Word Embeddings for Wine Recommender Systems Using Vocabularies of Experts and Consumers

This vision paper proposes an approach to use the most advanced word embeddings techniques to bridge the gap between the discourses of experts and non-experts and more specifically the terminologies used by the twocommunities. Word embeddings makes it possible to find equivalent terms between experts and non-experts, byapproach the similarity between words or by revealing hidden semantic relations. Thus, these controlledvocabularies with these new semantic enrichments are exploited in a hybrid recommendation system incorporating content-based ontology and keyword-based ontology to obtain relevant wines recommendations regardless of the level of expertise of the end user. The major aim is to find a non-expert vocabulary from semantic rules to enrich the knowledge of the ontology and improve the indexing of the items (i.e. wine) and the recommendation process

Survey on Quality of Observation within Sensor Web Systems

The Sensor Web vision refers to the addition of a middleware layer between sensors and applications. To bridge the gap between these two layers, Sensor Web systems must deal with heterogeneous sources, which produce heterogeneous observations of disparate quality. Managing such diversity at the application level can be complex and requires high levels of expertise from application developers. Moreover, as an information-centric system, any Sensor Web should provide support for Quality of Observation (QoO) requirements. In practice, however, only few Sensor Webs provide satisfying QoO support and are able to deliver high-quality observations to end consumers in a specific manner. This survey aims to study why and how observation quality should be addressed in Sensor Webs. It proposes three original contributions. First, it provides important insights into quality dimensions and proposes to use the QoO notion to deal with information quality within Sensor Webs. Second, it proposes a QoO-oriented review of 29 Sensor Web solutions developed between 2003 and 2016, as well as a custom taxonomy to characterise some of their features from a QoO perspective. Finally, it draws four major requirements required to build future adaptive and QoO-aware Sensor Web solutions

Deliverable D9.3 Final Project Report

This document comprises the final report of LinkedTV. It includes a publishable summary, a plan for use and dissemination of foreground and a report covering the wider societal implications of the project in the form of a questionnaire

Explaining and Predicting Abnormal Expenses at Large Scale using Knowledge Graph based Reasoning

International audienceGlobal business travel spend topped record-breaking $1.2 Trillion USD in 2015, and will reach$1.6 Trillion by 2020 according to the Global Business Travel Association, the world's premier business travel and meetings trade organization. Existing expenses systems are designed for reporting expenses, their type and amount over pre-defined views such as time period, service or employee group. However such systems do not aim at systematically detecting abnormal expenses, and more importantly explaining their causes. Therefore deriving any actionable insight for optimising spending and saving from their analysis is time-consuming, cumbersome and often impossible. Towards this challenge we present AIFS, a system designed for expenses business owner and auditors. Our system is manipulating and combining semantic web and machine learning technologies for (i) identifying, (ii) explaining and (iii) predicting abnormal expenses claim by employees of large organisations. Our prototype of semantics-aware employee expenses analytics and reasoning, experimented with 191, 346 unique Accenture employees in 2015, has demonstrated scalability and accuracy for the tasks of explaining and predicting abnormal expenses

Business Intelligence on Non-Conventional Data

The revolution in digital communications witnessed over the last decade had a significant impact on the world of Business Intelligence (BI). In the big data era, the amount and diversity of data that can be collected and analyzed for the decision-making process transcends the restricted and structured set of internal data that BI systems are conventionally limited to. This thesis investigates the unique challenges imposed by three specific categories of non-conventional data: social data, linked data and schemaless data. Social data comprises the user-generated contents published through websites and social media, which can provide a fresh and timely perception about people’s tastes and opinions. In Social BI (SBI), the analysis focuses on topics, meant as specific concepts of interest within the subject area. In this context, this thesis proposes meta-star, an alternative strategy to the traditional star-schema for modeling hierarchies of topics to enable OLAP analyses. The thesis also presents an architectural framework of a real SBI project and a cross-disciplinary benchmark for SBI. Linked data employ the Resource Description Framework (RDF) to provide a public network of interlinked, structured, cross-domain knowledge. In this context, this thesis proposes an interactive and collaborative approach to build aggregation hierarchies from linked data. Schemaless data refers to the storage of data in NoSQL databases that do not force a predefined schema, but let database instances embed their own local schemata. In this context, this thesis proposes an approach to determine the schema profile of a document-based database; the goal is to facilitate users in a schema-on-read analysis process by understanding the rules that drove the usage of the different schemata. A final and complementary contribution of this thesis is an innovative technique in the field of recommendation systems to overcome user disorientation in the analysis of a large and heterogeneous wealth of data

AN INVESTIGATION INTO ONTOLOGY BASED ENHANCEMENT OF SEARCH TECHNOLOGIES FOR E-GOVERNMENT

The field of e-government has always been a distinct area of standard testing for software research. To expound further, the e-government field presents a variety of principled elements, which makes the system more challenging and further encouraging than other electronic scenarios (e.g., e-business and e-commerce). To a certain extent, selecting an appropriate Web modelling technique (e.g., ontology) illustrates the critical role of establishing a successful e-government system. Ontology has been used in e-government to describe and define services provided to citizens. In addition, ontology technology is expected to play a pivotal role in enabling semantic web services.\ud While creating the ontology for e-government remains a significant task concerning its planning and implementation, there are still a few investigations to deal with the ontology technology’s effect on the government’s capability to deliver its due services. Because of the noticeable failure in large parts of e-government projects, the current study stands out as an attempt to improve their performance in terms of service delivery, appropriateness of applying modern technologies, and finally, a research-driven application. Factors for conducting academic research will lead to refining knowledge-building related to design procedures and setting up the ontological framework within the e-government domain. This research aims to verify whether the ontology technology can be described and considered a reference technology for providing e-government services over the Internet. Consequently, the research presents a practical method established on experimental research that moves nearer to the fact of the true function of ontological procedure in the field of e-government. In this study, the educational system in Singapore is used to represent the domain of e-government in which research questions are raised and addressed; also, general impressions can be tested and originated. The research methodology utilised in the research has the advantage of being consistent in the case study. Inside the case study, both quantitative and qualitative methods are applied to collect data and gain access to an answer to the research question

An Ontology-Driven Sociomedical Web 3.0 Framework

Web 3.0, the web of social and semantic cooperation, calls for a methodological multidisciplinary architecture in order to reach its mainstream objectives. With the lack of such an architecture and the reliance of existing efforts on lightweight semantics and RDF graphs, this thesis proposes "Web3.OWL", an ontology-driven framework towards a Web 3.0 knowledge architecture. Meanwhile, the online social parenting data and their corresponding websites users known as "mommy bloggers" undergo one of the fastest online demographics growth, and the available literature reflects the very little attention this growth has so far been given and the various deficiencies the parenting domain suffers from; these deficiencies all fall under the umbrella of the scarcity of parenting sociomedical analysis and decision-support systems. The Web3.OWL framework puts forward an approach that relies on the Meta-Object Facility for Semantics standard (SMOF) for the management of its modeled OWL (Web Ontology Language) expressive domain ontologies on the one hand, and the coordination of its various underlined Web 3.0 prerequisite disciplines on the other. Setting off with a holistic portrayal of Web3.OWL’s components and workflow, the thesis progresses into a more analytic exploration of its main paradigms. Out of its different ontology-aware paradigms are notably highlighted both its methodology for expressiveness handling through modularization and projection techniques and algorithms, and its facilities for tagging inference, suggestion and processing. Web3.OWL, albeit generic by conception, proves its efficiency in solving the deficiencies and meeting the requirements of the sociomedical domain of interest. Its conceived ontology for parenting analysis and surveillance, baptised "ParOnt", strongly contributes to the backbone metamodel and the various constituents of this ontology-driven framework. Accordingly, as the workflow revolves around Description Logics principles, OWL 2 profiles along with standard and beyond-standard reasoning techniques, conducted experiments and competency questions are illustrated, thus establishing the required Web 3.0 outcomes. The empirical results of the diverse preliminary decision-support and recommendation services targeting parenting public awareness, orientation and education do ascertain, in conclusion, the value and potentials of the proposed conceptual framework