International Journal of Semantic Computing

This paper presents a method to infer the quality of sprayers based on data collection of the drop spectra and their physical descriptors, which are used to generate a knowledge base to support decision-making in agriculture. The knowledge base is formed by collected experimental data, obtained in a controlled environment under specific operating conditions, and the semantics used in the spraying process to infer the quality in the application. The electro-hydraulic operating conditions of the sprayer system, which include speed and flow measurements, are used to define experimental tests, perform calibration of the spray booms and select the nozzle types. Using the Grubbs test and the quartile-quartile plot an exploratory analysis of the collected data was made in order to determine the data consistency, the deviation of atypical values, the independence between the data of each test, the repeatability and the normal representation of them. Therefore, integrating measurements to a knowledge base it was possible to improve the decision-making in relation to the quality of the spraying process defined in terms of a distribution function. Results shown that the use of advanced models and semantic interpretation improved the decision-making processes related to the quality of the agricultural sprayers.

This paper presents the design process of an embedded stereo vision system, which investigates the most relevant criteria for developing the hardware and software architectures for plant phenotyping. In other words, this paper is the result of a preliminary study in which the main motivation was the evaluation of the viability of a low-cost visual system for such field of knowledge. In addition, the implications of the adversities in an actual agricultural scenario under the system design are presented, since the system should not only meet the portability requirements but also the quality and precision for the measurements carried out by cameras. After the use of such method, the systems obtained may present a high chance of satisfying a set of constraints, and meeting their possibility to be used for machine vision applied in agricultural decision-making processes related to plant architecture and in situ recognition.

Ontologies are increasingly being developed on web-based repository hosting platforms such as GitHub. Accordingly, there is a demand for ontology editors which can be easily connected to the hosted repositories. TurtleEditor is a web-based RDF editor that provides this capability and supports the distributed development of ontologies on repository hosting platforms. It offers features such as syntax checking, syntax highlighting, and auto completion, along with a SPARQL endpoint to query the ontology. Furthermore, TurtleEditor integrates a visual editing view that allows for the graphical manipulation of the RDF graph and includes some basic clustering functionality. The text and graph views are constantly synchronized so that all changes to the ontology are immediately propagated and the views are updated accordingly. The results of a user study and performance tests show that TurtleEditor can indeed be effectively used to support the distributed development of ontologies on repository hosting platforms.

As distributed IoT applications become larger and more complex, the pure processing of raw sensor and actuation data streams becomes impractical. Instead, data streams must be fused into tangible facts and these pieces of information must be combined with a background knowledge to infer new pieces of knowledge. And since many IoT applications require almost real-time reactivity to stimulus of the environment, such information inference process has to be performed in a continuous, on-line manner. This paper proposes a new semantic model for data stream processing and real-time reasoning based on the concepts of Semantic Stream and Fact Stream, as a natural extension of Complex Event Processing (CEP) and RDF (graph-based knowledge model). The main advantages of our approach are that: (a) it considers time as a key relation between pieces of information; (b) the processing of streams can be implemented using CEP; (c) it is general enough to be applied to any Data Stream Management System (DSMS). We describe a scenario about patients flux monitoring in a hospital as an example of prospective application. Last, we present challenges and prospects on using machine learning and induction algorithms to learn abstractions and reasoning rules from a continuous data stream.

With the inception of the World Wide Web, the amount of data present on the Internet is tremendous. This makes the task of navigating through this enormous amount of data quite difficult for the user. As users struggle to navigate through this wealth of information, the need for the development of an automated system that can extract the required information becomes urgent. This paper presents a Question Answering system to ease the process of information retrieval. Question Answering systems have been around for quite some time and are a sub-field of information retrieval and natural language processing. The task of any Question Answering system is to seek an answer to a free form factual question. The difficulty of pinpointing and verifying the precise answer makes question answering more challenging than simple information retrieval done by search engines. The research objective of this paper is to develop a novel approach to Question Answering based on a composition of conventional approaches of Information Retrieval (IR) and Natural Language processing (NLP). The focus is on using a structured and annotated knowledge base instead of an unstructured one. The knowledge base used here is DBpedia and the final system is evaluated on the Text REtrieval Conference (TREC) 2004 questions dataset.

In the present work Latent Semantic Analysis of textual data was applied on texts related to courage, in order to compare and contrast results and evaluate the opportunity of integrating different data sets. To better understand the definition of courage in Italian context, 1199 participants were involved in the present study and was asked to answer to the following question “Courage is$\dots$”. The participants’ definitions of courage were analyzed with the Latent Semantic Analysis (LSA), and Latent Dirichlet Allocation (LDA), in order to study the fundamental concepts arising from the population. An analogous comparison with Twitter posts has been also carried out to analyze if the public opinion emerging from social media provides a challenging and rich context to explore computational models of natural language.

The synergy of Data Stream Management Systems and Semantic Web applications has steered towards a new paradigm known as Stream Reasoning. The Semantic Web standards for knowledge base modeling and querying, namely RDF, OWL and SPARQL, has extensively been used by the Stream Reasoning community. However, the Semantic Web rule languages, such as SWRL and RIF, have never been used in stream data applications. Instead, different non-Semantic Web rule systems have been approached. Since RIF is primarily intended for exchanging rules among systems, we focused on SWRL applications with stream data. This proves difficult following the SWRL’s open world semantics. To overcome SWRL’s expressivity issues we propose an infrastructure extension, which will enable SWRL reasoning with stream data. Namely, a query processing system, such as C-SPARQL, was layered under SWRL to support closed-world and time-aware reasoning. Moreover, OWLAPI constructs were utilized to enable non-monotonicity, while SPARQL constructs were used to enable negation as failure. Water quality monitoring was used as a validation domain of the proposed system.

Multimedia information has been extensively growing from a variety of sources such as cameras or video recorders. In order to select the useful multimedia objects, multimedia recommender system has been emerging as a tool to help users choose which multimedia objects might be interesting for them. However, given the complexity of multimedia objects, it is challenging to provide effective multimedia recommendations. In this paper, we therefore conduct a survey in both the multimedia information system and recommender system communities. We further focus on the works that span the two communities, especially the research on multimedia recommender systems. Based on our review, we propose a set of research challenges, which can be used to implicate the future research directions for multimedia recommender systems. For each research challenge, we have also provided the insights of how to perform the follow-up research.