scholarly journals Construction Disputes and Associated Contractual Knowledge Discovery Using Unstructured Text-Heavy Data: Legal Cases in the United Kingdom

2021 ◽  
Vol 13 (16) ◽  
pp. 9403
Author(s):  
JeeHee Lee ◽  
Youngjib Ham ◽  
June-Seong Yi
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Construction Projects ◽  
Large Scale ◽  
Lessons Learned ◽  
Final Decision ◽  
Legal Cases ◽  
Construction Disputes ◽  
The United Kingdom ◽  
Pairwise Correlations

Construction disputes are one of the main challenges to successful construction projects. Most construction parties experience claims—and even worse, disputes—which are costly and time-consuming to resolve. Lessons learned from past failure cases can help reduce potential future risk factors that likely lead to disputes. In particular, case law, which has been accumulated from the past, is valuable information, providing useful insights to prepare for future disputes. However, few efforts have been made to discover legal knowledge using a large scale of case laws in the construction field. The aim of this paper is to enhance understanding of the multifaceted legal issues surrounding construction adjudication using large amounts of accumulated construction legal cases. This goal is achieved by exploring dispute-related contract terms and conditions that affect judicial decisions based on their verdicts. This study builds on text mining methods to examine what type of contract conditions are frequently referenced in the final decision of each dispute. Various text mining techniques are leveraged for knowledge discovery (i.e., analyzing frequent terms, discovering pairwise correlations, and identifying potential topics) in text-heavy data. The findings show that (1) similar patterns of disputes have occurred repeatedly in construction-related legal cases and (2) the discovered dispute topics indicate that mutually agreed upon contract terms and conditions are import in dispute resolution.

scholarly journals Upscaling non-residential bio-based circular construction in the United Kingdom

2021 ◽  
Vol 855 (1) ◽  
pp. 012015
Author(s):  
B Dams ◽  
D Maskell ◽  
A Shea ◽  
S Allen ◽  
V Cascione ◽  
...  
Keyword(s):  
United Kingdom ◽  
Case Studies ◽  
Construction Projects ◽  
Large Scale ◽  
The United Kingdom ◽  
Distribution Centre ◽  
Vested Interests ◽  
The Uk ◽  
The University

Abstract Non-residential circular construction projects using bio-based materials have been realised in the United Kingdom. Case studies include the Adnams Distribution Centre, the University of East Anglia’s Enterprise Centre and the British Science Museum’s hempcrete storage facility. The bio-based buildings utilise the natural properties of bio-based materials to insulate and regulate internal environments, particularly with reducing fluctuations in temperature and relative humidity, which can be harmful to sensitive stored products and artefacts. Projects have been successful on both on environmental and physical performance levels; however, they have not led to a subsequent proliferation of non-residential large-scale circular projects within the UK using emerging bio-based materials. This study examines why and uses analysis based upon exclusive interviews with key figures associated with bio-based case studies. Challenges faced include the ability to upscale production by manufacturers of bio-based materials, problems surrounding initial costs, gaining accreditation for materials, the vested interests present in the construction industry and levels of knowledge among clients and construction professionals. Potential upscaling solutions identified include long-term financial savings on running costs and high staff productivity, policies regarding grants, incentives and planning applications and local economic regeneration.

Implicit Knowledge Discovery in Design Semantic Network by Applying Pythagorean Means on Shortest Path Searching

2017 ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Language Processing ◽  
Shortest Path ◽  
Large Scale ◽  
Semantic Network ◽  
Semantic Networks ◽  
Implicit Knowledge ◽  
Implicit Associations ◽  
Correlation Degree

With the advent of the big-data era, massive textual information stored in electronic and digital documents have become valuable resources for knowledge discovery in the fields of design and engineering. Ontology technologies and semantic networks have been widely applied with text mining techniques including Natural Language Processing (NLP) to extract structured knowledge associations from the large-scale unstructured textual data. However, most existing works mainly focus on how to construct the semantic networks by developing various text mining methods such as statistical approaches and semantic approaches, while few studies are found to focus on how to subsequently analyze and fully utilize the already well-established semantic networks. In this paper, a specific network analysis method is proposed to discover the implicit knowledge associations from the existing semantic network for improving knowledge discovery and design innovation. Pythagorean means are applied with Dijkstra’s shortest path algorithm to discover the implicit knowledge associations either around a single knowledge concept or between two concepts. Six criteria are established to evaluate and rank the correlation degree of the implicit associations. Two engineering case studies were conducted to illustrate the proposed knowledge discovery process, and the results showed the effectiveness of the retrieved implicit knowledge associations on helping providing relevant knowledge from various aspects, and provoking creative ideas for engineering innovation.

Text Mining for Biomedicine

2010 ◽  
pp. 1-9 ◽  
Author(s):  
Sophia Ananiadou
Keyword(s):  
United Kingdom ◽  
Text Mining ◽  
Knowledge Discovery ◽  
Information Overload ◽  
The United Kingdom ◽  
National Centre ◽  
Structured Analysis

Text mining provides the automated means to manage information overload and overlook. By adding meaning to text, text mining techniques produce a much more structured analysis of textual knowledge than do simple word searches, and can provide powerful tools for knowledge discovery in biomedicine. In this chapter, the author focus on the text mining services for biomedicine offered by the United Kingdom National Centre for Text Mining.

scholarly journals Biodiversity Observations Miner: A web application to unlock primary biodiversity data from published literature

2019 ◽  
Vol 7 ◽  
Author(s):  
Gabriel Muñoz ◽  
W. Daniel Kissling ◽  
E. Emiel van Loon
Keyword(s):  
Text Mining ◽  
Knowledge Discovery ◽  
Web Application ◽  
Large Scale ◽  
Biotic Interactions ◽  
Rapid Screening ◽  
Biodiversity Data ◽  
Biodiversity Science ◽  
Automated Discovery ◽  
Machine Readable

A considerable portion of primary biodiversity data is digitally locked inside published literature which is often stored as pdf files. Large-scale approaches to biodiversity science could benefit from retrieving this information and making it digitally accessible and machine-readable. Nonetheless, the amount and diversity of digitally published literature pose many challenges for knowledge discovery and retrieval. Text mining has been extensively used for data discovery tasks in large quantities of documents. However, text mining approaches for knowledge discovery and retrieval have been limited in biodiversity science compared to other disciplines. Here, we present a novel, open source text mining tool, the Biodiversity Observations Miner (BOM). This web application, written in R, allows the semi-automated discovery of punctual biodiversity observations (e.g. biotic interactions, functional or behavioural traits and natural history descriptions) associated with the scientific names present inside a corpus of scientific literature. Furthermore, BOM enable users the rapid screening of large quantities of literature based on word co-occurrences that match custom biodiversity dictionaries. This tool aims to increase the digital mobilisation of primary biodiversity data and is freely accessible via GitHub or through a web server.

Experiences With and Lessons Learned From Developing, Implementing, and Evaluating a Support Program for Older Hearing Aid Users and Their Communication Partners in the Hearing Aid Dispensing Setting

2020 ◽  
Vol 29 (3S) ◽  
pp. 638-647 ◽  
Author(s):  
Janine F. J. Meijerink ◽  
Marieke Pronk ◽  
Sophia E. Kramer
Keyword(s):  
Large Scale ◽  
Hearing Aid ◽  
Critical Discussion ◽  
Lessons Learned ◽  
Research Note ◽  
Support Program ◽  
Large Sample Size ◽  
Long Term Effects ◽  
Communication Program ◽  
Communication Partners

Purpose The SUpport PRogram (SUPR) study was carried out in the context of a private academic partnership and is the first study to evaluate the long-term effects of a communication program (SUPR) for older hearing aid users and their communication partners on a large scale in a hearing aid dispensing setting. The purpose of this research note is to reflect on the lessons that we learned during the different development, implementation, and evaluation phases of the SUPR project. Procedure This research note describes the procedures that were followed during the different phases of the SUPR project and provides a critical discussion to describe the strengths and weaknesses of the approach taken. Conclusion This research note might provide researchers and intervention developers with useful insights as to how aural rehabilitation interventions, such as the SUPR, can be developed by incorporating the needs of the different stakeholders, evaluated by using a robust research design (including a large sample size and a longer term follow-up assessment), and implemented widely by collaborating with a private partner (hearing aid dispensing practice chain).

Integration of Ecological and Engineering Aspects in Planning Large Scale Tidal Power Development in the Bay of Fundy, Canada

1984 ◽  
Vol 16 (1-2) ◽  
pp. 281-295 ◽  
Author(s):  
Donald C Gordon
Keyword(s):  
Large Scale ◽  
Feasibility Studies ◽  
Bay Of Fundy ◽  
Final Decision ◽  
Power Development ◽  
Tidal Power ◽  
History Of ◽  
Scientists And Engineers ◽  
The Many ◽  
University Scientists

Large-scale tidal power development in the Bay of Fundy has been given serious consideration for over 60 years. There has been a long history of productive interaction between environmental scientists and engineers durinn the many feasibility studies undertaken. Up until recently, tidal power proposals were dropped on economic grounds. However, large-scale development in the upper reaches of the Bay of Fundy now appears to be economically viable and a pre-commitment design program is highly likely in the near future. A large number of basic scientific research studies have been and are being conducted by government and university scientists. Likely environmental impacts have been examined by scientists and engineers together in a preliminary fashion on several occasions. A full environmental assessment will be conducted before a final decision is made and the results will definately influence the outcome.

Investigating Diseases and Chemicals in COVID-19 Literature with Text Mining (Preprint)

2020 ◽  
Author(s):  
Amir Karami ◽  
Brandon Bookstaver ◽  
Melissa Nolan
Keyword(s):  
Text Mining ◽  
Literature Review ◽  
Topic Modeling ◽  
Large Scale ◽  
Clinical Manifestations ◽  
International Health ◽  
Research Papers ◽  
Strategic Plans ◽  
Funding Agencies ◽  
The Relationship

BACKGROUND The COVID-19 pandemic has impacted nearly all aspects of life and has posed significant threats to international health and the economy. Given the rapidly unfolding nature of the current pandemic, there is an urgent need to streamline literature synthesis of the growing scientific research to elucidate targeted solutions. While traditional systematic literature review studies provide valuable insights, these studies have restrictions, including analyzing a limited number of papers, having various biases, being time-consuming and labor-intensive, focusing on a few topics, incapable of trend analysis, and lack of data-driven tools. OBJECTIVE This study fills the mentioned restrictions in the literature and practice by analyzing two biomedical concepts, clinical manifestations of disease and therapeutic chemical compounds, with text mining methods in a corpus containing COVID-19 research papers and find associations between the two biomedical concepts. METHODS This research has collected papers representing COVID-19 pre-prints and peer-reviewed research published in 2020. We used frequency analysis to find highly frequent manifestations and therapeutic chemicals, representing the importance of the two biomedical concepts. This study also applied topic modeling to find the relationship between the two biomedical concepts. RESULTS We analyzed 9,298 research papers published through May 5, 2020 and found 3,645 disease-related and 2,434 chemical-related articles. The most frequent clinical manifestations of disease terminology included COVID-19, SARS, cancer, pneumonia, fever, and cough. The most frequent chemical-related terminology included Lopinavir, Ritonavir, Oxygen, Chloroquine, Remdesivir, and water. Topic modeling provided 25 categories showing relationships between our two overarching categories. These categories represent statistically significant associations between multiple aspects of each category, some connections of which were novel and not previously identified by the scientific community. CONCLUSIONS Appreciation of this context is vital due to the lack of a systematic large-scale literature review survey and the importance of fast literature review during the current COVID-19 pandemic for developing treatments. This study is beneficial to researchers for obtaining a macro-level picture of literature, to educators for knowing the scope of literature, to journals for exploring most discussed disease symptoms and pharmaceutical targets, and to policymakers and funding agencies for creating scientific strategic plans regarding COVID-19.

scholarly journals The graph neural networking challenge

2021 ◽  
Vol 51 (3) ◽  
pp. 9-16
Author(s):  
José Suárez-Varela ◽  
Miquel Ferriol-Galmés ◽  
Albert López ◽  
Paul Almasan ◽  
Guillermo Bernárdez ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computer Networks ◽  
Real World ◽  
Large Scale ◽  
Lessons Learned ◽  
Educational Resources ◽  
Global Competition ◽  
International Telecommunication Union ◽  
International Telecommunication ◽  
Broad Audience

During the last decade, Machine Learning (ML) has increasingly become a hot topic in the field of Computer Networks and is expected to be gradually adopted for a plethora of control, monitoring and management tasks in real-world deployments. This poses the need to count on new generations of students, researchers and practitioners with a solid background in ML applied to networks. During 2020, the International Telecommunication Union (ITU) has organized the "ITU AI/ML in 5G challenge", an open global competition that has introduced to a broad audience some of the current main challenges in ML for networks. This large-scale initiative has gathered 23 different challenges proposed by network operators, equipment manufacturers and academia, and has attracted a total of 1300+ participants from 60+ countries. This paper narrates our experience organizing one of the proposed challenges: the "Graph Neural Networking Challenge 2020". We describe the problem presented to participants, the tools and resources provided, some organization aspects and participation statistics, an outline of the top-3 awarded solutions, and a summary with some lessons learned during all this journey. As a result, this challenge leaves a curated set of educational resources openly available to anyone interested in the topic.

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