Semantics-based approach for generating partial views from linked life-cycle highway project data

The purpose of this dissertation is to develop methods that can assist data integration and extraction from heterogeneous sources generated throughout the life-cycle of a highway project. In the era of computerized technologies, project data is largely available in digital format. Due to the fragmented nature of the civil infrastructure sector, digital data are created and managed separately by different project actors in proprietary data warehouses. The differences in the data structure and semantics greatly hinder the exchange and fully reuse of digital project data. In order to address those issues, this dissertation carries out the following three individual studies. The first study aims to develop a framework for interconnecting heterogeneous life cycle project data into an unified and linked data space. This is an ontology-based framework that consists of two phases: (1) translating proprietary datasets into homogeneous RDF data graphs; and (2) connecting separate data networks to each other. Three domain ontologies for design, construction, and asset condition survey phases are developed to support data transformation. A merged ontology that integrates the domain ontologies is constructed to provide guidance on how to connect data nodes from domain graphs. The second study is to deal with the terminology inconsistency between data sources. An automated method is developed that employs Natural Language Processing (NLP) and machine learning techniques to support constructing a domain specific lexicon from design manuals. The method utilizes pattern rules to extract technical terms from texts and learns their representation vectors using a neural network based word embedding approach. The study also includes the development of an integrated method of minimal-supervised machine learning, clustering analysis, and word vectors, for computing the term semantics and classifying the relations between terms in the target lexicon. In the last study, a data retrieval technique for extracting subsets of an XML civil data schema is designed and tested. The algorithm takes a keyword input of the end user and returns a ranked list of the most relevant XML branches. This study utilizes a lexicon of the highway domain generated from the second study to analyze the semantics of the end user keywords. A context-based similarity measure is introduced to evaluate the relevance between a certain branch in the source schema and the user query. The methods and algorithms resulting from this research were tested using case studies and empirical experiments. The results indicate that the study successfully address the heterogeneity in the structure and terminology of data and enable a fast extraction of sub-models of data. The study is expected to enhance the efficiency in reusing digital data generated throughout the project life-cycle, and contribute to the success in transitioning from paper-based to digital project delivery for civil infrastructure projects

Advancing Building Energy Modeling with Large Language Models: Exploration and Case Studies

The rapid progression in artificial intelligence has facilitated the emergence of large language models like ChatGPT, offering potential applications extending into specialized engineering modeling, especially physics-based building energy modeling. This paper investigates the innovative integration of large language models with building energy modeling software, focusing specifically on the fusion of ChatGPT with EnergyPlus. A literature review is first conducted to reveal a growing trend of incorporating of large language models in engineering modeling, albeit limited research on their application in building energy modeling. We underscore the potential of large language models in addressing building energy modeling challenges and outline potential applications including 1) simulation input generation, 2) simulation output analysis and visualization, 3) conducting error analysis, 4) co-simulation, 5) simulation knowledge extraction and training, and 6) simulation optimization. Three case studies reveal the transformative potential of large language models in automating and optimizing building energy modeling tasks, underscoring the pivotal role of artificial intelligence in advancing sustainable building practices and energy efficiency. The case studies demonstrate that selecting the right large language model techniques is essential to enhance performance and reduce engineering efforts. Besides direct use of large language models, three specific techniques were utilized: 1) prompt engineering, 2) retrieval-augmented generation, and 3) multi-agent large language models. The findings advocate a multidisciplinary approach in future artificial intelligence research, with implications extending beyond building energy modeling to other specialized engineering modeling

Personalized Memory Transfer for Conversational Recommendation Systems

Dialogue systems are becoming an increasingly common part of many users\u27 daily routines. Natural language serves as a convenient interface to express our preferences with the underlying systems. In this work, we implement a full-fledged Conversational Recommendation System, mainly focusing on learning user preferences through online conversations. Compared to the traditional collaborative filtering setting where feedback is provided quantitatively, conversational users may only indicate their preferences at a high level with inexact item mentions in the form of natural language chit-chat. This makes it harder for the system to correctly interpret user intent and in turn provide useful recommendations to the user. To tackle the ambiguities in natural language conversations, we propose Personalized Memory Transfer (PMT) which learns a personalized model in an online manner by leveraging a key-value memory structure to distill user feedback directly from conversations. This memory structure enables the integration of prior knowledge to transfer existing item representations/preferences and natural language representations. We also implement a retrieval based response generation module, where the system in addition to recommending items to the user, also responds to the user, either to elicit more information regarding the user intent or just for a casual chit-chat. The experiments were conducted on two public datasets and the results demonstrate the effectiveness of the proposed approach

Recurrent Session Approach to Generative Association Rule based Recommendation

This article introduces a generative association rule (AR)-based recommendation system (RS) using a recurrent neural network approach implemented when a user searches for an item in a browsing session. It is proposed to overcome the limitations of the traditional AR-based RS which implements query-based sessions that are not adaptive to input series, thus failing to generate recommendations.  The dataset used is accurate retail transaction data from online stores in Europe. The contribution of the proposed method is a next-item prediction model using LSTM, but what is trained to develop the model is an associative rule string, not a string of items in a purchase transaction. The proposed model predicts the next item generatively, while the traditional method discriminatively. As a result, for an array of items that the user has viewed in a browsing session, the model can always recommend the following items when traditional methods cannot.  In addition, the results of user-centered validation of several metrics show that although the level of accuracy (similarity) of recommended products and products seen by users is only 20%, other metrics reach above 70%, such as novelty, diversity, attractiveness and enjoyability

RESEARCH ON INFORMATION RESOURCES AGGREGATION IN ACADEMIC TO SEMANTIC PUBLISHING

With the constant development of information and digitization, the proportion of digitization in scientific research publications is increasing day by day. On the one hand, the rapid growth of digital scientific research data and academic literature has provided many facilities for academic exchanges among scientific research users. On the basis of systematically combing the relevant theories of semantic publishing and information resource integration, this paper summarizes the current situation of information resource aggregation in academic journals and the significance of digital resource aggregation. Secondly, this paper illustrates the important role of semantic information resource integration in semantic publishing of academic journals. Taking Elsevier semantic publishingmodel as an example, it focuses on the resource query and resource utilization under semantic publishing. Final adoption with the comparison of web of science database and the analysis and evaluation of the results of resource aggregation verify the feasibility of the semantic based digitalresource aggregation method in the digital publication of academic journals.Keywords: Semantic Publishing; Semantic Web, Digital Resource, and Aggregation elsevi

e-DOCSPROS : exploring TEXPROS into e-business era

Document processing is a critical element of office automation. TEXPROS (TEXt PROcessing System) is a knowledge-based system designed to manage personal documents. However, as the Internet and e-Business changed the way offices operate, there is a need to re-envision document processing, storage, retrieval, and sharing. In the current environment, people must be able to access documents remotely and to share those documents with others. e-DOCPROS (e-DOCument PROcessing System) is a new document processing system that takes advantage of many of TEXPROS\u27s structures but adapts the system to this new environment. The new system is built to serve e-businesses, takes advantage of Internet protocols, and to give remote access and document sharing. e-DOCPROS meets the challenge to provide wider usage, and eventually will improve the efficiency and effectiveness of office automation. It allows end users to access their data through any Web browser with Internet access, even a wireless network, which will evolutionarily change the way we manage information. The application of e-DOCPROS to e-Business is considered. Four types of business models re considered here. The first is the Business-to-Business (B2B) model, which performs business-to-business transactions through an Extranet. The Extranet consists of multiple Intranets connected via the Internet.The second is the Business-to-Consumer (B2Q model, which performs business-to-consumer transactions through the Internet. The third is the Intranet model, which performs transactions within an organization through the organization\u27s network. The fourth is the Consumer-to-Consumer (C2C) model, which performs consumer-to consumer transactions through the Internet. A triple model is proposed in this dissertation to integrate organization type hierarchy and document type hierarchy together into folder organization. e-DOCPROS introduces new features into TEXPROS to support those four business models and to accommodate the system requirements. Extensible Markup Language (XML), an industrial standard protocol for data exchange, is employed to achieve the goal of information exchange between e-DOCPROS and the other systems, and also among the subsystems within e-DOCPROS. Document Object Model (DOM) specification is followed throughout the implementation of e-DOCPROS to achieve portability. Agent-based Application Service Provider (ASP) implementation is employed in e-DOCPROS system to achieve cost-effectiveness and accessibility

Digital Image Access & Retrieval

The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio

The semantic Web : theories, languages, and applications

La popularité croissante du Web permet la diffusion d’une quantité phénoménale d’information de toutes sortes et l’accès à une panoplie de services en ligne en raison du développement effréné de ses contenus et de leur consultation quotidienne à faible coût. Malheureusement, cette explosion du Web cause un problème de surabondance de données pas toujours crédibles et souvent inutilisables; les réponses obtenues des engins de recherche peuvent être inadéquates ou imprécises et les services en ligne sont exclusifs ou incompatibles entre eux. Dans le but de pallier à ces inconvénients, le consortium W3C a proposé une solution globale, connue sous le nom de Web sémantique, qui améliore les structures de représentation des données de façon à rendre les contenus signifiants et à permettre l’inférence de nouvelles connaissances par des programmes. Ce mémoire explore les théories sous-jacentes au Web sémantique ainsi que les technologies qui lui sont propres. D’une part, les concepts de logique descriptive et de structure ontologique sont présentés et des liens sont établis entre eux. D’autre part, une hiérarchie de langages incluant, entre autres, les langages XML, RDF, DAML+OIL et OWL est introduite ainsi qu’une étude comparative de plusieurs moteurs d’inférence basés sur ces langages. Enfin, ce mémoire présente un exemple complet qui permet d’illustrer les principaux concepts du Web sémantique et d’évaluer la faisabilité de la mise en oeuvre d’une application par rapport à l’état actuel des technologies

A survey on opinion summarization technique s for social media

The volume of data on the social media is huge and even keeps increasing. The need for efficient processing of this extensive information resulted in increasing research interest in knowledge engineering tasks such as Opinion Summarization. This survey shows the current opinion summarization challenges for social media, then the necessary pre-summarization steps like preprocessing, features extraction, noise elimination, and handling of synonym features. Next, it covers the various approaches used in opinion summarization like Visualization, Abstractive, Aspect based, Query-focused, Real Time, Update Summarization, and highlight other Opinion Summarization approaches such as Contrastive, Concept-based, Community Detection, Domain Specific, Bilingual, Social Bookmarking, and Social Media Sampling. It covers the different datasets used in opinion summarization and future work suggested in each technique. Finally, it provides different ways for evaluating opinion summarization