0000000000908739
AUTHOR
Nuno Silva
Semantic HMC for Big Data Analysis
International audience; Analyzing Big Data can help corporations to im-prove their efficiency. In this work we present a new vision to derive Value from Big Data using a Semantic Hierarchical Multi-label Classification called Semantic HMC based in a non-supervised Ontology learning process. We also proposea Semantic HMC process, using scalable Machine-Learning techniques and Rule-based reasoning.
Effect of population structure and migration when investigating genetic continuity using ancient DNA
AbstractRecent advances in sequencing techniques provide means to access direct genetic snapshots from the past with ancient DNA data (aDNA) from diverse periods of human prehistory. Comparing samples taken in the same region but at different time periods may indicate if there is continuity in the peopling history of that area or if a large genetic input, such as an immigration wave, has occurred. Here we propose a new modeling approach for investigating population continuity using aDNA, including two fundamental elements in human evolution that were absent from previous methods: population structure and migration. The method also considers the extensive temporal and geographic heterogeneit…
Analyse Sémantique du Big Data par Classification Hiérarchique Multi-Label
International audience
Adaptive Learning Process for the Evolution of Ontology-Described Classification Model in Big Data Context
International audience; One of the biggest challenges in Big Data is to exploit value from large volumes of variable and changing data. For this, one must focus on analyzing the data in these Big Data sources and classify the data items according to a domain model (e.g. an ontology). To automatically classify unstructured text documents according to an ontology, a hierarchical multi-label classification process called Semantic HMC was proposed. This process uses ontologies to describe the classification model. To prevent cold start and user overload, the classification process automatically learns the ontology-described classification model from a very large set of unstructured text documen…
Semantic HMC for Business Intelligence using Cross-Referencing
International audience
AN ONTOLOGY-BASED RECOMMENDER SYSTEM USING HIERARCHICAL MULTICLASSIFICATION FOR ECONOMICAL E-NEWS
International audience; This paper focuses on a recommender system of economic news articles. Its objectives are threefold: (i) automatically multi-classify new economic articles, (ii) recommend articles by comparing profiles of users and multi-classification of articles, and (iii) managing the vocabulary of the economic news domain to improve the system based on seamlessly intervention of documentalists. In this paper we focus on the automatic multi-classification of the articles, managed by inference process of ontologies, and the enrichment of the documentalist-oriented ontology which provides the necessary capabilities to the DL reasoner for automatic multi-classification.
The population genomics of archaeological transition in west Iberia: Investigation of ancient substructure using imputation and haplotype-based methods
We analyse new genomic data (0.05–2.95x) from 14 ancient individuals from Portugal distributed from the Middle Neolithic (4200–3500 BC) to the Middle Bronze Age (1740–1430 BC) and impute genomewide diploid genotypes in these together with published ancient Eurasians. While discontinuity is evident in the transition to agriculture across the region, sensitive haplotype-based analyses suggest a significant degree of local hunter-gatherer contribution to later Iberian Neolithic populations. A more subtle genetic influx is also apparent in the Bronze Age, detectable from analyses including haplotype sharing with both ancient and modern genomes, D-statistics and Y-chromosome lineages. However, t…