Querying a regulatory model for compliant building design audit

The ingredients for an effective automated audit of a building design include a BIM model containing the design information, an electronic regulatory knowledge model, and a practical method of processing these computerised representations. There have been numerous approaches to computer-aided compliance audit in the AEC/FM domain over the last four decades, but none has yet evolved into a practical solution. One reason is that they have all been isolated attempts that lack any form of standardisation. The current research project therefore focuses on using an open standard regulatory knowledge and BIM representations in conjunction with open standard executable compliant design workflows to automate the compliance audit process. This paper provides an overview of different approaches to access information from a regulatory model representation. The paper then describes the use of a purpose-built high-level domain specific query language to extract regulatory information as part of the effort to automate manual design procedures for compliance audit

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Discovering Knowledge from Relational Data Extracted from Business News

Thousands of business news stories (including press releases, earnings reports, general business news, etc.) are released each day. Recently, information technology advances have partially automated the processing of documents, reducing the amount of text that must be read. Current techniques (e.g., text classification and information extraction) for full-text analysis for the most part are limited to discovering information that can be found in single documents. Often, however, important information does not reside in a single document, but in the relationships between information distributed over multiple documents. This paper reports on an investigation into whether knowledge can be discovered automatically from relational data extracted from large corpora of business news stories. We use a combination of information extraction, network analysis, and statistical techniques. We show that relationally interlinked patterns distributed over multiple documents can indeed be extracted, and (specifically) that knowledge about companiesÃÂÃÂÃÂâÃÂÃÂÃÂÃÂÃÂÃÂÃÂàinterrelationships can be discovered. We evaluate the extracted relationships in several ways: we give a broad visualization of related companies, showing intuitive industry clusters; we use network analysis to ask who are the central players, and finally, we show that the extracted interrelationships can be used for important tasks, such as for classifying companies by industry membership.Information Systems Working Papers Serie

Ethical Marketing: A Look on the Bright Side

This article offers an alternative to conventional approaches to ethical analysis in business and marketing. We submit that studying companies with exemplary records of ethical conduct and social responsibility offers useful and compelling guidance to marketing students and managers. It provides another needed perspective beyond simply examining examples of misconduct or offering normative advice that may not reflect the specifics of corporate situations. Based on examples presented in a recent text by the authors and Better Business Bureau Torch Awardees, we present information on thirteen companies of varying size and from several different industries. That information includes ethics policies, management practices, environmental practices, and company reputation. From these examples, we draw lessons that should offer ethical guidance to marketing managers

DocumentNet: Bridging the Data Gap in Document Pre-Training

Document understanding tasks, in particular, Visually-rich Document Entity Retrieval (VDER), have gained significant attention in recent years thanks to their broad applications in enterprise AI. However, publicly available data have been scarce for these tasks due to strict privacy constraints and high annotation costs. To make things worse, the non-overlapping entity spaces from different datasets hinder the knowledge transfer between document types. In this paper, we propose a method to collect massive-scale and weakly labeled data from the web to benefit the training of VDER models. The collected dataset, named DocumentNet, does not depend on specific document types or entity sets, making it universally applicable to all VDER tasks. The current DocumentNet consists of 30M documents spanning nearly 400 document types organized in a four-level ontology. Experiments on a set of broadly adopted VDER tasks show significant improvements when DocumentNet is incorporated into the pre-training for both classic and few-shot learning settings. With the recent emergence of large language models (LLMs), DocumentNet provides a large data source to extend their multi-modal capabilities for VDER.Comment: EMNLP 202

The LIFE2 final project report

Executive summary: The first phase of LIFE (Lifecycle Information For E-Literature) made a major contribution to understanding the long-term costs of digital preservation; an essential step in helping institutions plan for the future. The LIFE work models the digital lifecycle and calculates the costs of preserving digital information for future years. Organisations can apply this process in order to understand costs and plan effectively for the preservation of their digital collections The second phase of the LIFE Project, LIFE2, has refined the LIFE Model adding three new exemplar Case Studies to further build upon LIFE1. LIFE2 is an 18-month JISC-funded project between UCL (University College London) and The British Library (BL), supported by the LIBER Access and Preservation Divisions. LIFE2 began in March 2007, and completed in August 2008. The LIFE approach has been validated by a full independent economic review and has successfully produced an updated lifecycle costing model (LIFE Model v2) and digital preservation costing model (GPM v1.1). The LIFE Model has been tested with three further Case Studies including institutional repositories (SHERPA-LEAP), digital preservation services (SHERPA DP) and a comparison of analogue and digital collections (British Library Newspapers). These Case Studies were useful for scenario building and have fed back into both the LIFE Model and the LIFE Methodology. The experiences of implementing the Case Studies indicated that enhancements made to the LIFE Methodology, Model and associated tools have simplified the costing process. Mapping a specific lifecycle to the LIFE Model isn’t always a straightforward process. The revised and more detailed Model has reduced ambiguity. The costing templates, which were refined throughout the process of developing the Case Studies, ensure clear articulation of both working and cost figures, and facilitate comparative analysis between different lifecycles. The LIFE work has been successfully disseminated throughout the digital preservation and HE communities. Early adopters of the work include the Royal Danish Library, State Archives and the State and University Library, Denmark as well as the LIFE2 Project partners. Furthermore, interest in the LIFE work has not been limited to these sectors, with interest in LIFE expressed by local government, records offices, and private industry. LIFE has also provided input into the LC-JISC Blue Ribbon Task Force on the Economic Sustainability of Digital Preservation. Moving forward our ability to cost the digital preservation lifecycle will require further investment in costing tools and models. Developments in estimative models will be needed to support planning activities, both at a collection management level and at a later preservation planning level once a collection has been acquired. In order to support these developments a greater volume of raw cost data will be required to inform and test new cost models. This volume of data cannot be supported via the Case Study approach, and the LIFE team would suggest that a software tool would provide the volume of costing data necessary to provide a truly accurate predictive model