International Journal of Semantic Computing

Statistical data is one of the most important sources of information, relevant for large numbers of stakeholders in the governmental, scientific and business domains alike. In this article, we overview how statistical data can be managed on the Web. With OLAP2DataCube and CSV2DataCube we present two complementary approaches on how to extract and publish statistical data. We also discuss the linking, repair and the visualization of statistical data. As a comprehensive use case, we report on the extraction and publishing on the Web of statistical data describing 10 years of life in Brazil.

The Internet of Things refers to extending the Internet to physical entities of interest (EoI) to humans (e.g., a table, a room or another human being) sensed as a set of properties that can be observed, measured, accessed or triggered by devices such as actuators, sensors or other smart components. In this vision, the IoT foresees novel types of applications dynamically finding the associations between devices and EoIs around a common feature of interest (e.g., temperature of a room) to provide meaningful information as well as rich services to users about the things they are interested in. Growing interest in providing sensors and actuators has led to billions of services or data offered through different platforms, some of them wrapped with semantic descriptions to realize aforementioned associations through accurate search processes. However, due to the ubiquitous aspect of the IoT and the potential mobility of the devices that enable it, a centralized approach does not allow designing scalable processes to efficiently search and manage these associations or the devices and EoIs that compose them. As location seems to be an important parameter when searching the IoT, we believe that designing a framework composed of geographically distributed nodes with local reasoning capabilities is a much more scalable approach to realize the IoT vision. We describe our approach of such a vision by creating a federated network composed of such nodes that declare their location based on a formal model. In this vision, each node is capable of processing semantic descriptions of devices or EoIs to share deduced associations with other peers that are selected based on their location nearness.

We define a generic model for finite audio or symbolic musical patterns that structurally encodes a rich and abstract synchronization mechanism. This is achieved by distinguishing for each pattern a realization window, describing what the pattern is, from a synchronization window, describing how the pattern can be used. The sequential composition of patterns is defined and studied. An algebra of musical patterns is introduced in a mathematically well-founded approach. We propose several high level operators that can be used either in audio processing or in musical analysis and composition. Practical uses and experiments conducted in both fields are described.

In the context of semantic knowledge bases, among the possible problems that may be tackled by means of data-driven inductive strategies, one can consider those that require the prediction of the unknown values of existing numeric features or the definition of new features to be derived from the data model. These problems can be cast as regression problems so that suitable solutions can be devised based on those found for multi-relational databases. In this paper, a new framework for the induction of logical regression trees is presented. Differently from the classic logical regression trees and the recent fork of the terminological classification trees, the novel terminological regression trees aim at predicting continuous values, while tests at the tree nodes are expressed with Description Logic concepts. They are intended for multiple uses with knowledge bases expressed in the standard ontology languages for the Semantic Web. A top-down method for growing such trees is proposed as well as algorithms for making predictions with the trees and deriving rules. The system that implements these methods is experimentally evaluated on ontologies selected from popular repositories.

Computers are central in processing scientific data. This data is typically expressed as numbers and strings. Appropriate annotation of "bare" data is required to allow people or machines to interpret it and to relate the data to real-world phenomena. In scientific practice however, annotations are often incomplete and ambiguous — let alone machine interpretable. This holds for reports and papers, but also for spreadsheets and databases. Moreover, in practice it is often unclear how the data has been created. This hampers interpretation, reproduction and reuse of results and thus leads to suboptimal science. In this paper we focus on annotation of scientific computations. For this purpose we propose the ontology OQR (Ontology of Quantitative Research). It includes a way to represent generic scientific methods and their implementation in software packages, invocation of these methods and handling of tabular datasets. This ontology promotes annotation by humans, but also allows automatic, semantic processing of numerical data. It allows scientists to understand the selected settings of computational methods and to automatically reproduce data generated by others. A prototype application demonstrates this can be done, illustrated by a case in food research. We evaluate this case with a number of researchers in the considered domain.

Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. This article discusses the problem of Learning Non-Taxonomic Relationships of ontologies and proposes a generic process for approaching it. Some techniques representing the state of the art of this field are discussed along with their advantages and limitations. Finally, a framework for Learning Non-Taxonomic Relationships being developed by the authors is briefly discussed. This framework intends to be a customizable solution to reach good effectiveness in the process of extraction of non-taxonomic relationships according to the characteristics of the corpus.