A semantic-based system for querying personal digital libraries

This is the author's accepted manuscript. The final publication is available at Springer via http://dx.doi.org/10.1007/978-3-540-28640-0_4. Copyright @ Springer 2004.The decreasing cost and the increasing availability of new technologies is enabling people to create their own digital libraries. One of the main topic in personal digital libraries is allowing people to select interesting information among all the different digital formats available today (pdf, html, tiff, etc.). Moreover the increasing availability of these on-line libraries, as well as the advent of the so called Semantic Web [1], is raising the demand for converting paper documents into digital, possibly semantically annotated, documents. These motivations drove us to design a new system which could enable the user to interact and query documents independently from the digital formats in which they are represented. In order to achieve this independence from the format we consider all the digital documents contained in a digital library as images. Our system tries to automatically detect the layout of the digital documents and recognize the geometric regions of interest. All the extracted information is then encoded with respect to a reference ontology, so that the user can query his digital library by typing free text or browsing the ontology

Semantically-Aware Retrieval of Oceanographic Phenomena Annotated on Satellite Images

Scientists in the marine domain process satellite images in order to extract information that can be used for monitoring, understanding, and forecasting of marine phenomena, such as turbidity, algal blooms and oil spills. The growing need for effective retrieval of related information has motivated the adoption of semantically aware strategies on satellite images with different spatiotemporal and spectral characteristics. A big issue of these approaches is the lack of coincidence between the information that can be extracted from the visual data and the interpretation that the same data have for a user in a given situation. In this work, we bridge this semantic gap by connecting the quantitative elements of the Earth Observation satellite images with the qualitative information, modelling this knowledge in a marine phenomena ontology and developing a question answering mechanism based on natural language that enables the retrieval of the most appropriate data for each user’s needs. The main objective of the presented methodology is to realize the content-based search of Earth Observation images related to the marine application domain on an application-specific basis that can answer queries such as “Find oil spills that occurred this year in the Adriatic Sea”

An ontology-based framework for the automated analysis and interpretation of comic books' images

International audienceSince the beginning of the twenty-first century, the cultural industry has been through a massive and historical mutation induced by the rise of digital technologies. The comic books industry keeps looking for the right solution and has not yet produced anything as convincing as the music or movie have. A lot of energy has been spent to transfer printed material to digital supports so far. The specificities of those supports are not always exploited at the best of their capabilities, while they could potentially be used to create new reading conventions. In spite of the needs induced by the large amount of data created since the beginning of the comics history, content indexing has been left behind. It is indeed quite a challenge to index such a composition of textual and visual information. While a growing number of researchers are working on comic books' image analysis from a low-level point of view, only a few are tackling the issue of representing the content at a high semantic level. We propose in this article a framework to handle the content of a comic book, to support the automatic extraction of its visual components and to formalize the semantic of the domain's codes. We tested our framework over two applications: 1) the unsupervised content discovery of comic books' images, 2) its capabilities to handle complex layouts and to produce a respectful browsing experience to the digital comics reader

Advances on the morphological classification of radio galaxiesreview: A review

Modern radio telescopes will generate, on a daily basis, data sets on the scale of exabytes for systems like the Square Kilometre Array (SKA). Massive data sets are a source of unknown and rare astrophysical phenomena that lead to discoveries. Nonetheless, this is only plausible with the exploitation of machine learning to complement human-aided and traditional statistical techniques. Recently, there has been a surge in scientific publications focusing on the use of machine/deep learning in radio astronomy, addressing challenges such as source extraction, morphological classification, and anomaly detection. This study provides a comprehensive and concise overview of the use of machine learning techniques for the morphological classification of radio galaxies. It summarizes the recent literature on this topic, highlighting the main challenges, achievements, state-of-the-art methods, and the future research directions in the field. The application of machine learning in radio astronomy has led to a new paradigm shift and a revolution in the automation of complex data processes. However, the optimal exploitation of machine/deep learning in radio astronomy, calls for continued collaborative efforts in the creation of high-resolution annotated data sets. This is especially true in the case of modern telescopes like MeerKAT and the LOw-Frequency ARray (LOFAR). Additionally, it is important to consider the potential benefits of utilizing multi-channel data cubes and algorithms that can leverage massive datasets without relying solely on annotated datasets for radio galaxy classification.<br/

A Machine Learning Based Analytical Framework for Semantic Annotation Requirements

The Semantic Web is an extension of the current web in which information is given well-defined meaning. The perspective of Semantic Web is to promote the quality and intelligence of the current web by changing its contents into machine understandable form. Therefore, semantic level information is one of the cornerstones of the Semantic Web. The process of adding semantic metadata to web resources is called Semantic Annotation. There are many obstacles against the Semantic Annotation, such as multilinguality, scalability, and issues which are related to diversity and inconsistency in content of different web pages. Due to the wide range of domains and the dynamic environments that the Semantic Annotation systems must be performed on, the problem of automating annotation process is one of the significant challenges in this domain. To overcome this problem, different machine learning approaches such as supervised learning, unsupervised learning and more recent ones like, semi-supervised learning and active learning have been utilized. In this paper we present an inclusive layered classification of Semantic Annotation challenges and discuss the most important issues in this field. Also, we review and analyze machine learning applications for solving semantic annotation problems. For this goal, the article tries to closely study and categorize related researches for better understanding and to reach a framework that can map machine learning techniques into the Semantic Annotation challenges and requirements