Проблеми семантизації інформаційних ресурсів web для підтримки управління процесами оподаткування

Introduction. Now information support of taxation processes is oriented on intelligent analysis of the Web resources. Such analysis requires the processing of large volumes of heterogeneous information at the semantic level. Ontological analysis is a powerful tool for formalizing knowledge in diff erent domains. At present a large number of standards, languages and software tools are developed for these purposes within the of the Semantic Web project. The formalization of knowledge about the subjects of economic activity in the form of ontologies simplifi es the search for the necessary knowledge and allows integration of data from diff erent sources from the open information environment – the Web, local and corporate networks, etc.Проанализированы проблемы, связанные с поиском в Web информации, касающейся налогообложения. Обоснована целесообразность использования семантического поиска, в котором применяются знания данной предметной области. Для формализации этих знаний предлагается использовать онтологию, которая содержит основные термины из сферы налогообложения и связи между ними. Эти знания используются для того, чтобы определить семантическую близость между найденными ресурсами и информационными потребностями пользователей. Термины онтологии используются как основа для семантической разметки найденных в процессе поиска информационных ресурсов, которая упрощает восприятие информации пользователем. Предоставляются рекомендации относительно выбора источников для пополнения этой онтологии и метод получения онтологических знаний из естественноязыковых ресурсов.Проаналізовано проблеми, пов’язані з пошуком у Web інформації, що стосується оподаткуванням. Обґрунтовано доцільність використання семантичного пошуку, в якому застосовуються знання цієї предметної області. Для формалізації цих знань пропонується використовувати онтологію, яка містить основні терміни зі сфери оподаткування та зв’язки між ними. Ці знання використовуються для того, щоб визначити семантичну близькість між знайденими ресурсами та інформаційними потребами користувачів. Терміни онтології використовуються як основа для семантичної розмітки знайдених у процесі пошуку інформаційних ресурсів, що спрощує сприйняття інформації користувачами. Надаються рекомендації щодо вибору джерел для поповнення цієї онтології та метод здобуття онтологічних знань з природномовних ресурсів

A Novel Ontology Approach to Support Design for Reliability considering Environmental Effects

Environmental effects are not considered sufficiently in product design. Reliability problems caused by environmental effects are very prominent. This paper proposes a method to apply ontology approach in product design. During product reliability design and analysis, environmental effects knowledge reusing is achieved. First, the relationship of environmental effects and product reliability is analyzed. Then environmental effects ontology to describe environmental effects domain knowledge is designed. Related concepts of environmental effects are formally defined by using the ontology approach. This model can be applied to arrange environmental effects knowledge in different environments. Finally, rubber seals used in the subhumid acid rain environment are taken as an example to illustrate ontological model application on reliability design and analysis

Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization

[EN] The paper aims to identify the individuals who influence the knowledge sharing processes from an internal social network and to forecast the future knowledge flows that may cross it. Exploratory research is employed, and a four-phase methodology is developed which combines a social network analysis with structural modeling. This is applied to the internal enterprise social network used by a British insurance company. The main results emphasize the most influential groups, their relationships, future knowledge flows, and the connection between the network's heterogeneity and structure, and employees' future knowledge sharing intention. These findings have both theoretical and practical implications. The theory is extended by proving that a social network analysis can be used as a tool for evaluating and predicting future knowledge flows. At the same time, a solution is offered to decision-makers so they will be able to: (i) identify the potential knowledge loss; (ii) determine leaders; (iii) establish who is going to act as a knowledge diffuser, by sharing what they know with their coworkers, and who is going to act as a knowledge repository, by focusing on acquiring increasingly more knowledge; (iv) identify the elements that influence employees' future knowledge sharing intention. (C) 2016 Elsevier Inc. All rights reserved."The research reported in this paper is supported by the European Commission for the project "Engaging in Knowledge Networking via an interactive 3D social Supplier Network (KNOWNET)" (FP7-PEOPLE-2013-IAPP 324408)".Leon, R.; Rodríguez Rodríguez, R.; Gómez-Gasquet, P.; Mula, J. (2017). Social network analysis: A tool for evaluating and predicting future knowledge flows from an insurance organization. Technological Forecasting and Social Change. 114:103-118. https://doi.org/10.1016/j.techfore.2016.07.032S10311811

An ontology-based semantic building post-occupancy evaluation framework and its application

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonCatering to sustainable development in Architecture, Engineering and Construction (AEC) industry, many building performance evaluation (BPE) schemas have been developed to support building assessment and aim to narrow down the performance gap. Post-Occupancy Evaluation (POE), viewed as a sub-process of BPE, is a systematic method to obtain feedback on building performance in use. However, building evaluation is a complex and knowledge-intensive process with scattered and fragmented knowledge, it is time-consuming and error-prone to acquire explicit knowledge. Benefiting from the advantages of Semantic Web technology in knowledge conceptualization, ontology, as the core of the Semantic Web, has been widely taken as an effective method for knowledge management, information representation and extraction, and logical inference in the AEC industry, especially in the BPE field. However, most of the existing ontologies in the AEC industry are lightweight ontologies that mainly focus on building a structured system to represent the specific domain knowledge or information, without developing formal axioms and constraints to provide higher expressivity. Moreover, the research focus of ontology in building assessment is mainly on energy-related fields, and there is not a comprehensive POE ontology yet, especially with the focus on building occupant satisfaction, which is the starting point of this research. This research develops an ontology-based post-occupancy evaluation framework dedicated to building performance assessment, with the ultimate aim of optimizing building operation and improving building occupants' use experience quality and well-being. In the developed framework, a heavyweight ontology is developed to structure the fragmented building performance assessment knowledge in the POE domain. In POE ontology, the building occupants' needs for building performance are generalized and classified, and the corresponded building performance assessment knowledge is formalized. In addition, a set of SWRL (Semantic Web Rule Language) rules and SQWRL (Semantic Query-Enhanced Web Rule Language) query rules are developed based on the benchmarking evaluation axioms to enable automatic rule-based reasoning and query in different identified application scenarios. This ontology model enables effective POE-related knowledge retrieving and sharing, and promotes its implementation in the POE domain. To validate the developed framework, a case study is carried out facilitated by the Building Use Studies (BUS) Methodology to illustrate its feasibility and effectiveness in different application scenarios. This research concludes that the proposed ontology-based POE framework has the capability to conduct a multi-objective and multi-criteria POE assessment at the building operation stage and provide a multi-criteria optimised solution

An ontology-based holistic approach for multi-objective sustainable structural design

Building construction industry has significant impact on sustainability. The construction, operation and maintenance of buildings account for approximately 50% of global energy usage and anthropogenic greenhouse gas (GHG) emissions. In recent years, the embodied energy and carbon are identified increasingly important in terms of sustainability throughout building life cycle. Incorporation of sustainable development in building structural design becomes undoubtedly crucial. The effective building design requires smart and holistic tools that can process multi-objective and inter-connected domain knowledge to provide genuine sustainable buildings. With the advancement of information and communication technologies, various methods and techniques have been applied to accomplish the multiple objectives of sustainable development in building design. One of the most successful approaches is building information modelling (BIM), which requires further enhancement of interoperability. The emergence of Semantic Web technology provides more opportunity to improve the information modelling, knowledge management and system integration. The research presented in this thesis investigates how ontology and Semantic Web rules can be used in a knowledge-based holistic system, in order to integrate information about structural design and sustainability, and facilitate decision-making in design process by recommending appropriate solutions for different use cases. A research prototype namely OntoSCS incorporating OWL ontology and SWRL rules has been developed and tested in typical structural design cases. The holistic approach considers five inter-connected dimensions of sustainability, including structural feasibility, embodied energy and carbon, cost, durability and safety. In addition, the selection of structural material supplier and criteria in sustainability assessment are taken into account as well. This research concludes that the Semantic Web technology can be applied to structural design at early stage to provide multi-criteria optimised solution. The methodology and framework employed in this study can be further adapted as a generic multi-criteria and holistic decision support system for other domains in construction sector

Análise de riscos e efeitos no projeto informacional e conceitual: uma abordagem ontológica.

Este trabalho apresenta em seu início uma revisão histórica das publicações sobre FMEA enfatizando os problemas e soluções apresentadas na literatura com a finalidade de identificar como a metodologia evolui ao longo do tempo. A análise das publicações mostrou que a FMEA, apesar de sempre citada como o primeiro dos métodos de avaliação de riscos, é executada por exigências contratuais ou normativas, normalmente realizado ao final do processo, inclusive no desenvolvimento de novos produtos, com um time comprometido com o cumprimento de prazos e não com o resultado da análise, além das dificuldades relativas a classificação dos riscos. Permitiu também entender que o processo de realização da FMEA para identificar os modos de falha quase não sofreu alterações ao longo do tempo. Desta forma, o presente trabalho propõe uma nova abordagem para a Análise de Modo e Efeitos de Falha, mais visual e interativa para possibilitar uma mudança na forma integrar a equipe, melhorando o processo de geração de ideias, estimulando a reflexão e a identificação de novos caminhos para a interpretação dos modos de falha, aqui denominada de Análise de Modos de Falha canvas ou FMEA canvas. A abordagem pretende suprir a necessidade de identificar modos de falha na fase informacional do processo de desenvolvimento de produtos, principalmente na área médica, onde os produtos se tornaram muito complexos e caros, justificando este trabalho. O texto mostra como utilizar o canvas e os resultados de uma aplicação utilizando o método de Soft System Methodology em uma empresa que desenvolve produtos médicos. Os resultados apontados pela empresa que participou da avaliação do FMEA canvas mostraram que o formato seria bem-sucedido nas fases iniciais do processo de desenvolvimento de produtos, pois permite a integração do time de desenvolvimento inclusive com a equipe gerencial, apresenta-se mais compreensível, dinâmico e colaborativo e que a utilização de notas autoadesivas trouxe flexibilidade ao andamento das discussões, estimulando a criatividade, a revisão contínua do andamento da resolução dos modos de falha e a facilidade na recuperação das informações ao longo do processo, além de outras vantagens quando utilizada dentro dos limites para os quais foi planejado

A knowledge-based framework for information extraction and exploration

Harnessing insights from the colossal amount of online information requires the computerised processing of unstructured text in order to satisfy the information need of particular applications such as recommender systems and sentiment analysis. The increasing availability of online documents that describe domain-specific information provides an opportunity in employing a knowledge-based approach in extracting information from Web data. In this thesis, a novel comprehensive knowledge-based framework is proposed to construct and exploit a domain-specific semantic knowledgebase. The proposed framework introduces a methodology for linking several components of different techniques and tools. It focuses on providing reusable and configurable data and application templates, which allow developers to apply it in diversity of domains. The objectives of this framework are: extracting information from unstructured data, constructing a semantic knowledgebase from the extracted information, enriching the resultant semantic knowledgebase by sourcing appropriate semi-structured and structured datasets, and consuming the resultant semantic knowledgebase to facilitate the intelligent exploration and search of information. For the purpose of investigating the challenges of extracting and modelling information in a specific domain, the financial domain was employed as a use-case in the context of a stock investment motivating scenario. The developed knowledge-based approach exploits the semantic and syntactic characteristics of the problem domain knowledge in implementing a hybrid approach of Rule-based and Machine Learning based relation classification. The rule-based approach is adopted in the Natural Language Processing tasks associated with linguistic and structural features, Named Entity Recognition, instances labelling and feature generation processes. The results of these tasks are used to classify the relations between the named entities by employing the Machine Learning based relation classification. In addition, the domain knowledge is analysed to benefit knowledge modelling by translating the domain key concepts into a formal ontology. This ontology is employed in constructing semantic knowledgebase from unstructured online data of a specific domain, enriching the resulting semantic knowledgebase by sourcing semi-structured and structured online data sources and applying advanced classifications and inference technologies to infer new and interesting facts to improve the decision-making and intelligent exploration activities. However, most relations are non-binary in the problem domain knowledge because of its specific characteristic hence an appropriate N-ary relation patterns technique were adopted and investigated. A serious of a novel experiments were conducted to implement and configure a Machine Learning based relation classification. The experimental evaluation evidenced that the developed knowledge-assisted ML relation classification model, which was further boosted by our implementation of GAs to reduce the feature space, has resulted in significant improvement in the process of relation extraction. The experimental results also indicate that amongst the implemented ML algorithms, SVM exhibited the best relation classification accuracy in the majority of the training datasets, while retaining acceptable levels of accuracy in the rest in the remaining training datasets. Web Ontology Language (OWL) reasoning and rule-based reasoning on the resultant semantic knowledgebase were applied to derive stock investment specific recommendations. In addition, SPARQL query language was employed to explore the semantic knowledgebase. Moreover, taking into consideration the problem domain's requirements for modelling non-binary relations, a relation-as-class N-ary relations pattern was implemented, and the reasoning axioms and query language were adjusted to fit the intermediate resources in the N-ary relations requirements. In this thesis also the experience on addressing the challenges of implementing the proposed knowledge-based framework for constructing and exploiting a semantic knowledgebase were summarised. These challenges can be considered by domain experts and knowledge engineers as a novel methodology for employing the Semantic Web Technologies for the knowledge user to intelligently exploit knowledge in similar problem domains. The evaluation of knowledge accessibility by utilising Semantic Web Technologies in the developed application includes the ability of data retrieval to obtain either the entire or some portion of the data from the semantic knowledgebase for a particular use-case scenario. Investigating the tasks of reasoning, accessing and querying the semantic knowledgebase evidences that Semantic Web Technologies can perform an accurate and complex knowledge representation to share Knowledge from a diversity of data sources and, improve the decision‑making process and the intelligent exploration of the semantic knowledgebase