IMEXT: a method and system to extract geolocated images from Tweets - Analysis of a case study

open5noopenFrancalanci, Chiara; Guglielmino, Paolo; Montalcini, Matteo; Scalia, Gabriele; Pernici, BarbaraFrancalanci, Chiara; Guglielmino, Paolo; Montalcini, Matteo; Scalia, Gabriele; Pernici, Barbar

Deliverable D5.6 Final LinkedTV End-to-End Platform

This Deliverable describes the final LinkedTV End-to-End Platform, which integrates a whole workflow from video ingestion over video analysis, annotated media fragment generation, content enrichment to personalized playout by a dedicated media player

Smart information retrieval: domain knowledge centric optimization approach

In the age of Internet of Things (IoT), online data has witnessed significant growth in terms of volume and diversity, and research into information retrieval has become one of the important research themes in the Internet oriented data science research. In information retrieval, machine-learning techniques have been widely adopted to automate the challenging process of relation extraction from text data, which is critical to the accuracy and efficiency of information retrieval-based applications including recommender systems and sentiment analysis. In this context, this paper introduces a novel, domain knowledge centric methodology aimed at improving the accuracy of using machine-learning methods for relation classification, and then utilise Genetic Algorithms (GAs) to optimise the feature selection for the learning algorithms. The proposed methodology makes significant contribution to the processes of domain knowledge-based relation extraction including interrogating Linked Open Datasets to generate the relation classification training-data, addressing the imbalanced classification in the training datasets, determining the probability threshold of the best learning algorithm, and establishing the optimum parameters for the genetic algorithm utilised in feature selection. The experimental evaluation of the proposed methodology reveals that the adopted machine-learning algorithms exhibit higher precision and recall in relation extraction in the reduced feature space optimised by the implementation. The considered machine learning includes Support Vector Machine, Perceptron Algorithm Uneven Margin and K-Nearest Neighbours. The outcome is verified by comparing against the Random Mutation Hill-Climbing optimisation algorithm using Wilcoxon signed-rank statistical analysis

Knowledge Patterns for the Web: extraction, tranformation and reuse

This thesis aims at investigating methods and software architectures for discovering what are the typical and frequently occurring structures used for organizing knowledge in the Web. We identify these structures as Knowledge Patterns (KPs). KP discovery needs to address two main research problems: the heterogeneity of sources, formats and semantics in the Web (i.e., the knowledge soup problem) and the difficulty to draw relevant boundary around data that allows to capture the meaningful knowledge with respect to a certain context (i.e., the knowledge boundary problem). Hence, we introduce two methods that provide different solutions to these two problems by tackling KP discovery from two different perspectives: (i) the transformation of KP-like artifacts to KPs formalized as OWL2 ontologies; (ii) the bottom-up extraction of KPs by analyzing how data are organized in Linked Data. The two methods address the knowledge soup and boundary problems in different ways. The first method provides a solution to the two aforementioned problems that is based on a purely syntactic transformation step of the original source to RDF followed by a refactoring step whose aim is to add semantics to RDF by select meaningful RDF triples. The second method allows to draw boundaries around RDF in Linked Data by analyzing type paths. A type path is a possible route through an RDF that takes into account the types associated to the nodes of a path. Then we present K~ore, a software architecture conceived to be the basis for developing KP discovery systems and designed according to two software architectural styles, i.e, the Component-based and REST. Finally we provide an example of reuse of KP based on Aemoo, an exploratory search tool which exploits KPs for performing entity summarization

Resource optimization of edge servers dealing with priority-based workloads by utilizing service level objective-aware virtual rebalancing

IoT enables profitable communication between sensor/actuator devices and the cloud. Slow network causing Edge data to lack Cloud analytics hinders real-time analytics adoption. VRebalance solves priority-based workload performance for stream processing at the Edge. BO is used in VRebalance to prioritize workloads and find optimal resource configurations for efficient resource management. Apache Storm platform was used with RIoTBench IoT benchmark tool for real-time stream processing. Tools were used to evaluate VRebalance. Study shows VRebalance is more effective than traditional methods, meeting SLO targets despite system changes. VRebalance decreased SLO violation rates by almost 30% for static priority-based workloads and 52.2% for dynamic priority-based workloads compared to hill climbing algorithm. Using VRebalance decreased SLO violations by 66.1% compared to Apache Storm\u27s default allocation

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Cross-Platform Text Mining and Natural Language Processing Interoperability - Proceedings of the LREC2016 conference

No abstract available

Improving Schema Mapping by Exploiting Domain Knowledge

This dissertation addresses the problem of semi-automatically creating schema mappings. The need for developing schema mappings is a pervasive problem in many integration scenarios. Although the problem is well-known and a large body of work exists in the area, the development of schema mappings is today largely performed manually in industrial integration scenarios. In this thesis an approach for the semi-automatic creation of high quality schema mappings is developed