Ontology Domain Modeling Support for Multilingual Servicies in e-commerce: MKBEEM

One of the main objectives of a truly user-friendly Information Society is to focus on advanced human language technologies enabling cost-effective interchange across language and culture and more natural interfaces to digital services. The recently launched IST-1999-10589 project MKBEEM (Multilingual Knowledge Based European Electronic Marketplace, 1st Feb. 2000 - 1st Aug. 2002) is rightly in that direction and the work will address basically, written language technologies and its use in the key sector of global business and electronic commerce. In particular MKBEEM will focus on adding multilinguality to all stages of the information cycle, including multilingual content generation and maintenance, automated translation and interpretation and enhancing the natural interactivity and usability of the service with unconstrained language input. On the Knowledge engineering side, the MKBEEM Ontologies will provide a consensual representation of the electronic commerce field in three typical Domains (Tourism, Mail order, Retailers) allowing the exchanges independently of the language of the end user, the service, or the content provider. Ontologies will be used for classifying and indexing catalogues, for filtering user’s query, for facilitating multilingual man-machine dialogues between user and software agent, and for inferring information that is relevant to the user’s request. This paper concentrates on ontology issues, while the used human language processing approaches will be presented closely in our later papers

E-business Environment in the Global Information Society

In today´s digital 21st century, almost all businesses face intense competition from competitors all around the globe. There are no borders and business area for the all companies is almost unlimited. As the main supports of mentioned fact are globalization and ICT´s development. Influences such as globalization, increased popularity of outsourcing and offshoring have recently combined to produce an environment where ICT graduates need to have up-to-date and industry-relevant knowledge and skills, so that they can be successful in this highly competitive environment. Development of e-business and e-commerce make possible the companies to enter to the global markets. Fundamental prerequisite of the successful company in the global market is well-made corporate strategy and correct source information. Of high account condition of an entry to global markets is an adjustment of the information system to global information and business system management standards. The statistics gained from the Czech market shows, that in spite of enormous Internet proliferation the ratio of e-sales is lower than the ratio of e-purchases. Some possible reasons for these phenomena are discussed at the end of this paper.E-business, e-commerce, global information society, information and communication technology, IT security, IT standards, business strategy, enterprise resource planning, customer relationship management.

Towards effectively appraising online stores

This paper introduces research being carried out into the measurement of the quality of e-commerce systems. Considerable work has been done on software metrics in the last few decades but e-commerce specific metrics seem only applicable to already deployed systems. It is proposed that a set of metrics is needed, which can be applied from the earlier stages of E-Commerce system development to improve risk management. This paper attempts to appraise e-commerce systems by proposing a set of essential attributes for an e-commerce site to succeed. This paper also serves as groundwork for future e-commerce metrication work based on these same attributes.peer-reviewe

English as the Infrastructure Language for a Multilingual Internet

English has been the dominant language of the Internet for users and infrastructure development. This is changing due to the increasing number of non-English speaking Internet users. While the Internet community will eventually become a multilingual environment; the development of the Internet infrastructure source will still be English. This will have an impact on global businesses and government’s education systems in non-English speaking countries. Eventually, English will be the global language for Internet developers and professionals of global businesses

Overcoming Language Barriers in Business-To-Consumer Electronic Service

Communication has been described as one of the determinants of service quality. However, communication is only effective when the parties involved speak the same language. This is almost impossible to achieve in Business-To-Consumer (B2C) Electronic Commerce (e-Commerce) given the diversity of languages used on the Internet. This paper seeks to explore the possibility of using current advances in technology to bridge the communication gap among entities on the Internet

Examining the Impact of Culture and Language on the User Acceptance of the Media Website in Jordan

This study examines the website acceptance based on the information system quality and the impact of cultural dimensions and language components on the actual acceptance and usage of the identified media websites. Based on the data from three groups of users, namely the users of bbc.com to represent the purely English media websites, the al-jazeera.com representing the semi-localized media website and the al-rai.com representing the local websites. Questionnaires were administered to 420 internet users in different regions in Jordan. The questionnaire measures nine parameters which include the system accessibility, the response time, information quality, cultural adaptation, Arabic language, perceived ease of use, perceived usefulness, attitudes towards using the websites and the behavioral intention to use the websites. Capitalizing on the quantitative research methodology by expanding the technology acceptance model for the research framework, the findings showed that the cultural dimensions of power distance, collectivism, masculinity and uncertainty avoidance have the positive impact on the Jordanian users’ preference of the media websites. The conclusions are drawn from the positive impact of cultural adaptation on the perceived ease of use of the local websites and also on the users’ attitudes towards the use of the local websites. However, there is a negative impact based on the lack of cultural adaptation on the users’ attitudes towards the use of English originated websites. Similarly, there is no significant impact of the cultural adaptation on the users’ attitudes towards the use of the semi- localized websites. The research findings showed that the websites’ information system quality, the Arabic language usage and the Arabic cultural adaptation have positive impacts on the Jordanian users’ perceptions and acceptance in choosing the media websites as preferred websites

Application of pre-training and fine-tuning AI models to machine translation: a case study of multilingual text classification in Baidu

With the development of international information technology, we are producing a huge amount of information all the time. The processing ability of information in various languages is gradually replacing information and becoming a rarer resource. How to obtain the most effective information in such a large and complex amount of multilingual textual information is a major goal of multilingual information processing. Multilingual text classification helps users to break the language barrier and accurately locate the required information and triage information. At the same time, the rapid development of the Internet has accelerated the communication among users of various languages, giving rise to a large number of multilingual texts, such as book and movie reviews, online chats, product introductions and other forms, which contain a large amount of valuable implicit information and urgently need automated tools to categorize and process those multilingual texts. This work describes the Natural Language Process (NLP) sub-task known as Multilingual Text Classification (MTC) performed within the context of Baidu, a Chinese leading AI company with a strong Internet base, whose NLP division led the industry in deep learning technology to go online in Machine Translation (MT) and search. Multilingual text classification is an important module in NLP machine translation and a basic module in NLP tasks. It can be applied to many fields, such as Fake Reviews Detection, News Headlines Categories Classification, Analysis of positive and negative reviews and so on. In the following work, we will first define the AI model paradigm of 'pre-training and fine-tuning' in deep learning in the Baidu NLP department. Then investigated the application scenarios of multilingual text classification. Most of the text classification systems currently available in the Chinese market are designed for a single language, such as Alibaba's text classification system. If users need to classify texts of the same category in multiple languages, they need to train multiple single text classification systems and then classify them one by one. However, many internationalized products do not have a single text language, such as AliExpress cross-border e-commerce business, Airbnb B&B business, etc. Industry needs to understand and classify users’ reviews in various languages, and have conducted in-depth statistics and marketing strategy development, and multilingual text classification is particularly important in this scenario. Therefore, we focus on interpreting the methodology of multilingual text classification model of machine translation in Baidu NLP department, and capture sets of multilingual data of reviews, news headlines and other data for manual classification and labeling, use the labeling results for fine-tuning of multilingual text classification model, and output the quality evaluation data of Baidu multilingual text classification model after fine-tuning. We will discuss if the pre-training and fine-tuning of the large model can substantially improve the quality and performance of multilingual text classification. Finally, based on the machine translation-multilingual text classification model, we derive the application method of pre-training and fine-tuning paradigm in the current cutting-edge deep learning AI model under the NLP system and verify the generality and cutting-edge of the pre-training and fine-tuning paradigm in the deep learning-intelligent search field.Com o desenvolvimento da tecnologia de informação internacional, estamos sempre a produzir uma enorme quantidade de informação e o recurso mais escasso já não é a informação, mas a capacidade de processar informação em cada língua. A maior parte da informação multilingue é expressa sob a forma de texto. Como obter a informação mais eficaz numa quantidade tão considerável e complexa de informação textual multilingue é um dos principais objetivos do processamento de informação multilingue. A classificação de texto multilingue ajuda os utilizadores a quebrar a barreira linguística e a localizar com precisão a informação necessária e a classificá-la. Ao mesmo tempo, o rápido desenvolvimento da Internet acelerou a comunicação entre utilizadores de várias línguas, dando origem a um grande número de textos multilingues, tais como críticas de livros e filmes, chats, introduções de produtos e outros distintos textos, que contêm uma grande quantidade de informação implícita valiosa e necessitam urgentemente de ferramentas automatizadas para categorizar e processar esses textos multilingues. Este trabalho descreve a subtarefa do Processamento de Linguagem Natural (PNL) conhecida como Classificação de Texto Multilingue (MTC), realizada no contexto da Baidu, uma empresa chinesa líder em IA, cuja equipa de PNL levou a indústria em tecnologia baseada em aprendizagem neuronal a destacar-se em Tradução Automática (MT) e pesquisa científica. A classificação multilingue de textos é um módulo importante na tradução automática de PNL e um módulo básico em tarefas de PNL. A MTC pode ser aplicada a muitos campos, tais como análise de sentimentos multilingues, categorização de notícias, filtragem de conteúdos indesejados (do inglês spam), entre outros. Neste trabalho, iremos primeiro definir o paradigma do modelo AI de 'pré-treino e afinação' em aprendizagem profunda no departamento de PNL da Baidu. Em seguida, realizaremos a pesquisa sobre outros produtos no mercado com capacidade de classificação de texto — a classificação de texto levada a cabo pela Alibaba. Após a pesquisa, verificamos que a maioria dos sistemas de classificação de texto atualmente disponíveis no mercado chinês são concebidos para uma única língua, tal como o sistema de classificação de texto Alibaba. Se os utilizadores precisarem de classificar textos da mesma categoria em várias línguas, precisam de aplicar vários sistemas de classificação de texto para cada língua e depois classificá-los um a um. No entanto, muitos produtos internacionalizados não têm uma única língua de texto, tais como AliExpress comércio eletrónico transfronteiriço, Airbnb B&B business, etc. A indústria precisa compreender e classificar as revisões dos utilizadores em várias línguas. Esta necessidade conduziu a um desenvolvimento aprofundado de estatísticas e estratégias de marketing, e a classificação de textos multilingues é particularmente importante neste cenário. Desta forma, concentrar-nos-emos na interpretação da metodologia do modelo de classificação de texto multilingue da tradução automática no departamento de PNL Baidu. Colhemos para o efeito conjuntos de dados multilingues de comentários e críticas, manchetes de notícias e outros dados para classificação manual, utilizamos os resultados dessa classificação para o aperfeiçoamento do modelo de classificação de texto multilingue e produzimos os dados de avaliação da qualidade do modelo de classificação de texto multilingue da Baidu. Discutiremos se o pré-treino e o aperfeiçoamento do modelo podem melhorar substancialmente a qualidade e o desempenho da classificação de texto multilingue. Finalmente, com base no modelo de classificação de texto multilingue de tradução automática, derivamos o método de aplicação do paradigma de pré-formação e afinação no atual modelo de IA de aprendizagem profunda de ponta sob o sistema de PNL, e verificamos a robustez e os resultados positivos do paradigma de pré-treino e afinação no campo de pesquisa de aprendizagem profunda

Studying How E-Markets Evaluation Can Enhance Trust in Virtual Business Communities

One of the major drawbacks of conducting business online is the raised level of risk associated with business transactions. Potential business partners usually have limited information about each others reliability or product / service quality before an online transaction. In this paper, we focus on the problem of selecting a trustful electronic market (e-market), in order to perform business transactions with it. In particular, we examine how the decision of selecting an appropriate e-market can be facilitated by an e-market recommendation algorithm. For this purpose, a metadata model for collecting and storing e-market evaluations from the members of a virtual business community in a reusable and interoperable manner is introduced. Then, an e-market recommendation algorithm that can synthesize existing e-market evaluations stored using the metadata model, is designed. Finally, a scenario of how the presented e-market recommendation algorithm can support a virtual agribusiness community of the organic agriculture sector is discussed.E-market, metadata, recommender system, virtual community, Institutional and Behavioral Economics, Marketing,