Semantic Web Service Engineering: Annotation Based Approach

Web services are an emerging paradigm which aims at implementing software components in the Web. They are based on syntactic standards, notably WSDL. Semantic annotation of Web services provides better qualitative and scalable solutions to the areas of service interoperation, service discovery, service composition and process orchestration. Manual annotation is a time-consuming process which requires deep domain knowledge and consistency of interpretation within annotation teams. Therefore, we propose an approach for semi-automatically annotating WSDL Web services descriptions. This is allowed by Semantic Web Service Engineering. The annotation approach consists of two main processes: categorization and matching. Categorization process consists in classifying WSDL service description to its corresponding domain. Matching process consists in mapping WSDL entities to pre-existing domain ontology. Both categorization and matching rely on ontology matching techniques. A tool has been developed and some experiments have been carried out to evaluate the proposed approach

Use of a Fast Information Extraction Method as a Decision Support Tool

Ad-hoc extraction of information from documents can ensure the transparency of decisions made by an organization. Different Information Extraction methods have been applied to extract information from various domains. Most widely known methods use manually annotated training documents that require high development time. The automated training methods are not scalable to large application domains. We have developed a semi-automated knowledge-engineering method for building the knowledge-base with minimal efforts. Because our method reduces manual processing of the training data, the development process is very fast. We have developed a prototype application to extract information from the project-reports of the American Recovery and Reinvestment Act (ARRA) of 2009. The fast development process of our system, its scalability to large application domains, and its high extraction effectiveness will help the transparency of management decisions by extracting and mining relevant information

Towards a Context-Aware Knowledge Model for Smart Service Systems

The advancement of the Internet of things, big data, and mobile computing leads to the need for smart services that enable the context awareness and the adaptability to their changing contexts. Today, designing a smart service system is a complex task due to the lack of an adequate model support in awareness and pervasive environment. In this paper, we present a context-aware knowledge model for smart service systems that organizes the domain and context-aware knowledge into knowledge components based on the three levels of services: Services, Service system and Network of service systems. The context-aware knowledge model for smart service systems integrates all the information and knowledge related to smart services, knowledge components and context awareness that can play a key role for any framework, infrastructure, or applications deploying smart services. To demonstrate the approach, a case study about a chatbot as a smart service for customer support is presented

Competences, skills and tasks in today's jobs for linguists: Evidence from a corpus of job advertisements. UPSKILLS Intellectual output 1.3

The corpus-based analysis of job advertisements is part of the UPSKILLS needs analysis. Its objective is twofold. First, it aims to provide an overview of the knowledge, skills and competences mentioned in job posts targeting graduates in language-related degrees or professionals with expertise in this area, as well as of the typical tasks and responsibilities associated with these positions. Second, it aims to provide an initial list of companies at the crossroads between the language sector and the digital sector, which can be involved as stakeholders for the dissemination of UPSKILLS results

Quality Assessment Methods for Textual Conversational Interfaces: A Multivocal Literature Review

The evaluation and assessment of conversational interfaces is a complex task since such software products are challenging to validate through traditional testing approaches. We conducted a systematic Multivocal Literature Review (MLR), on five different literature sources, to provide a view on quality attributes, evaluation frameworks, and evaluation datasets proposed to provide aid to the researchers and practitioners of the field. We came up with a final pool of 118 contributions, including grey (35) and white literature (83). We categorized 123 different quality attributes and metrics under ten different categories and four macro-categories: Relational, Conversational, User-Centered and Quantitative attributes. While Relational and Conversational attributes are most commonly explored by the scientific literature, we testified a predominance of User-Centered Attributes in industrial literature. We also identified five different academic frameworks/tools to automatically compute sets of metrics, and 28 datasets (subdivided into seven different categories based on the type of data contained) that can produce conversations for the evaluation of conversational interfaces. Our analysis of literature highlights that a high number of qualitative and quantitative attributes are available in the literature to evaluate the performance of conversational interfaces. Our categorization can serve as a valid entry point for researchers and practitioners to select the proper functional and non-functional aspects to be evaluated for their products

An exploration of the relatedness problem between arguments: combining the generative lexicon with inference

International audienceGiven a controversial issue, argument mining from natural language texts is extremely challenging: domain knowledge is often required together with appropriate forms of inferences. This contribution explores the use of the Generative Lexicon viewed as both a lexicon and a domain knowledge representation

Measuring open innovation practices through topic modelling: Revisiting their impact on firm financial performance

Despite the popularity of open innovation in recent years, studies examining the impact of open innovation upon firm performance have shown mixed results. Previous empirical work on this topic is often based on surveys or archival sources, usually done either in isolation or in aggregate through employing proxy measures. In contrast, we employ an unsupervised learning technique (i.e., topic modelling) utilizing natural language processing to extract information on companies’ open innovation practices, creating an initial keyword basket for future development. We then revisit the relationship between open innovation practices and financial performance of firms. The results show that a firm’s overall openness level is associated with improved financial performance. More granular practices developed from our approach, however, show variations. The inverted U-shaped relationships are observed in specific open innovation practices but not in all, partly supporting the existence of the openness paradox from prior literature. The complementarity between internal R&D and individual open innovation practices also varies by practice. Further, the influence of these open innovation practices also varies by sector. Our findings prompt us to conclude that open innovation’s impact on financial performance is nuanced, and that there is no uniform set of best practices to practice open innovation effectively

Automatically Classifying User Engagement for Dynamic Multi-party Human–Robot Interaction

© 2017, The Author(s). A robot agent designed to engage in real-world human–robot joint action must be able to understand the social states of the human users it interacts with in order to behave appropriately. In particular, in a dynamic public space, a crucial task for the robot is to determine the needs and intentions of all of the people in the scene, so that it only interacts with people who intend to interact with it. We address the task of estimating the engagement state of customers for a robot bartender based on the data from audiovisual sensors. We begin with an offline experiment using hidden Markov models, confirming that the sensor data contains the information necessary to estimate user state. We then present two strategies for online state estimation: a rule-based classifier based on observed human behaviour in real bars, and a set of supervised classifiers trained on a labelled corpus. These strategies are compared in offline cross-validation, in an online user study, and through validation against a separate test corpus. These studies show that while the trained classifiers are best in a cross-validation setting, the rule-based classifier performs best with novel data; however, all classifiers also change their estimate too frequently for practical use. To address this issue, we present a final classifier based on Conditional Random Fields: this model has comparable performance on the test data, with increased stability. In summary, though, the rule-based classifier shows competitive performance with the trained classifiers, suggesting that for this task, such a simple model could actually be a preferred option, providing useful online performance while avoiding the implementation and data-scarcity issues involved in using machine learning for this task