Data-driven science is heralded as a new paradigm in materials science. In this field, data is the new resource, and knowledge is extracted from materials datasets that are too big or complex for traditional human reasoning—typically with the intent to discover new or improved materials or materials phenomena. Multiple factors, including the open science movement, national funding, and progress in information technology, have fueled its development. Such related tools as materials databases, machine learning, and high-throughput methods are now established as parts of the materials research toolset. However, there are a variety of challenges that impede progress in data-driven materials science: data veracity, integration of experimental and computational data, data longevity, standardization, and the gap between industrial interests and academic efforts. In this perspective article, the historical development and current state of data-driven materials science, building from the early evolution of open science to the rapid expansion of materials data frastructures are discussed. Key successes and challenges so far are also reviewed, providing a perspective on the future development of the field.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/advs.201900808

Data-Driven Materials Science: Status, Challenges, and Perspectives

https://hal.archives-ouvertes.fr/hal-01123854/document

Semantic annotation for knowledge explicitation in a product lifecycle management context

A survey on knowledge representation in materials science and engineering

Reviews on web resources (e.g. courses, movies) become increasingly exploited in text analysis tasks (e.g. opinion detection, controversy detection). This paper investigates contradiction intensity in reviews exploiting different features such as variation of ratings and variation of polarities around specific entities (e.g. aspects, topics). Firstly, aspects are identified according to the distributions of the emotional terms in the vicinity of the most frequent nouns in the reviews collection. Secondly, the polarity of each review segment containing an aspect is estimated. Only resources containing these aspects with opposite polarities are considered. Finally, some features are evaluated, using feature selection algorithms, to determine their impact on the effectiveness of contradiction intensity detection. The selected features are used to learn some state-ofthe-art learning approaches. The experiments are conducted on the Massive Open Online Courses data set containing 2244 courses and their 73,873 reviews, collected from coursera.org. Results showed that variation of ratings, variation of polarities, and reviews quantity are the best predictors of contradiction intensity. Also, J48 was the most effective learning approach for this type of classification.

Conférence Nationale d'Intelligence Artificielle Année 2020

Nowadays, Public Sector Advertising PSA is conveyed as unidirectional top-down stream of messages that clearly separates the content producers governments usually from content consumers citizens. As social networks and Linked Open Government Data LOGD initiatives are moving forward e-Government towards connected government, PSA platforms need to embrace the modern paradigms of empowering citizens and communities to increasingly and actively participate in functioning of the society for their own benefits. In this position paper, firstly we present our findings related to the use of content from social networks as public ads and secondly, we propose an open and collaborative platform that supports semantically-enabled, participative PSA.

Integrating Social Media and Open Data in a Cloud-Based Platform for Public Sector Advertising

The objective of ONTORULE is to enable users, from business executives over business analysts to IT developers, to interact in their own way with the part of a business application that is relevant to them. This extended abstract describes the approach the ONTORULE project proposes to business rule application development, and it introduces the architecture and the semantic technologies that we develop for that purpose and that are validated and demonstrated in two pilot applications.

The ONTORULE project: Where ontology meets business rules

Over 30 years, evolutionary multi- and many-objective optimization (EMO/EMaO) algorithms have been extensively applied to find well-distributed Pareto-optimal (PO) solutions in a single run. However, in real-world problems, the PO front may not always be a single continuous hyper-surface, rather several irregularities may exist involving disjointed surfaces, holes within the surface, or patches of mixed-dimensional surfaces. When a set of trade-off solutions are obtained by EMO/EMaO algorithms, there may exist less dense or no solutions (we refer as 'gaps') in certain parts of the front. This can happen for at least two reasons: (i) gaps naturally exist in the PO front, or (ii) no natural gaps exists, but the chosen EMO/EMaO algorithm is not able to find any solution in the apparent gaps. To make a confident judgement, we propose a three-step procedure here. First, we suggest a computational procedure to identify gaps, if any, in the EMO/EMaO-obtained PO front. Second, we propose a computational method to identify well-distributed gap-points in the gap regions. Third, we apply a focused EMO/EMaO algorithm to search for possible representative trade-off points in the gaps. We then propose two metrics to qualitatively establish whether a gap truly exists in the obtained dataset, and if yes, whether the gap naturally exists on the true Pareto-set. Procedures are supported by results on two to five-objective test problems and on a five-objective scheduling problem from a steel-making industry.

Miguel Iglesias Escudero

Papers

The ONTORULE project: Where ontology meets business rules

Gap finding and validation in evolutionary multi- and many-objective optimization