive review of existing literature, this paper aims to uncover research gaps and explore the advantages of transitioning to a contact centre that utilizes natural language solutions as the norm. Additionally, we will examine the major challenges faced by contact centre organizations and offer reco

Ontology-based manufacturability analysis automation for industrialized construction

The current digital fabrication workflow requires many iterations between design and manufacturing. Automated manufacturability analysis can reduce the number of iterations at the design stage. However, existing approaches that leverage design for manufacturing and assembly (DfMA) do not consider detailed product features and production capabilities. To address this limitation, this paper utilizes an ontology-based approach to connect design and manufacturing knowledge. The developed manufacturability analysis system (MAS) involves semantic reasoning to analyze manufacturability by combining feature-based modelling, production capability modelling and manufacturing rules. The system was tested on a timber panelized project to demonstrate complex manufacturability analysis capability. The testing proves that the system could provide real-time feedback to the designers, leading to fewer design iterations. Thus, the paper is a first step towards automated fabrication-aware design and the results from the study lay the foundation for future research on connecting knowledge for interdisciplinary rule checkin

ifcOWL-DfMA a new ontology for the offsite construction domain

Architecture, Engineering and Construction (AEC) is a fragmented in-dustry dealing with heterogeneous data formats coming from different domains. Building Information Modelling (BIM) is one of the most important efforts to manage information collaboratively within the AEC industry. The Industry Foun-dation Classes (IFC) can be used as a data format to achieve data exchange be-tween diverse software applications in a BIM process. The advantage of using Semantic Web Technologies to overcome these challenges has been recognised by the AEC community and the ifcOWL ontology, which transforms the IFC schema to a Web Ontology Language (OWL) representation, is now a de facto standard. Even though the ifcOWL ontology is very extensive, there is a lack of detailed knowledge representation in terms of process and sub-processes explain-ing Design for Manufacturing and Assembly (DfMA) for offsite construction, and also a lack of knowledge on how product and productivity measurement such as production costs and durations are incurred, which is essential for evaluation of alternative DfMA design options. In this article we present a new ontology named ifcOWL-DfMA as a new domain specific module for ifcOWL with the aim of representing offsite construction domain terminology and relationships in a machine-interpretable format. This ontology will play the role of a core vocab-ulary for the DfMA design management and can be used in many scenarios such as life cycle cost estimation. To demonstrate the usage of ifcOWL-DfMA ontol-ogy a production line of wall panels is presented. We evaluate our approach by querying the wall panel production model about information such as activity se-quence, cost estimation per activity and also the direct material cost. This ulti-mately enable users to evaluate the overall product from the system

A Programme for Women achieving Excellence in Research (PoWER): theoretically informed intervention design and evaluation

Academics in Higher Education are often expected both to teach and to research; this is a particular challenge for women both structurally and individually. Initiatives to address structural issues include AdvanceHE. Here, we focus on individual issues and report on the Programme for Women Achieving Excellence in Research, a theory-based intervention. Barriers to success were assessed and course content tailored accordingly. Evaluation demonstrated that barriers were reduced and that confidence increased. Although the barriers are both individual and contextual, our rigorous approach allows international application through intervention modification without loss of fidelity. This offers a new approach for academic developers to enable female researchers

Large Language Models and Knowledge Graphs: Opportunities and Challenges

Large Language Models (LLMs) have taken Knowledge Representation -- and the world -- by storm. This inflection point marks a shift from explicit knowledge representation to a renewed focus on the hybrid representation of both explicit knowledge and parametric knowledge. In this position paper, we will discuss some of the common debate points within the community on LLMs (parametric knowledge) and Knowledge Graphs (explicit knowledge) and speculate on opportunities and visions that the renewed focus brings, as well as related research topics and challenges.Comment: 30 page

Financial Sentiment Analysis on Twitter During Covid-19 Pandemic in the UK

The surge in Covid-19 cases seen in 2020 has caused the UK government to enact regulations to stop the virus’s spread. Along with other aspects like altered customer confidence and activity, the financial effects of these actions must be taken into account. This later can be studied from the user generated content posted on social net- works such as Twitter. In this paper, we provide a supervised technique to analyze tweets exhibiting bullish and bearish sentiments, by predicting a sentiment class positive, negative, or neutral. Both machine learning and deep learning techniques are implemented to predict our financial sentiment class. Our research highlights how word embeddings, most importantly word2vec may be effectively used to conduct sentiment analysis in the financial sector providing favourable solutions. In addition, comprehensive research has been elicited between our technique and a lexicon-based approach. The outcomes of the study indicate how well Word2Vec model with deep learning techniques outperforms the others with an accuracy of 87%

Financial Sentiment Analysis on Twitter During Covid-19 Pandemic in the UK

The surge in Covid-19 cases seen in 2020 has caused the UK government to enact regulations to stop the virus’s spread. Along with other aspects like altered customer confidence and activity, the financial effects of these actions must be taken into account. This later can be studied from the user generated content posted on social net- works such as Twitter. In this paper, we provide a supervised technique to analyze tweets exhibiting bullish and bearish sentiments, by predicting a sentiment class positive, negative, or neutral. Both machine learning and deep learning techniques are implemented to predict our financial sentiment class. Our research highlights how word embeddings, most importantly word2vec may be effectively used to conduct sentiment analysis in the financial sector providing favourable solutions. In addition, comprehensive research has been elicited between our technique and a lexicon-based approach. The outcomes of the study indicate how well Word2Vec model with deep learning techniques outperforms the others with an accuracy of 87%