The global adoption of Industralised Building System (IBS) : lessons learned

Industrialised Building Systems (IBS) is typically used interchangeably with other terms such as prefabrication, offsite manufacturing, offsite construction, and modern method of construction (MMC), industrialised building and industrialised construction. Nevertheless, the term modern method of construction (MMC) has been used to collectively describe both offsite-based construction technologies and innovative onsite technologies in the United Kingdom. It is evident that there exist a wide range of contextual issues which stems from the definition of these terminologies. However, lack of previous research has explored the relationship between these terminologies. Therefore, this paper emphasises the contrasting concepts of IBS and MMC, and concludes that ill-defining the MMC-IBS terms leads to misunderstanding, uncertainty and prejudice of the IBS concept and its benefits besides the adoption of IBS in global, which will be detrimental to efforts promoting the use of IBS in the construction industry

Exploiting Sentence Embedding for Medical Question Answering

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.Comment: 8 page

Analysis of equivalence mapping for terminology services

This paper assesses the range of equivalence or mapping types required to facilitate interoperability in the context of a distributed terminology server. A detailed set of mapping types were examined, with a view to determining their validity for characterizing relationships between mappings from selected terminologies (AAT, LCSH, MeSH, and UNESCO) to the Dewey Decimal Classification (DDC) scheme. It was hypothesized that the detailed set of 19 match types proposed by Chaplan in 1995 is unnecessary in this context and that they could be reduced to a less detailed conceptually-based set. Results from an extensive mapping exercise support the main hypothesis and a generic suite of match types are proposed, although doubt remains over the current adequacy of the developing Simple Knowledge Organization System (SKOS) Core Mapping Vocabulary Specification (MVS) for inter-terminology mapping

Context-sensitive Spelling Correction Using Google Web 1T 5-Gram Information

In computing, spell checking is the process of detecting and sometimes providing spelling suggestions for incorrectly spelled words in a text. Basically, a spell checker is a computer program that uses a dictionary of words to perform spell checking. The bigger the dictionary is, the higher is the error detection rate. The fact that spell checkers are based on regular dictionaries, they suffer from data sparseness problem as they cannot capture large vocabulary of words including proper names, domain-specific terms, technical jargons, special acronyms, and terminologies. As a result, they exhibit low error detection rate and often fail to catch major errors in the text. This paper proposes a new context-sensitive spelling correction method for detecting and correcting non-word and real-word errors in digital text documents. The approach hinges around data statistics from Google Web 1T 5-gram data set which consists of a big volume of n-gram word sequences, extracted from the World Wide Web. Fundamentally, the proposed method comprises an error detector that detects misspellings, a candidate spellings generator based on a character 2-gram model that generates correction suggestions, and an error corrector that performs contextual error correction. Experiments conducted on a set of text documents from different domains and containing misspellings, showed an outstanding spelling error correction rate and a drastic reduction of both non-word and real-word errors. In a further study, the proposed algorithm is to be parallelized so as to lower the computational cost of the error detection and correction processes.Comment: LACSC - Lebanese Association for Computational Sciences - http://www.lacsc.or

Report on the EHCR (Deliverable 26.1)

The challenge of richly interpreting electronic health information, in order to populate EHR instances with suitable terms, to provide decision support in the care of individuals, to identify suitable patients for teaching or clinical trials recruitment, and to mine populations of records for public health or to discover new medical knowledge, all require that the heterogeneous clinical entry instances within EHR repositories can be systematically analysed and interpreted. Achieving this requires the combination and co-operation of many different health informatics tools and technologies, underpinned by shared representations of clinical concepts and inferencing formalisms. Much of this work is at the level of R&D, and is well represented across the Semantic Mining consortium. The challenge of WP26 is to build up a vision of the ways in which these historically independent threads of health informatics research can collaborate, and uncover the research challenges that are needed in order to deliver good demonstrations of semantically indexed and richly analysable EHRs. The partners have begun WP26 by acquiring a better knowledge of each other’s areas of endeavour, and are beginning to steer their research interests towards future areas of collaboration

TERMINOLOGY, THE ROLE OF DOMAIN AND INTERDISCIPLINARITY

Terminology deals with the specialized communication, which is achieved in a certain scientific, technical and professional domain. Interdisciplinarity is considered another orientation specific to modern sciences, in which the terms of a specific science, could be found in another science or in many sciences. It becomes relevant when a specialist in a specific field of study, knows a few characteristics of the concepts he needs, in order to have a professional interaction with the users of the concepts. The role of the domain is highly important, if it is registered in dictionaries, we could establish the interdisciplinarity of various scientific domains.terminology, interdisciplinarity, term, concept, semantics, interaction, domain

Investigating the feasibility of a distributed, mapping-based, approach to solving subject interoperability problems in a multi-scheme, cross-service, retrieval environment

The HILT project is researching the problems of facilitating interoperability of subject descriptions in a distributed multi-scheme environment. HILT Phase I found a UK community consensus in favour of utilising an inter-scheme mapping service to improve interoperability. HILT Phase II investigated the approach by building a pilot server, and identified a range of issues that would have to be tackled if an operational service was to be successful. HILT Phase III will implement a centralised version of an M2M pilot, but will aim to design it so that the possibility of a move to a distributed service remains open. This aim will impact on likely future research concerns in Phase III and beyond. Wide adoption of a distributed approach to the problem could lead to the creation of a framework within which regional, national, and international efforts in the area can be harmonised and co-ordinated