Methodological guidelines for reusing general ontologies

Currently, there is a great deal of well-founded explicit knowledge formalizing general notions, such as time concepts and the part_of relation. Yet, it is often the case that instead of reusing ontologies that implement such notions (the so-called general ontologies), engineers create procedural programs that implicitly implement this knowledge. They do not save time and code by reusing explicit knowledge, and devote effort to solve problems that other people have already adequately solved. Consequently, we have developed a methodology that helps engineers to: (a) identify the type of general ontology to be reused; (b) find out which axioms and definitions should be reused; (c) make a decision, using formal concept analysis, on what general ontology is going to be reused; and (d) adapt and integrate the selected general ontology in the domain ontology to be developed. To illustrate our approach we have employed use-cases. For each use case, we provide a set of heuristics with examples. Each of these heuristics has been tested in either OWL or Prolog. Our methodology has been applied to develop a pharmaceutical product ontology. Additionally, we have carried out a controlled experiment with graduated students doing a MCs in Artificial Intelligence. This experiment has yielded some interesting findings concerning what kind of features the future extensions of the methodology should have

Trustworthiness in Mobile Cyber Physical Systems

Computing and communication capabilities are increasingly embedded in diverse objects and structures in the physical environment. They will link the ‘cyberworld’ of computing and communications with the physical world. These applications are called cyber physical systems (CPS). Obviously, the increased involvement of real-world entities leads to a greater demand for trustworthy systems. Hence, we use "system trustworthiness" here, which can guarantee continuous service in the presence of internal errors or external attacks. Mobile CPS (MCPS) is a prominent subcategory of CPS in which the physical component has no permanent location. Mobile Internet devices already provide ubiquitous platforms for building novel MCPS applications. The objective of this Special Issue is to contribute to research in modern/future trustworthy MCPS, including design, modeling, simulation, dependability, and so on. It is imperative to address the issues which are critical to their mobility, report significant advances in the underlying science, and discuss the challenges of development and implementation in various applications of MCPS

Soundtrack recommendation for images

The drastic increase in production of multimedia content has emphasized the research concerning its organization and retrieval. In this thesis, we address the problem of music retrieval when a set of images is given as input query, i.e., the problem of soundtrack recommendation for images. The task at hand is to recommend appropriate music to be played during the presentation of a given set of query images. To tackle this problem, we formulate a hypothesis that the knowledge appropriate for the task is contained in publicly available contemporary movies. Our approach, Picasso, employs similarity search techniques inside the image and music domains, harvesting movies to form a link between the domains. To achieve a fair and unbiased comparison between different soundtrack recommendation approaches, we proposed an evaluation benchmark. The evaluation results are reported for Picasso and the baseline approach, using the proposed benchmark. We further address two efficiency aspects that arise from the Picasso approach. First, we investigate the problem of processing top-K queries with set-defined selections and propose an index structure that aims at minimizing the query answering latency. Second, we address the problem of similarity search in high-dimensional spaces and propose two enhancements to the Locality Sensitive Hashing (LSH) scheme. We also investigate the prospects of a distributed similarity search algorithm based on LSH using the MapReduce framework. Finally, we give an overview of the PicasSound|a smartphone application based on the Picasso approach.Der drastische Anstieg von verfügbaren Multimedia-Inhalten hat die Bedeutung der Forschung über deren Organisation sowie Suche innerhalb der Daten hervorgehoben. In dieser Doktorarbeit betrachten wir das Problem der Suche nach geeigneten Musikstücken als Hintergrundmusik für Diashows. Wir formulieren die Hypothese, dass die für das Problem erforderlichen Kenntnisse in öffentlich zugänglichen, zeitgenössischen Filmen enthalten sind. Unser Ansatz, Picasso, verwendet Techniken aus dem Bereich der Ähnlichkeitssuche innerhalb von Bild- und Musik-Domains, um basierend auf Filmszenen eine Verbindung zwischen beliebigen Bildern und Musikstücken zu lernen. Um einen fairen und unvoreingenommenen Vergleich zwischen verschiedenen Ansätzen zur Musikempfehlung zu erreichen, schlagen wir einen Bewertungs-Benchmark vor. Die Ergebnisse der Auswertung werden, anhand des vorgeschlagenen Benchmarks, für Picasso und einen weiteren, auf Emotionen basierenden Ansatz, vorgestellt. Zusätzlich behandeln wir zwei Effizienzaspekte, die sich aus dem Picasso Ansatz ergeben. (i) Wir untersuchen das Problem der Ausführung von top-K Anfragen, bei denen die Ergebnismenge ad-hoc auf eine kleine Teilmenge des gesamten Indexes eingeschränkt wird. (ii) Wir behandeln das Problem der Ähnlichkeitssuche in hochdimensionalen Räumen und schlagen zwei Erweiterungen des Lokalitätssensitiven Hashing (LSH) Schemas vor. Zusätzlich untersuchen wir die Erfolgsaussichten eines verteilten Algorithmus für die Ähnlichkeitssuche, der auf LSH unter Verwendung des MapReduce Frameworks basiert. Neben den vorgenannten wissenschaftlichen Ergebnissen beschreiben wir ferner das Design und die Implementierung von PicassSound, einer auf Picasso basierenden Smartphone-Anwendung

Grafovi preferencije i njihova primjena

Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest

Grafovi preferencije i njihova primjena

Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest

Grafovi preferencije i njihova primjena

Usporedba objekata (alternativa) u parovima često se koristi u kontekstu odlučivanja. Metoda potencijala koristi (težinski) graf preferencije kao osnovnu strukturu generiranu takvim usporedbama. Iz toga grafa, rješavanjem sustava jednadžbi koji uključuje njegovu Laplaceovu matricu, dobiva se funkcija vrijednosti na skupu alternativa koju nazivamo potencijalom. U višekriterijskom ili grupnom odlučivanju (npr. izbornim procedurama), svaki kriterij ili sudionik može se predstaviti grafom preferencije. Multigraf dobiven spajanjem tih grafova koristi se za agregaciju preferencija i generira grupni potencijal. Moguće je postaviti proizvoljne težine da bismo podesili utjecaj pojedinog kriterija ili sudionika na grupni potencijal. Agregaciju grafova preferencije primjenjujemo na izborne procedure. Različiti oblici glasačkih listića generiraju grafove preferencije: tako dobivamo univerzalnu izbornu proceduru koja ne ovisi o obliku glasačkog listića. Još jedna primjena je klasterska analiza skupine na temelju preferencija njezinih članova, gdje valja provesti hijerarhijsku ili particijsku klasterizaciju više grafova preferencije. U tom kontekstu, agregacijski multigraf koristimo za definiranje središta klastera ili udaljenosti dvaju klastera. Opisanu izbornu proceduru, kao i klastersku analizu, za ilustraciju smo primijenili na glasačke preferencije zemalja s Eurosong natjecanja.Pairwise comparison of various objects (alternatives) is common in many procedures related to decision making. Potential Method (PM) uses a (weighted) preference graph as the basic structure generated by such comparisons. This graph implies a value function (called potential) on the set of alternatives. The potential is calculated as a solution of a system of equations involving the Laplacian matrix of the preference graph. In multiple-criteria decision analysis or group decision making (i.e., voting systems), each criterion or decision maker is represented by a preference graph. A multigraph obtained by joining these graphs is used for preference aggregation, generating the group potential. We can set arbitrary weights to adjust the influence of each criterion or decision maker on the group potential. Aggregation of preference graphs is applied to voting systems. Many different forms of voting ballots can generate preference graphs, which gives us a universal (ballot-independent) voting system. Another application is a cluster analysis of the group based on the members’ preferences, where a hierarchical or partitional clustering of their (multiple) preference graphs should be performed. In this context, the multigraph-based aggregation is used to define the center of the cluster or the distance between two clusters. As an illustration, we have applied the described voting system, as well as the cluster analysis, to the voting data from the Eurovision Song Contest

Data Science and Knowledge Discovery

Data Science (DS) is gaining significant importance in the decision process due to a mix of various areas, including Computer Science, Machine Learning, Math and Statistics, domain/business knowledge, software development, and traditional research. In the business field, DS's application allows using scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data to support the decision process. After collecting the data, it is crucial to discover the knowledge. In this step, Knowledge Discovery (KD) tasks are used to create knowledge from structured and unstructured sources (e.g., text, data, and images). The output needs to be in a readable and interpretable format. It must represent knowledge in a manner that facilitates inferencing. KD is applied in several areas, such as education, health, accounting, energy, and public administration. This book includes fourteen excellent articles which discuss this trending topic and present innovative solutions to show the importance of Data Science and Knowledge Discovery to researchers, managers, industry, society, and other communities. The chapters address several topics like Data mining, Deep Learning, Data Visualization and Analytics, Semantic data, Geospatial and Spatio-Temporal Data, Data Augmentation and Text Mining

Un enfoque de priorización de requerimientos a partir de la segmentación de las preferencias de los stakeholders

La presente Investigación tiene como propósito general diseñar un modelo de priorización de requerimientos de software a partir de la segmentación de preferencias de los stakeholders. Para ello, se propone fusionar una serie de enfoques teóricos de la priorización de requerimientos, del tipo: Negociación-Cognitivo para luego integrarlos metodológicamente con algunos elementos propios de la Inteligencia Computacional (SOM de Kohonen), el Proceso de Jerarquía Analítica (AHP) y métricas de la Psicología Cognitiva (DF, VTV y RE). El Modelo propuesto, pretende establecer un Esquema General de Prioridad Implementable de Requerimientos de Usuarios (EGPIRU) que sea consensuado y validado por el total de stakeholders del caso experimental “Subsistema de Flujo de Trabajo” del Centro de Investigación en Procesos Básicos, Metodología y Educación (CIMEPB) de la Facultad de Psicología de la Universidad Nacional de Mar del Plata.Facultad de Informátic

Agnostic content ontology design patterns for a multi-domain ontology

This research project aims to solve the semantic heterogeneity problem. Semantic heterogeneity mimics cancer in that semantic heterogeneity unnecessarily consumes resources from its host, the enterprise, and may even affect lives. A number of authors report that semantic heterogeneity may cost a significant portion of an enterprise’s IT budget. Also, semantic heterogeneity hinders pharmaceutical and medical research by consuming valuable research funds. The RA-EKI architecture model comprises a multi-domain ontology, a cross-industry agnostic construct composed of rich axioms notably for data integration. A multi-domain ontology composed of axiomatized agnostic data model patterns would drive a cognitive data integration application system usable in any industry sector. This project’s objective is to elicit agnostic data model patterns here considered as content ontology design patterns. The first research question of this project pertains to the existence of agnostic patterns and their capacity to solve the semantic heterogeneity problem. Due to the theory-building role of this project, a qualitative research approach constitutes the appropriate manner to conduct its research. Contrary to theory testing quantitative methods that rely on well-established validation techniques to determine the reliability of the outcome of a given study, theorybuilding qualitative methods do not possess standardized techniques to ascertain the reliability of a study. The second research question inquires on a dual method theory-building approach that may demonstrate trustworthiness. The first method, a qualitative Systematic Literature Review (SLR) approach induces the sought knowledge from 69 retained publications using a practical screen. The second method, a phenomenological research protocol elicits the agnostic concepts from semi-structured interviews involving 22 senior practitioners with 21 years in average of experience in conceptualization. The SLR retains a set of 89 agnostic concepts from 2009 through 2017. The phenomenological study in turn retains 83 agnostic concepts. During the synthesis stage for both studies, data saturation was calculated for each of the retained concepts at the point where the concepts have been selected for a second time. The quantification of data saturation constitutes an element of the trustworthiness’s transferability criterion. It can be argued that this effort of establishing the trustworthiness, i.e. credibility, dependability, confirmability and transferability can be construed as extensive and this research track as promising. Data saturation for both studies has still not been reached. The assessment performed in the course of the establishment of trustworthiness of this project’s dual method qualitative research approach yields very interesting findings. Such findings include two sets of agnostic data model patterns obtained from research protocols using radically different data sources i.e. publications vs. experienced practitioners but with striking similarities. Further work is required using exactly the same protocols for each of the methods, expand the year range for the SLR and to recruit new co-researchers for the phenomenological protocol. This work will continue until these protocols do not elicit new theory material. At this point, new protocols for both methods will be designed and executed with the intent to measure theoretical saturation. For both methods, this entails in formulating new research questions that may, for example, focus on agnostic themes such as finance, infrastructure, relationships, classifications, etc. For this exploration project, the road ahead involves the design of new questionnaires for semi-structured interviews. This project will need to engage in new knowledge elicitation techniques such as focus groups. The project will definitely conduct other qualitative research methods such as research action for eliciting new knowledge and know-how from actual development and operation of an ontology-based cognitive application. Finally, a mixed methods qualitative-quantitative approach would prepare the transition toward theory testing method using hypothetico-deductive techniques