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There is a growing demand for statistics to better understand the globalisation that is accelerating due to the removal of barriers in international trade and to technological progress. Key players in globalisation are the multinational enterprise (MNE) groups that have increased in number and complexity and need to be properly represented by macroeconomic and business statistics. To deal with this need, the European Union Member States, the European Free Trade Association (EFTA) countries and Eurostat have collaborated to create the EuroGroups Register (EGR). This paper explores the use of web intelligence to improve the accuracy and completeness of the EGR, which makes use of tools for extracting and exploring information from the World Wide Web. Additionally, it presents a methodology to assess the quality of the information retrieved from the web, based on an ex-post comparison with the official information contained in the EGR. The results provide indications about the possibility of using web intelligence tools to support MNE group monitoring and to complete the EGR data in cases where this information is missing. Finally, it dives deeply into the approach to harness Wikipedia as a data source and into the techniques and methodology used to extract data from this source. [ABSTRACT FROM AUTHOR]

This document is a call for a new era of artificial intelligence (AI) in the connected world, specifically focusing on the field of Web Intelligence (WI). WI has evolved over the past 20 years, with three significant stages: WI 1.0 (Wisdom Web), WI 2.0 (Wisdom Web of Thing), and WI 3.0 (Wisdom Web of Everything). The document highlights several papers published in the Web Intelligence journal that explore different aspects of WI, including its impact on a better connected world, cross-cutting areas with brain informatics, biases in recommender systems, the influence of ChatGPT on information retrieval, the role of big data in the retail industry, order pairing mechanisms in real-time ride-sharing, data replication in cloud systems, and security risks in IoT-fog architecture. The document calls for more collaboration and communication among researchers in the WI field and emphasizes the importance of global interactions for co-intelligence and co-creation. [Extracted from the article]

Exponential increase in the quantity of user generated content in websites and social networks have resulted in the emergence of web intelligence approaches. Several natural language processing (NLP) tools are commonly used to examine the large quantity of data generated online. Particularly, sentiment analysis (SA) is an effective way of classifying the data into different classes of user opinions or sentiments. The latest advances in machine learning (ML) and deep learning (DL) approaches offer an intelligent way of analyzing sentiments. In this view, this study introduces a web intelligence with enhanced sunflower optimization based deep learning model for sentiment analysis (WIESFO-DLSA) technique. The major intention of the WIESFO-DLSA technique is to identify the expressions or sentiments that exist in the social networking data. The WIESFO-DLSA technique initially performs pre-processing and word2vec feature extraction processes to generate a meaningful set of features. At the same time, bidirectional long short term memory (BiLSTM) model is applied for classification of sentiments into different class labels. Moreover, an enhanced sunflower optimization (ESFO) algorithm is exploited to optimally adjust the hyperparameters of the BiLSTM model. A wide range of simulation analyses is performed to report the better outcomes of the WISFO-DLSA technique and the experimental outcomes ensured its promising performance under several measures. [ABSTRACT FROM AUTHOR]

The convergence of systems neuroscience and open science arouses great interest in the current brain big data era, highlighting the thinking capability of intelligent agents in handling multi-source knowledge, information and data across various levels of granularity. To realize such thinking-inspired brain computing during a brain investigation process, one of the major challenges is to find a holistic brain map that can model multi-dimensional variables of brain investigations across brain functions, experimental tasks, brain data and analytical methods synthetically. In this paper, we propose a context-enhanced graph learning method to fuse open knowledge from different sources, including: contextual information enrichment, structural knowledge fusion, and holistic graph learning. Such a method can enhance contextual learning of abstract concepts and relational learning between two concepts that have large gap from different dimensions. As a result, an extensible space, namely Thinking Space, is generated to represent holistic variables and their relations in a map, which currently contributes to the field of brain research for systematic brain computing. In the future, the Thinking Space coupled with the rapid development and spread of artificial intelligence generated content will be developed in more scenarios so as to promote global interactions of intelligence in the connected world. [ABSTRACT FROM AUTHOR]

The availability of interoperability standards allows using a uniform model for integrating technologies and designing collaborating applications with a shared understanding of the exchanged information. In this perspective, we propose here the design and development of an agent-based platform that uses an interoperability framework to build a diffused museum in a cyber-physical system, where intelligent agents deliver cultural content through innovative interaction models. In particular, agents’ awareness, natural language understanding capability, and interaction ability are extended through the monitoring of user’s interaction with the IIIF (International Image Interoperability Framework) standard and the integration with IIIF compliant technologies. The scalability of the proposed solution is evaluated stressing the platform with simulated workloads.

This paper studies the relations between outside properties and personalities of created characters in comics/anime. Fans of comics/animes can generally predict the personality of a character from how the character is expressed in the drawing. From this facţ it seems that people have some common unconscious senses for relationships between outside properties and the inside personality of characters. We propose a method to study the relationships between outside properties and personality by calculating the conditional probability for certain attributes. We analyze the dataset of Character Elements Kingdom, which is a fan site of characters appearing in comics and anime. Given the appearance or personality as the conditional attribute, the posterior probability distribution for a category of attributes is obtained. From the posterior probability distribution, we discuss the relationships between outside properties and personalities of created characters. Through the discussion, we confirmed that there were some tendencies for the relationships between appearance and personality, e.g., "black hair girls seemed to be relatively stable though blond hair girls seemed to be relatively active." These findings suggested typical patterns of designing created characters in Japanese comics/animes. We believe the knowledge obtained through our study can be referenced for creating new characters in some narrative content. [ABSTRACT FROM AUTHOR]

In recent times, web intelligence (WI) has become a hot research topic, which utilizes Artificial Intelligence (AI) and advanced information technologies on theWeb and Internet. The users post reviews on social media and are employed for sentiment analysis (SA), which acts as feedback to business people and government. Proper SA on the reviews helps to enhance the quality of the services and products, however, web intelligence techniques are needed to raise the company profit and user fulfillment.With this motivation, this article introduces a new modified pigeon inspired optimization based feature selection (MPIO-FS) with Bayesian deep learning (BDL), named MPIOBDL model for SA on WI applications. The presented MPIO-BDL model initially involved preprocessing and feature extraction take place using Term Frequency—Inverse Document Frequency (TF-IDF) technique to derive a useful set of information from the user reviews. Besides, the MPIO-FS model is applied for the selection of optimal feature subsets, which helps to enhance classification accuracy and reduce computation complexity. Moreover, the BDL model is employed to allocate the proper class labels of the applied user review data. A comprehensive experimental results analysis highlighted the improved classification efficiency of the presented model. [ABSTRACT FROM AUTHOR]

Understanding human intelligence, especially brain intelligence, is the cornerstone of reaching the ultimate AI. In this paper, we briefly review the historical interactions between AI and brain science, and look towards the future vision of AI in the connected world. In particular, we introduce two rapidly developing fields in Web Intelligence (WI, AI in the Connected World) and Brain Informatics (BI, the brain/mind-centric study and the application of brain-machine intelligence), and combine them to accelerate the arrival of a human–level AI society. Furthermore, combining these two fields by connecting AI and brain science with big data, creates a new vision from the systematic brain-machine intelligence research to new AI industry chain in the connected social-cyber-physical-thinking spaces. [ABSTRACT FROM AUTHOR]

Background: The lack of availability of disability data has been identified as a major challenge hindering continuous disability equity monitoring. It is important to develop a platform that enables searching for disability data to expose systemic discrimination and social exclusion, which increase vulnerability to inequitable social conditions. Objective: Our project aims to create an accessible and multilingual pilot disability website that structures and integrates data about people with disabilities and provides data for national and international disability advocacy communities. The platform will be endowed with a document upload function with hybrid (automated and manual) paragraph tagging, while the querying function will involve an intelligent natural language search in the supported languages. Methods: We have designed and implemented a virtual community platform using Wikibase, Semantic Web, machine learning, and web programming tools to enable disability communities to upload and search for disability documents. The platform data model is based on an ontology we have designed following the United Nations Convention on the Rights of Persons with Disabilities (CRPD). The virtual community facilitates the uploading and sharing of validated information, and supports disability rights advocacy by enabling dissemination of knowledge. Results: Using health informatics and artificial intelligence techniques (namely Semantic Web, machine learning, and natural language processing techniques), we were able to develop a pilot virtual community that supports disability rights advocacy by facilitating uploading, sharing, and accessing disability data. The system consists of a website on top of a Wikibase (a Semantic Web--based datastore). The virtual community accepts 4 types of users: information producers, information consumers, validators, and administrators. The virtual community enables the uploading of documents, semiautomatic tagging of their paragraphs with meaningful keywords, and validation of the process before uploading the data to the disability Wikibase. Once uploaded, public users (information consumers) can perform a semantic search using an intelligent and multilingual search engine (QAnswer). Further enhancements of the platform are planned. Conclusions: The platform ontology is flexible and can accommodate advocacy reports and disability policy and legislation from specific jurisdictions, which can be accessed in relation to the CRPD articles. The platform ontology can be expanded to fit international contexts. The virtual community supports information upload and search. Semiautomatic tagging and intelligent multilingual semantic search using natural language are enabled using artificial intelligence techniques, namely Semantic Web, machine learning, and natural language processing. [ABSTRACT FROM AUTHOR]

Performing company valuations within the domain of biotechnology, pharmacy and medical technology is a challenging task, especially when considering the unique set of risks biotech start-ups face when entering new markets. Companies specialized in global valuation services, therefore, combine valuation models and past experience with heterogeneous metrics and indicators that provide insights into a company's performance. This paper illustrates how automated knowledge discovery, extraction and data fusion can be used to (i) obtain additional indicators that provide insights into the success of a company's product development efforts, and (ii) support labor-intensive data curation processes. [ABSTRACT FROM AUTHOR]

Web farming can advance computational social science into a never-end learning process, in which social phenomena are dynamically and scientifically understood based on continuously produced, updated and expired data in the connected hyper world. Named entity recognition is a basic and core task of Web farming. However, the existing named entity recognition methods mainly depend on the complete, high-quality and well-labelled data sets and cannot meet the requirements of real-world applications. This paper proposes a continuous learning method for recognizing named entity by introducing the Web farming mode of Web Intelligence into the recognizing process. During the on-line stage, the domain contextual relevance of candidate entities is calculated by using the domain discrimination degree and the domain dependence function for recognizing the target entities. During the off-line stage, an active learning approach is designed to continuously improve the target corpus set by binding density-based clustering with semantic distance measurement. Experimental results show that the proposed method can effectively improve the accuracy of entity recognition and is more suitable for real-world applications. [ABSTRACT FROM AUTHOR]

Information retrieval systems like web search engines can be used to meet the user's information needs by searching and retrieving the relevant documents that match the user's query. Firstly, the query is inputted to the web search engine and assumed to be a good representative for the user's intention and reflecting specifically his information needs and thus it should be long enough, discriminative, specific and unambiguous. Secondly, the web search engine typically respond to the query by sending back a long flat list of web search results and each search result represents a relevant document. Typically, that list may contain thousands or millions of web search results and thus it is difficult to navigate and locate a specific document relevant to a specific topic. As a postretrieval process, web search results clustering may be a solution for this issue where web search results can be categorized as clusters. These clusters supposed to contain topically related documents and labelled by descriptive and concise labels. These labels supposed to correctly describe the contents of each cluster. Thus the users can easily choose a cluster representing the intended topic and navigate through relatively few documents inside that cluster. High-quality labelling for clusters is crucial for users who can now gain insight into that clusters'; contents, general structure, and distribution of the topics among documents in the clusters. This make the user able to preview and navigate easily and fast. To this end, the authors in this paper introduced a methodology to enhance labels for clusters of web search results. The proposed methodology is founded on the idea of using the existing labels nominated by the original Suffix Tree Clustering (STC) algorithm and adapting these labels and/or clusters so that it become more concise and descriptive. The propose methodology was conducted on the original STC algorithm to produce an enhanced version of the classical STC algorithm. The enhanced algorithm was experimented and the produced clusters and labels were evaluated and compared with respect to the classical STC algorithm. For evaluation, the authors used clusters labelling performance measure considered five parameters f1: Comprehensibility, f2: Descriptiveness, f3: Discriminative Power, f4: Uniqueness, and f5: Nonredundancy. The reported results shown that the new enhanced labels outperformed the original labels and the overall performance has been enhanced. The recorded results indicated that: (i) The proposed methodology achieved better performance and the overall average recorded values for the used performance measure (f6) was 0.921. (ii) Number of clusters was decreased from 15 to 9 clusters only. (iii) Number of duplicated results was decreased from 143 to 121 only, and (iv) average number of phrases per label was increased from 1.67 to 2.00 phrases.

Today, understanding customer satisfaction is becoming a difficult and complex task for companies due to the explosive growth of the voice of the customer in online reviews. This has pushed companies to rethink their business strategies and resort to business intelligence techniques in order to help them in analyzing customer requirements and market trends. This paper proposes a decision support framework for dynamically transforming the voice of the customer data into actionable insight. The framework measures the customer satisfaction by extracting key products' aspects along with customers' sentiments from online reviews using a text mining technique: the latent Dirichlet allocation approach. We apply the Fuzzy-Kano model to classify the real customer requirements, then, map them dynamically to the SWOT matrix. The proposed approach is extensively tested on an empirical dataset based on several performance metrics including accuracy, precision, recall, and F-score. The reported results showed that latent Dirichlet allocation approach has correctly extracted aspects with 97.4% accuracy and 92.4% precision. [ABSTRACT FROM AUTHOR]

The challenge in today's corporations is that even though the technology portfolio of a company plays a crucial role in delivering revenue--falling as a topic mainly under the area of technology management--technology may have a negative image due to observed risks or failing the sustainability criteria. It may influence the company's image and brand image, possibly also influencing decisions at corporate level. The monitoring of technology sentiments is therefore emphasized, benefiting from the advanced methods for business environment scanning, namely market and competitor intelligence functions. This paper utilizes a new big data based method, mostly utilized in market(MI)/competitor intelligence(CI) functions of the company, opinion mining, to analyse the global media sentiment of nuclear power and projects deploying the technology. With this approach, it is easier to understand the linkage to corporate images of companies deploying the technology and also related corporate decisions, mainly done in the areas of technology market deployment, marketing and strategic planning. The results indicate how the media sentiment towards nuclear power has been mostly negative globally, particularly in social media. In addition, results from similar analyses from a single company's images for the companies currently deploying the technology are seemingly less negative, indicating the influence of company's communication and branding activities. This paper has implications showing that a technology's media sentiment can influence a company's brand image, marketing communications and the need for actions when technology is deployed. In conclusion, there seems to be a need for better co-operation between different corporate functions, namely technology management, MI, marketing and strategic planning, in order to indicate technology image impacts and also counteract firestorms from social media. [ABSTRACT FROM AUTHOR]

Recent developments in the fields of computer science, such as advances in the areas of big data, knowledge extraction, and deep learning, have triggered the application of data-driven research methods to disciplines such as the social sciences and humanities. This article presents a collaborative, interdisciplinary process for adapting data-driven research to research questions within other disciplines, which considers the methodological background required to obtain a significant impact on the target discipline and guides the systematic collection and formalization of domain knowledge, as well as the selection of appropriate data sources and methods for analyzing, visualizing, and interpreting the results. Finally, we present a case study that applies the described process to the domain of communication science by creating approaches that aid domain experts in locating, tracking, analyzing, and, finally, better understanding the dynamics of media criticism. The study clearly demonstrates the potential of the presented method, but also shows that data-driven research approaches require a tighter integration with the methodological framework of the target discipline to really provide a significant impact on the target discipline.