Model the System from Adversary Viewpoint: Threats Identification and Modeling

Security attacks are hard to understand, often expressed with unfriendly and limited details, making it difficult for security experts and for security analysts to create intelligible security specifications. For instance, to explain Why (attack objective), What (i.e., system assets, goals, etc.), and How (attack method), adversary achieved his attack goals. We introduce in this paper a security attack meta-model for our SysML-Sec framework, developed to improve the threat identification and modeling through the explicit representation of security concerns with knowledge representation techniques. Our proposed meta-model enables the specification of these concerns through ontological concepts which define the semantics of the security artifacts and introduced using SysML-Sec diagrams. This meta-model also enables representing the relationships that tie several such concepts together. This representation is then used for reasoning about the knowledge introduced by system designers as well as security experts through the graphical environment of the SysML-Sec framework.Comment: In Proceedings AIDP 2014, arXiv:1410.322

Enabling Web-scale data integration in biomedicine through Linked Open Data

The biomedical data landscape is fragmented with several isolated, heterogeneous data and knowledge sources, which use varying formats, syntaxes, schemas, and entity notations, existing on the Web. Biomedical researchers face severe logistical and technical challenges to query, integrate, analyze, and visualize data from multiple diverse sources in the context of available biomedical knowledge. Semantic Web technologies and Linked Data principles may aid toward Web-scale semantic processing and data integration in biomedicine. The biomedical research community has been one of the earliest adopters of these technologies and principles to publish data and knowledge on the Web as linked graphs and ontologies, hence creating the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we provide our perspective on some opportunities proffered by the use of LSLOD to integrate biomedical data and knowledge in three domains: (1) pharmacology, (2) cancer research, and (3) infectious diseases. We will discuss some of the major challenges that hinder the wide-spread use and consumption of LSLOD by the biomedical research community. Finally, we provide a few technical solutions and insights that can address these challenges. Eventually, LSLOD can enable the development of scalable, intelligent infrastructures that support artificial intelligence methods for augmenting human intelligence to achieve better clinical outcomes for patients, to enhance the quality of biomedical research, and to improve our understanding of living systems

Anti-Pattern Specification and Correction Recommendations for Semantic Cloud Services

Given the economic and technological advantages \ they offer, cloud services are increasing being offered by \ several cloud providers. However, the lack of standardized \ descriptions of cloud services hinders their discovery. \ In an effort to standardize cloud service descriptions, \ several works propose to use ontologies. Nevertheless, \ the adoption of any of the proposed ontologies \ calls for an evaluation to show its efficiency in cloud \ service discovery. Indeed, the existing cloud providers \ describe, their similar offered services in different ways. \ Thus, various existing works aim at standardizing the \ representation of cloud computing services by proposing \ ontologies. However, since the existing proposals \ were not evaluated, they might be less adopted and considered. \ Indeed, the ontology evaluation has a direct impact \ on its understandability and reusability. In this paper, \ we propose an evaluation approach to validate our \ proposed Cloud Service Ontology (CSO), to guarantee \ an adequate cloud service discovery. To this end, this \ paper has a three-fold contribution. First, we specify a \ set of patterns and anti-patterns in order to evaluate our \ CSO. Second, we define an anti-pattern detection algorithm \ based on SPARQL queries which provides a set of \ correction recommendations to help ontologists revise \ their ontology. Finally, tests were conducted in relation \ to: (i) the algorithm efficiency and (ii) anti-pattern detection \ of design anomalies as well as taxonomic and \ domain errors within CSO

Reply to: Soils need to be considered when assessing the impacts of land-use change on carbon sequestration

Industrial Ecolog

Towards Semantic Detection of Smells in Cloud Infrastructure Code

Automated deployment and management of Cloud applications relies on descriptions of their deployment topologies, often referred to as Infrastructure Code. As the complexity of applications and their deployment models increases, developers inadvertently introduce software smells to such code specifications, for instance, violations of good coding practices, modular structure, and more. This paper presents a knowledge-driven approach enabling developers to identify the aforementioned smells in deployment descriptions. We detect smells with SPARQL-based rules over pattern-based OWL 2 knowledge graphs capturing deployment models. We show the feasibility of our approach with a prototype and three case studies.Comment: 5 pages, 6 figures. The 10 th International Conference on Web Intelligence, Mining and Semantics (WIMS 2020

Ontology Evolution Using Ontology Templates

Evolving ontologies by domain experts is difficult and typically cannot be performed without the assistance of an ontology engineer. This process takes long time and often recurrent modeling errors have to be resolved. This paper proposes a technique for creating controlled ontology evolution scenarios that ensure consistency of the possible ontology evolution and give guarrantees to the domain expert that his/her updates do not cause inconsistency. We introduce ontology templates that formalize the notion of controlled evolution and define ontology template consistency checking service together with a consistency checking algorithm. We prove correctness and demonstate the practical use of the techniques in two scenarios

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

Ontology-based information extraction from learning management systems

In this work we present a system for information extraction from Learning Management Systems. This system is ontology-based. It retrieves information according to the structure of the ontology to populate the ontology. We graphically present statistics about the ontology data. These statistics present latent knowledge which is difficult to see in the traditional Learning Management System. To answer questions about the ontology, a question answering system was developed using Natural Language Processing in the conversion of the natural language question into an ontology query language; Sumário: Extração de Informação de Sistemas de Gestão para Educação Usando Ontologias Neste dissertação apresentamos um sistema de extracção de informação de sistemas de gestão para educação (Learning Management Systems). Este sistema é baseado em ontologias e extrai informação de acordo com a estrutura da ontologia para a popular. Também permite apresentar graficamente algumas estatísticas sobre os dados da ontologia. Estas estatísticas revelam o conhecimento latente que é difícil de ver num sistema tradicional de gestão para a educação. Para poder responder a perguntas sobre os dados da ontologia, um sistema de resposta automática a perguntas em língua natural foi desenvolvido usando Processamento de Língua Natural para converter as perguntas para linguagem de interrogação de ontologias