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In today's data landscape, data streams are well represented. This is mainly due to the rise of data-intensive domains such as the Internet of Things (IoT), Smart Industries, Pervasive Health, and Social Media. To extract meaningful insights from these streams, they should be processed in real time, while solving an integration problem as these streams need to be combined with more static data and their domain knowledge. Ontologies are ideal for modeling this domain knowledge and facilitate the integration of heterogeneous data within data-intensive domains such as the IoT. Expressive reasoning techniques, such as OWL2 DL reasoning, are needed to completely interpret the domain knowledge and for the extraction of meaningful decisions. Expressive reasoning techniques have mainly focused on static data environments, as it tends to become slow with growing datasets. There is thus a mismatch between expressive reasoning and the real-time requirements of data-intensive domains. In this paper, we take a first step towards bridging the gap between expressivity and efficiency while reasoning over high-velocity IoT data streams for the task of event enrichment. We present a structural caching technique that eliminates reoccurring reasoning steps by exploiting the characteristics of most IoT streams, i.e., streams typically produce events that are similar in structure and size. Our caching technique speeds up reasoning time up to thousands of times for fully fledged OWL2 DL reasoners and even tenths and hundreds of times for less expressive OWL2 RL and OWL2 EL reasoners.

OBO library ontologies include a wealth of inter-ontology semantic links, which require OWL reasoning to utilize fully. Merging and reasoning over a large suite of ontologies is resource intensive, and challenging for many users. Here we present Ubergraph, an RDF triplestore and public SPARQL query endpoint, which includes a novel approach to precomputing OWL inferences, stored in a readily traversable knowledge graph. Ubergraph’s “relation graphs” allow users to perform SPARQL queries which make use of the semantics of the included ontologies. Ubergraph currently includes 39 OBO library ontologies. We describe several use cases enabled by Ubergraph such as ontology browsing and entailment validation., Also supported by NIH-NCATS Biomedical Data Translator program, 3OT2TR003449-01S1.

Ontologies provide a powerful method for representing, reusing, and sharing domain knowledge. They are extensively used in a wide range of disciplines, including artificial intelligence, knowledge engineering, biomedical informatics, and many more. For several reasons, developing domain ontologies is a challenging task. One of these reasons is that it is a complicated and time-consuming process. Multiple ontology development methodologies have already been proposed. However, there is room for improvement in terms of covering more activities during development (such as enrichment) and enhancing others (such as conceptualization). In this research, an enhanced ontology development methodology (ON-ODM) is proposed. Ontology-driven conceptual modeling (ODCM) and natural language processing (NLP) serve as the foundation of the proposed methodology. ODCM is defined as the utilization of ontological ideas from various areas to build engineering artifacts that improve conceptual modeling. NLP refers to the scientific discipline that employs computer techniques to analyze human language. The proposed ON-ODM is applied to build a tourism ontology that will be beneficial for a variety of applications, including e-tourism. The produced ontology is evaluated based on competency questions (CQs) and quality metrics. It is verified that the ontology answers SPARQL queries covering all CQ groups specified by domain experts. Quality metrics are used to compare the produced ontology with four existing tourism ontologies. For instance, according to the metrics related to conciseness, the produced ontology received a first place ranking when compared to the others, whereas it received a second place ranking regarding understandability. These results show that utilizing ODCM and NLP could facilitate and improve the development process, respectively.

The correct functioning of Semantic Web applications requires that given RDF graphs adhere to an expected shape. This shape depends on the RDF graph and the application’s supported entailments of that graph. During validation, RDF graphs are assessed against sets of constraints, and found violations help refining the RDF graphs. However, existing validation approaches cannot always explain the root causes of violations (inhibiting refinement), and cannot fully match the entailments supported during validation with those supported by the application. These approaches cannot accurately validate RDF graphs, or combine multiple systems, deteriorating the validator’s performance. In this paper, we present an alternative validation approach using rule-based reasoning, capable of fully customizing the used inferencing steps. We compare to existing approaches, and present a formal ground and practical implementation “Validatrr”, based on N3Logic and the EYE reasoner. Our approach – supporting an equivalent number of constraint types compared to the state of the art – better explains the root cause of the violations due to the reasoner’s generated logical proof, and returns an accurate number of violations due to the customizable inferencing rule set. Performance evaluation shows that Validatrr is performant for smaller datasets, and scales linearly w.r.t. the RDF graph size. The detailed root cause explanations can guide future validation report description specifications, and the fine-grained level of configuration can be employed to support different constraint languages. This foundation allows further research into handling recursion, validating RDF graphs based on their generation description, and providing automatic refinement suggestions.

RDF and Property Graphs are data models that are being used to represent Knowledge Graphs. The definition of methods to transform RDF data into Property graph data is fundamental to allow interoperability among the systems using these models. Although both models are based on a graph structure, they have special features that complicate the definition of data transformation methods. This article presents an ontology-based approach to transform (automatically) property graphs into RDF graphs. The ontology, called PGO, defines a set of terms that allows describing the elements of a property graph. The algorithm corresponding to the transformation method is described, and some properties of the method are discussed (complexity, data preservation, and monotonicity). The results of an experimental evaluation are also presented.

In this paper we present an ontological approach to build a knowledge-base on climate change related facts. Our knowledge space, developed in Ontology Web Language (OWL), enables knowledge within the Semantic Web. It allows a dynamic classification and a semantic characterization of the different events that can be related to each other, as well as to external sources of information. The resulting ontology is structured as a timeline which aims to describe the climate change story from multiple perspectives, including scientific, social, political and technological ones. We have created an instance of the ontology as an attempt to tell the climate change story so far. Such a population is based on the collection of factual information and critical literature review, with a focus on relevant theories, happenings, social and political initiatives. In this paper we present an ontological approach to build a knowledge-base on climate change related facts. Our knowledge space, developed in Ontology Web Language (OWL), enables knowledge within the Semantic Web. It allows a dynamic classification and a semantic characterization of the different events that can be related to each other, as well as to external sources of information. The resulting ontology is structured as a timeline which aims to describe the climate change story from multiple perspectives, including scientific, social, political and technological ones. We have created an instance of the ontology as an attempt to tell the climate change story so far. Such a population is based on the collection of factual information and critical literature review, with a focus on relevant theories, happenings, social and political initiatives.

Background Population-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions. Results A unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task. Conclusions The unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.

Crowdsourcing plays an important role in modern IT landscape, enabling the use of human information processing abilities to solve problems that are still hard for machines. One of the specific (and most demanded) applications of crowdsourcing is collecting item annotations, i.e., describing the contents of complex items with a help of labels (tags). Input received from crowdsourcing participants is typically unreliable, therefore, to increase the quality of annotations, each item is typically processed by several participants and the obtained annotations have to be aggregated. The paper considers a special case of annotating, where a set of possible labels, as well as the set of relationships between the labeled items and the labels are defined by an OWL 2 ontology (OWL QL). Such semantic item annotations turn out to be very useful in organizing large collections of items and enabling semantic search in them. In order to increase annotations quality, the paper proposes two aggregation methods - Onto Voting and OntoSB, differing in that the first one is agnostic with respect to participants' reliability and the second one accounts for variations in reliability. Simulation experiments with ontology-based annotations of varying quality show that the proposed aggregation methods increase the quality of collected ontology-based item annotations.

Collaborative spectrum access requires wireless devices to perform spectrum-related tasks (such as sensing) on request from other nodes. Thus, while joining the network, they need to inform neighboring devices and/or the central coordinator of their capabilities. During the operational phase, nodes may request other permissions from the the controller, like the opportunity to transmit according to the current policies and spectrum availability. To achieve such coordinated behavior, all associated devices within the network need a language for describing radio capabilities, requests, scenarios, policies, and spectrum availability. In this paper, we present a thorough comparison of the use of two candidate languages—Web Ontology Language (OWL) and eXtensible Markup Language (XML)—for such purposes. Towards this goal, we propose an evaluation method for automating quantitative comparisons with metrics such as precision, recall, device registration, and the query response time. The requests are expressed in both SPARQL Protocol and RDF Query Language (SPARQL) and XML Query Language (XQuery), whereas the device capabilities are expressed in both OWL and XML. The evaluation results demonstrate the advantages of using OWL semantics to improve the quality of matching results over XML. We also discuss how the evaluation method can be applicable to other scenarios where knowledge, datasets, and queries require richer expressiveness and semantics.

In this paper, the relation between the FAIR (Findable, Accessible, Interoperable, Reusable) ontologies and accountability and transparency of ontology-based AI systems is analysed. Also, governance-related gaps in ontology quality evaluation metrics were identified by examining their relation with FAIR principles and FAcct (Fairness, Accountability, Transparency) governance aspects. A simple SKOS vocabulary, titled "Hospital Adverse Incidents Classification Scheme" (HAICS) has been used as a use case for this study. Theoretically, we found that there is a straight relation between FAIR principles and FAccT AI, which means that FAIR ontologies enhance transparency and accountability in ontology-based AI systems. We suggest that "FAIRness" should be assessed as one of the ontology quality evaluation aspects.

Numerous sources for disease definitions and data models currently exist, which include HPO, OMIM, SNOMED CT, ICD, PhenoDB, MedDRA, MedGen, ORDO, DO, GARD, etc; however, these sources partially overlap and sometimes conflict, making it difficult to know definitively how they relate to each other. This has resulted in a proliferation of mappings between disease entries in different resources; however mappings are problematic: collectively, they are expensive to create and maintain. Most importantly, the mappings lack completeness, accuracy, and precision; as a result, mapping calls are often inconsistent between resources. The UMLS provides intermediate concepts through which other resources can be mapped, but these mappings suffer from the same challenges: they are not guaranteed to be one-to-one, especially in areas with evolving disease concepts such as rare disease. In order to address the lack of a unified disease terminology that provides precise equivalences between disease concepts, we created Mondo, which provides a logic-based structure for unifying multiple disease resources. Mondo utilizes human curation assisted by a combination of logical, probabilistic and lexical methods to assist in curation and hone curation on difficult areas, together with fostering a broader community of disease resources committed to working together. &nbsp

1.AbstractTraditional cultural beliefs influence perceptions of animals, and in some cases can result in persecution of wildlife. Stigmas against species associated with witchcraft, for example, can act as a barrier to the uptake of more cost effective, sustainable, and environmentally sound practices such as reducing crop damage by controlling rodent agricultural pests by relying on indigenous predators rather than pesticides. One way of enhancing perceptions of wildlife to increase participation in such ecologically-based rodent management (EBRM) schemes, is the development of environmental education initiatives. Low intensity programmes are cost-effective and can produce positive attitudinal shifts, but their impact has not been assessed for species strongly associated with witchcraft. We set out to test whether a single presentation on the natural history of owls (order Strigiformes) could improve perceptions of these species, and increase willingness to participate in an EBRM scheme that involved the installation of owl boxes to increase owl populations and reduce rodent populations and crop damage in agricultural fields. We used a questionnaire survey to assess perceptions of owls at four schools in two villages in South Africa. Our initial survey sampled perceptions of respondents before listening to the presentation. A follow-up survey conducted three months later sampled the perceptions of respondents that had listened to the presentation as well as perceptions of a control group that did not listen to the presentation. We found that associations between owls and witchcraft was a common theme driving negative perceptions of owls. Respondents that watched the presentation had more positive perceptions of owls than respondents that had not watched the presentation, and they were more likely to be willing to put up owl boxes near their home. Despite this shift, negative perceptions of owls still dominated responses due to cultural associations with the occult. These findings indicate that even low-intensity programmes can be effective at enhancing perceptions of wildlife associated with witchcraft. We suggest that environmental education programmes featuring culturally taboo species should adopt a culturally sensitive and locally tailored approach, focus on the benefits these species provide, and may be more effective when delivered with greater intensity.

UML class diagrams are used in order to demonstrate the model information systems. Most of the former systems were previously framed by applying UML class diagrams. Besides, the current trend has a growing demand of reusing data from older information systems, which is very beneficial for the implementation of semantic knowledge. The upgradation and conversion of UML class diagrams into ontology aim to reuse older systems; therefore, cost reduction is essential. There have been many studies for transformation from UML class diagrams into ontology; however, they have not fully proposed the transformation rules. This paper will suggest the addition of rules for transforming UML class diagrams to ontologies, such as attribute transformations which have data types as classes, the structured attributes, the association with association class, the recursive association, the aggregation with a qualification.

The key task of the current stage of the education system development is the task of transition to new methods of organization and support of the educational process, allowing providing high-quality training of graduates with practical knowledge and skills in demand in the labor market. To solve this task is possible only with the use of innovative methods and tools for the creation of information-educational environment in the formation and processing of learning objects and software services distributed in the educational network and allowing the flexible implementation of the processes of educational and methodological support of educational activities. The aim of the research is to determine the possibilities of using the ontological approach to the construction of innovative automated education management systems, including the construction of individual learning trajectories. In this regard, the problem of modeling and algorithmization of the formation of educational programs as a chain of interrelated training modules and objects, taking into account the implementation of a competence-based model of learning in a distributed educational environment of the University, seems to be interesting. The modern education system is to be aimed primarily at adaptive generation of educational content, taking into account the needs of students and in accordance with constantly changing conditions and needs of the labor market. At present, research in this area mainly concerns issues such as the use of multimedia technologies and simulation programs, knowledge testing technologies based on the competency model, methods for measuring the degree of satisfaction with the quality of training, and management methods based on the portfolio of students. These issues and problems are considered with varying degrees of detail, for example, in [1, 2, 3, 4] and other works. The developed methodology for the formation of educational programs is based on the research results of these authors and is developing towards the use of ontologies that can integrate various concepts of professional and educational standards and create on their basis fundamentally new educational modules and objects. In our opinion, it corresponds to world trends in the development of science in this field of knowledge. It is propose d to use the methods and methodologies of knowledge engineering as engineering methods: ontological engineering methods used for semantic modeling of the information and educational environment, allowing to represent the subject area in the form of a set of interconnected ontologies; methods for constructing and visualizing ontologies; methodologies for building databases and knowledge bases and forming complex queries to databases and knowledge bases; methods for semantic analysis of metadata; graph theory. As a result of the analysis of the objects of the information and educational environment, the requirements for the meta-description of educational objects and services are identified, and a description of the concepts of basic ontologies of the informational and educational environment is presented. The article also discusses the possibilities of using modern software for the formation of an intellectual repository of ontologies. Thus, the proposed toolkit for generating educational programs, in our opinion, will allow us to formalize, simplify, and significantly accelerate the process offlexible development of materials necessary for organizing and supporting the educational process. Application of the described approach makes it possible to improve the quality of training of specialists due to the prompt creation of organizational, managerial and educational materials adapted to the requirements of the labor market, taking into account the peculiarities of forms and methods of training.

In recent years, there has been a growing interest in the application of foundational ontologies, i.e., formal ontological theories in the philosophical sense, to provide a theoretically sound foundation for improving the theory and practice of conceptual modeling and knowledge representation. This paper addresses one particular foundational theory of events termed UFO-B, which has been successfully employed as a reference model for addressing problems from complex media management, enterprise architecture, software engineering, and modeling of events in petroleum exploration. Despite its success, there is still no formalization of UFO-B in a decidable knowledge representation language that could support reasoning about complex events and event relations. We address this gap by proposing a number of alternative translations from UFO-B's original axiomatization (in first-order logic and in the Alloy formal language) to the description logic SROIQ, which is the formal underpinning of OWL 2 DL. Additionally, to support practical applications, we translated these SROIQ theories to OWL 2 DL TBoxes, which were validated by showing that all the intended models of UFO-B (the logical models of the UFO-B specification in Alloy) that we generated are consistent with these UFO-B TBoxes. In a sense, the specification in Alloy implements the specification in first-order logic, while the OWL 2 TBoxes implement the SROIQ specifications. Incidentally, the methodology that we designed for the translation from UFO-B's original axiomatization in FOL and Alloy to SROIQ came to be a key contribution of this work by providing us evidence of the inadequacy of DLs for the specification of comprehensive foundational ontologies.

In Video Surveillance age, the monitoring activity, especially from unmanned vehicles, needs some degree of autonomy in the scenario interpretation. Video Analysis tasks are crucial for the target tracking and recognition; anyway, it would be desirable if a further level of understanding could provide a comprehensive, high-level scene description, by reflecting that human cognitive capability of providing a concise scene description that comes from the analysis of involved objects relationships and actions. This paper presents a smart system to identify mobile scene objects, such as people, vehicles, automatically, by analyzing the videos acquired by drones in flight, along with the activities they carried out, so as to depict what it happens in the scene from a high-level perspective. The system uses Artificial Vision methods to detect and track the mobile objects and the area where they move, and Semantic Web technologies to provide a high-level description of the scenario. Spatio/temporal relations among the tracked objects as well as simple object activities (events) are described. By semantic reasoning, the system is able to connect the simple activities into more complex activities, that better reflect a human-like description of a scenario portion. Tests conducted on several videos, showing scenarios set in different environments, return convincing results which affirm the effectiveness of the proposed approach.

The industrial cyber-physical system enables collaboration between distributed nodes across industrial clouds and edge devices. Flexibility and interoperability could be enhanced significantly by introducing the service-oriented architecture to industrial edge devices. From the industrial edge computing perspective, software components shall be dynamically composed across heterogeneous edge devices to perform various functionalities. In this paper, a knowledge-driven Microservice-based architecture to enable plug-and-play software components is proposed for industrial edges. These software components can be dynamically configured based on the orchestration of microservices with the support of the knowledge base and the reasoning process. These semantically enhanced plug-and-play microservices could provide rapid online reconfiguration without any programming efforts. The use of the plug-and-play software components is demonstrated by an assembly line example.

This chapter introduces the different background material needed to understand the next three Chapters respectively presenting the Cyber-Physical Systems (CPS), Multi-Paradigm Modelling (MPM) and Multi-Paradigm Modelling for Cyber-Physical Systems (MPM4CPS) ontologies. It first introduces the approach and tools that have been used to define the ontologies. It then provides an overview of the ontological framework and a description of the shared ontology, which provides concepts defined in order to frame in a more general context some of the more specific notions of the other ontologies. Then, the chapter presents a brief introduction of the two CPS engineering environments employing MPM that were used to explore the domains covered by the ontologies and to guide readers through our exploration path.

Nowadays, companies and official bodies are using the data as a principal asset to take strategic decisions. The advances in big data processing, storage and analysis techniques have allowed to manage the continuous increase in the volume of data. This increase in the volume of data together with its high variability and the large number of sources lead to a constant growing of the complexity of the data management environment. Data governance is the key for simplifying that complexity: it is the element that controls the decision making and responsibilities for all the processes related to data management. This paper discusses an approach to data governance based on ontological reasoning to reduce data management complexity. The proposed data governance system is built over an autonomous system based on distributed components. It implements semantic techniques and automatic ontology-based reasoning. The different components use a Shared Knowledge Plane to interact. Its fundamental piece is an ontology that represents all the data management processes included in data governance. A prototype of such a system has been implemented and tested for Telefonica’s global video service. The results obtained show the feasibility of using this type of technology to reduce the complexity of managing big data environments.

Retroelement insertions (RIs) are low-homoplasy characters that are ideal data for addressing deep evolutionary radiations, where gene tree reconstruction errors can severely hinder phylogenetic inference with DNA and protein sequence data. Phylogenomic studies of Neoaves, a large clade of birds (>9000 species) that first diversified near the Cretaceous–Paleogene boundary, have yielded an array of robustly supported, contradictory relationships among deep lineages. Here, we reanalyzed a large RI matrix for birds using recently proposed quartet-based coalescent methods that enable inference of large species trees including branch lengths in coalescent units, clade-support, statistical tests for gene flow, and combined analysis with DNA-sequence-based gene trees. Genome-scale coalescent analyses revealed extremely short branches at the base of Neoaves, meager branch support, and limited congruence with previous work at the most challenging nodes. Despite widespread topological conflicts with DNA-sequence-based trees, combined analyses of RIs with thousands of gene trees show emergent support for multiple higher-level clades (Columbea, Passerea, Columbimorphae, Otidimorphae, Phaethoquornithes). RIs express asymmetrical support for deep relationships within the subclade Afroaves that hints at ancient gene flow involving the owl lineage (Strigiformes). Because DNA-sequence data are challenged by gene tree-reconstruction error, analysis of RIs represents one approach for improving gene tree-based methods when divergences are deep, internodes are short, terminal branches are long, and introgressive hybridization further confounds species–tree inference.

Many different digital representations of a building are produced over the course of its lifecycle. While these representations individually contain metadata required to support different stages of the building's lifecycle, they are largely not interoperable due to differences in structure, syntax and semantics. This impedes the development and deployment of data-driven applications providing fault detection and diagnosis, virtual metering or optimal control. [4] introduces a new platform for the continuous curation of a unified, Brick [1]-based metadata model that can be maintained throughout the building lifecycle. In this demonstration, we present a live proof-of-concept implementation of the platform with support for several metadata representations in the context of a simulated building lifecycle.

Many different digital representations of a building are produced over the course of its lifecycle. These representations contain the metadata required to support different stages of the building, from initial planning and design, to construction and commissioning, through operations, audits, retrofits and maintenance. However, because of differences in the semantics, structure and syntax of these representations, the metadata they contain is not interoperable. We present a novel method for leveraging these representations to create a unified, authoritative Brick metadata model for a building that can be continually maintained over the course of the building lifecycle. A simple synchronization protocol relays inferred Brick metadata from existing metadata sources such as gbXML, BuildingSync, Project Haystack and Modelica to a central integration server, which merges the metadata into a valid Brick model.

https://douroucouli.wordpress.com/2020/11/02/aligning-design-patterns-across-multiple-ontologies-in-the-life-sciences/ https://docs.google.com/presentation/d/1M6zxYQPwy3fgyQRstaFYCZ3uscTg4J5OwooYQkNCQ8E/edit#slide=id.p

The Holy Quran, due to it is full of many inspiring stories and multiple lessons that need to understand it requires additional attention when it comes to searching issues and information retrieval. Many works were carried out in the Holy Quran field, but some of these dealt with a part of the Quran or covered it in general, and some of them did not support semantic research techniques and the possibility of understanding the Quranic knowledge by the people and computers. As for others, techniques of data analysis, processing, and ontology were adopted, which led to directed these to linguistic aspects more than semantic. Another weakness in the previous works, they have adopted the method manually entering ontology, which is costly and time-consuming. In this paper, we constructed the ontology of Quranic stories. This ontology depended in its construction on the MappingMaster domain-specific language (MappingMaster DSL)technology, through which concepts and individuals can be created and linked automatically to the ontology from Excel sheets. The conceptual structure was built using the object role modeling (ORM) modeling language. SPARQL query language used to test and evaluate the propsed ontology by asking many competency questions and as a result, the ontology answered all these questions well.

In this paper we introduce Eddy, a new open-source tool for the graphical editing of OWL~2 ontologies. Eddy is specifically designed for creating ontologies in Graphol, a completely visual ontology language that is equivalent to OWL~2. Thus, in Eddy ontologies are easily drawn as diagrams, rather than written as sets of formulas, as commonly happens in popular ontology design and engineering environments. This makes Eddy particularly suited for usage by people who are more familiar with diagramatic languages for conceptual modeling rather than with typical ontology formalisms, as is often required in non-academic and industrial contexts. Eddy provides intuitive functionalities for specifying Graphol diagrams, guarantees their syntactic correctness, and allows for exporting them in standard OWL 2 syntax. A user evaluation study we conducted shows that Eddy is perceived as an easy and intuitive tool for ontology specification.

Advanced service robots are not, as of yet, widely adopted, partly due to the effectiveness of robots’ object recognition capabilities, the issue of object heterogeneity, a lack of knowledge sharing, and the difficulty of knowledge management. To encourage more widespread adoption of service robots, we propose an ontology-based framework for cooperative robot learning that takes steps toward solving these problems. We present a use case of the framework in which multiple service robots offload compute-intensive machine vision tasks to cloud infrastructure. The framework enables heterogeneous 3D object recognition with the use of ontologies. The main contribution of our proposal is that we use the Unified Robot Description Format (URDF) to represent robots, and we propose the use of a new Robotic Object Description (ROD) ontology to represent the world of objects known by the collective. We use the WordNet database to provide a common understanding of objects across various robotic applications. With this framework, we aim to give a widely distributed group of robots the ability to cooperatively learn to recognize a variety of 3D objects. Different robots and different robotic applications could share knowledge and benefit from the experience of others via our framework. The framework was validated and then evaluated using a proof-of-concept, including a Web application integrated with the ROD ontology and the WordNet API for semantic analysis. The evaluation demonstrates the feasibility of using an ontology-based framework and using the Ontology Web Language (OWL) to provide improved knowledge management while enabling cooperative learning between multiple robots.

Purpose With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels. Design/methodology/approach This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation. Findings Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper. Originality/value The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

Within an enterprise, various stakeholders create different conceptual models, such as process, data, and requirements models. These models are fundamentally based on similar underlying enterprise (domain) concepts, but they differ in focus, use different modelling languages, take different viewpoints, utilize different terminology, and are used to develop different enterprise artefacts; as such, they typically lack consistency and interoperability. This issue can be solved by enterprise-specific ontologies, which serve as a reference during the conceptual model creation. Using such a shared semantic repository makes conceptual models interoperable and facilitates model integration. The challenge to accomplish this is twofold: on the one hand, an up-to-date enterprise-specific ontology needs to be created and maintained, and on the other hand, different modellers also need to be supported in their use of the enterprise-specific ontology. In this article, we propose to tackle these challenges by means of a recommendation-based conceptual modelling and an ontology evolution framework, and we focus in particular on ontology-based modelling support. To this end, we present our framework for Business Process Modelling Notation (BPMN) as a conceptual modelling language, and focus on how modellers can be assisted during the modelling process and how this impacts the semantic quality of the resulting models. Subsequently, we present a first, large-scale explorative experiment involving 140 business students to evaluate the BPMN instantiation of our framework. The experiments show promising results with regard to incurred overhead, intention of use and model interoperability.

International audience; Objective: Ontologies are widely used in the biomedical domain. While many tools exist for the edition, alignment or evaluation of ontologies, few solutions have been proposed for ontology programming interface, i.e. for accessing and modifying an ontology within a programming language. Existing query languages (such as SPARQL) and APIs (such as OWLAPI) are not as easy-to-use as object programming languages are. Moreover, they provide few solutions to difficulties encountered with biomedical ontologies. Our objective was to design a tool for accessing easily the entities of an OWL ontology, with high-level constructs helping with biomedical ontologies. Methods: From our experience on medical ontologies, we identified two difficulties: (1) many entities are represented by classes (rather than individuals), but the existing tools do not permit manipulating classes as easily as individuals, (2) ontologies rely on the open-world assumption, whereas the medical reasoning must consider only evidence-based medical knowledge as true. We designed a Python module for ontology-oriented programming. It allows access to the entities of an OWL ontology as if they were objects in the programming language. We propose a simple high-level syntax for managing classes and the associated " role-filler " constraints. We also propose an algorithm for performing local closed world reasoning in simple situations. Results: We developed Owlready, a Python module for a high-level access to OWL ontologies. The paper describes the architecture and the syntax of the module version 2. It details how we integrated the OWL ontology model with the Python object model. The paper provides examples based on Gene Ontology (GO). We also demonstrate the interest of Owlready in a use case focused on the automatic comparison of the contraindications of several drugs. This use case illustrates the use of the specific syntax proposed for manipulating classes and for performing local closed world reasoning. Conclusion: Owlready has been successfully used in a medical research project. It has been published as Open-Source software and then used by many other researchers. Future developments will focus on the support of vagueness and additional non-monotonic reasoning feature, and automatic dialog box generation.

The amount of social information published in the Web has dramatically increased in the past years. FOAF, Dublin Core, and vCard are examples of popular vocabularies that are used to present computer understandable profiles of persons in social media. These profiles describe facts about a person such as his or her interests, and thereby we can easily find persons having the same interests. However, in reality we are rather interested in about the closeness of persons' interests than whether they have precisely the same interests. Such aspects are easily understandable for humans but they are not machine understandable without additional semantics. Therefore we have extended the vocabularies of social media by domain specific taxonomies. In addition, to increase the expression power of persons' profiles we have also attached weights to persons' interests. We have also consider a variety of ways for expressing and computing the closeness of weighted profiles. (c) 2017 The Authors. Published by Elsevier B.V.

Over the recent years, the vast variety of widely accessible cloud computing services along with the need to combine transportation services either from public or private providers, have led to the rise of the Mobility as a Service (MaaS) concept. The main feature of MaaS is that it gives users access to a set of heterogeneous transportation services from a single access point (i.e., an app). The ever-increasing adoption of MaaS by service providers introduces a variety of new business models and technologies that can successfully support the design and deployment of MaaS services.

For the last 15 years, Biodiversity Information Standards (TDWG) has recognized two competing standards for organism occurrence data, ABCD (Access to Biological Collections Data; Holetschek et al. 2012) and DarwinCore (Wieczorek et al. 2012). These two representations emerged from contrasting strategies for mobilizing information about organism occurrences (also commonly called species occurrence data). ABCD was capable of representing details of more kinds of information, but was necessarily more complicated. DarwinCore, on the other hand, was simpler but more limited in its ability to represent data of different kinds and formats. TDWG endorsed both standards because the different projects and communities that generated them remained dedicated to their different strategies and tool sets, and the Global Biodiversity Information Facility (GBIF) developed the ability to integrate data published in either standard. Since their inceptions, DarwinCore and ABCD have become more similar. DarwinCore has gotten more complicated through the addition of terms and has begun to assign terms to classes. ABCD is now expressed in RDF (Resource Description Framework), potentially enabling re-use of terms with alternative structures among classes. At the same time, methodologies for conceptual modeling and representing complex scientific data have continued to evolve. In particular, a suite of modeling and data representation methods related to linked data and the semantic web, i.e., RDF, SKOS (Simple Knowledge Organization System), and OWL (web Ontology Language), promise to make it easier for us to reconcile shared concepts among different representations or schemas. A mapping between ABCD 2.1 and DarwinCore has existed since before 2005.*1 ABCD 3.0 and DarwinCore are both now represented in RDF. In addition, the BioCollections Ontology (BCO) covers many of the shared concepts and is derived from the Basic Formal Ontology (BFO), an upper level ontology that has oriented many other biomedical ontologies. Reconciling ABCD and DarwinCore through alignment with BCO (in the OBO Foundry; Smith et al. 2007) would better connect TDWG standards to other domains in biology. We appreciate that many working scientists and data managers perceive ontologies as overly complicated. To mitigate the steep learning curve associated with ontologies, we expect to create simpler application profiles or schemas to guide and serve narrower communities of practice within the wider biodiversity domain. We also plan to integrate the current work of the Taxonomic Names and Concepts Interest Group and thereby eliminate the redundancy between DarwinCore and Taxonomic Concepts Transfer Schema (TCS; Kennedy et al. 2006). At the time of this writing, we have only agreements from the authors (i.e., conveners of relevant TDWG Interest Groups and other key stakeholders) to collaborate in pursuit of these common goals. In this presentation we will give a more detailed description of our objectives and products, the methods we are using to achieve them, and our progress to date.

Ontologies provide formal, machine-readable, and human-interpretable representations of domain knowledge. Therefore, ontologies have come into question with the development of Semantic Web technologies. People who want to use ontologies need an understanding of the ontology, but this understanding is very difficult to attain if the ontology user lacks the background knowledge necessary to comprehend the ontology or if the ontology is very large. Thus, software tools that facilitate the understanding of ontologies are needed. Ontology visualization is an important research area because visualization can help in the development, exploration, verification, and comprehension of ontologies. This paper introduces the design of a new ontology visualization tool, which differs from traditional visualization tools by providing important metrics and analytics about ontology concepts and warning the ontology developer about potential ontology design errors. The tool, called Onyx, also has advantages in terms of speed and readability. Thus, Onyx offers a suitable environment for the representation of large ontologies, especially those used in biomedical and health information systems and those that contain many terms. It is clear that these additional functionalities will increase the value of traditional ontology visualization tools during ontology exploration and evaluation. Öztürk, Ö., Açıkgöz, H. G. (2019), Onyx: A New Canvas-Based Tool For Visualizing Biomedical And Health Ontologies, New York: Wiley.

En el conjunto de las plataformas de gestión online de la docencia o LMS (Learning Management System), Moodle es posiblemente la más extendida a nivel mundial por las facilidades que ofrece en sus dimensiones organizativa, pedagógica y tecnológica. La Universidad de Málaga no es una excepción, ya que viene utilizando Moodle desde hace años para la integración de su Campus Virtual (CV). Esta plataforma, en su funcionamiento cotidiano, está recolectando grandes cantidades de datos (sobre 20.000 registros diarios) con información sobre las interacciones de los estudiantes, como: entradas, intervenciones en foros, entrega de tareas, consultas, selección de cursos, etc. Moodle ofrece módulos con capacidades de filtrado y estadísticas básicas, aunque la información generada se dispone en informes y ficheros con tablas de datos en crudo, sin posibilidad de obtener conocimiento explícito y significativo sobre la evolución de los alumnos y los cursos. La extracción de dicha información y su transformación en conocimiento útil para la mejora de los procesos educativos es una tarea compleja, ya que requiere de técnicas avanzadas de análisis de datos que den soporte a los educadores. En este proyecto, basándose en técnicas y herramientas de minería de datos, se pretenden generar una serie de funcionalidades para el análisis avanzado de los datos obtenidos a partir de la plataforma Moodle del Campus Virtual. El objetivo es alinear hábitos de uso de CV con desarrollo de los alumnos (rendimiento en las asignaturas) para detectar patrones y tratar de establecer mecanismos de detección, atención y acción temprana.

Irradiation experiments (IE) are an essential step in the development of High-Energy Physics (HEP) particle accelerators and detectors. They assess the radiation hardness of materials used in HEP experimental devices by simulating, in a short time, the common long-term degradation effects due to their bombardment by high-energy particles. IEs are also used in other scientific and industrial fields such as medicine (e.g., for cancer treatment, medical imaging, etc.), space/avionics (e.g., for radiation testing of payload equipment) as well as in industry (e.g., for food sterilization). Usually carried out with ionizing radiation, these complex processes require highly specialized infrastructures: the irradiation facilities. Currently, hundreds of such facilities exist worldwide. To help develop best practices and promote a computer-assisted handling and management of IEs, we introduce IEDM, a new OWL-based Irradiation Experiment Data Management ontology. This paper provides an overview of the classes and properties of IEDM. Since one of the key design choices for IEDM was to maximize the reuse of existing foundational ontologies such as the Ontology of Scientific Experiments (EXPO), the Ontology of Units of Measure (OM) and the Friend-of-a-Friend Ontology (FOAF), we discuss the methodological issues of the integration of IEDM with these imported ontologies. We illustrate the use of IEDM via an actual IE recently performed at IRRAD, the CERN proton irradiation facility. Finally, we discuss other motivations for this work, including the use of IEDM for the generation of user interfaces for IE management, and their impact on our methodology.

An ontology is a formal representation of domain knowledge, which can be interpreted by machines. In recent years, ontologies have become a major tool for domain knowledge representation and a core component of many knowledge management systems, decision support systems and other intelligent systems, inter alia, in the context of agriculture. A review of the existing literature on agricultural ontologies, however, reveals that most of the studies, which propose agricultural ontologies, are lacking an explicit evaluation procedure. This is undesired because without well-structured evaluation processes, it is difficult to consider the value of ontologies to research and practice. Moreover, it is difficult to rely on such ontologies and share them on the Semantic Web or between semantic aware applications. With the growing number of ontology-based agricultural systems and the increasing popularity of the Semantic Web, it becomes essential that such development and evaluation methods are put forward to guide future efforts of ontology development. Our work contributes to the literature on agricultural ontologies, by presenting a method for evaluating agricultural ontologies, which seems to be missing from most existing studies on agricultural ontologies. The framework supports the matching of appropriate evaluation methods for a given ontology based on the ontology's purpose., Comment: 18 pages

Background and Objective: Breast cancer is widely known as the most lethal and chronic disease among women. Computational techniques have been applied to support and aid early detection of the disease. Methods such as image analysis, knowledgebase systems, machine learning, reasoning algorithm and natural language processing techniques. Approaches aimed at leveraging the availability of patient electronic records have been investigated to support formalisation of medical reasoning thereby achieving early detection of breast cancer. However, computational performance of these approaches is often limited largely due to formalism used for representing the records. We consider that a body of information silo in patient record systems, when accurately formalized, could be efficiently used for improving the performance of computational diagnostic systems which detects breast cancer. The objective of this study is the use of an accurate method for representation and formalisation of text-based patient records and domain knowledge to provide support to medical reasoning algorithm aimed at diagnosing breast cancer. Methods: We propose an architecture which supports formalism of knowledgebase that is applied to reasoning algorithm used for diagnosing breast cancer. The method applied allow for the use of ontologies for the formalisation of both patient records and domain knowledge. Diagnostic procedure and guidelines in the domain were represented using rules based on semantic web rule language (SWRL). Furthermore, we applied the formalized ontologies and rules to Select and Test (ST) medical reasoning algorithm. Results: Experimentation was done using records of ten (10) patient collected from University Teaching Hospital. Result obtained showed that our proposed system achieved accuracy gain of 23.5% and AUC of (0.49, 1.0). Conclusion: The impressive performance of the proposed architecture demonstrates the effectiveness of using rules and ontologies for knowledge representation. In addition, we found the interesting performance of the applied ST algorithm as a pointer that it is a potential algorithm in modeling computer aided diagnostic systems (CADs) for detection of breast cancer.

Human-induced habitat changes have been typically linked to negative effects on native species, but an increasing number of studies show that many species are unaffected by these changes or even benefited from them. The Burrowing Owl Athene cunicularia is a raptor species that has deserved special attention in recent years due to its capacity to live in a variety of natural and modified habitats. In this study, we analyzed habitat characteristics that determine the habitat selection of the Burrowing Owl at the nest-patch, territory and landscape scales in the Pampas of Argentina. We performed broadcasting call surveys to evaluate presence-absence of owls at random points. In addition, we measured habitat variables in the field and used satellite imagery to obtain land-use information. We used Generalized Linear Models to explore the influence of habitat variables on the probability of occupancy by Burrowing Owls. Our results indicate that Burrowing Owls demonstrate good ability to live in a wide variety of habitat types and with different disturbance levels in the Pampas. At the nest-patch scale, which includes the nest-site and the surrounding patch around it, the presence of owls was positively associated with the horizontal visibility and was influenced by the land-cover type. At the territory scale, the occurrence of owls was positively associated with the presence of active (non-vegetated) dunes and negatively with croplands. At the landscape scale, the presence of owls was negatively associated with the disturbance level and positively with the amount of borders between habitats. A unique multi-scale model containing variables of the three spatial scales was more robust to explain variation in Burrowing Owl occupancy patterns than any single-scale model. This would reveal the hierarchical nature of habitat selection by Burrowing Owls in the Pampas, comparable to that observed in North American populations. Fil: Baladron Felix, Alejandro Victor. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Isacch, Juan Pablo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Cavalli, Matilde. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina Fil: Bó, Maria Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata. Instituto de Investigaciones Marinas y Costeras. Universidad Nacional de Mar del Plata. Facultad de Ciencia Exactas y Naturales. Instituto de Investigaciones Marinas y Costeras; Argentina

Mobile ad-hoc network is collection of mobile nodes like laptops; cell phones which have limited processing power and limited battery power also have limited bandwidth. MANET is self-adjustable with dynamic topology network, because of its mobile environment, routing face challenges. Main challenges in routing are to maximize delivery ratio and minimize End-to-End delay with minimum use of nodes resources, because mobile nodes have limited batter power and bandwidth. DSDV is proactive type routing protocol which trying to minimize route discovery time by manage route information in routing table and broadcast to other neighbors. OWL is a reactive routing protocol which uses DFS instead of broadcasting of RREQ to all its neighbors. In this paper we are going to compare these two routing protocol on the basis of their power consumption in different phases of routing.

In this paper, a novel approach using Description Logic (DL) based inference rules, for ontology matching is presented. Alignment concerns ontology concepts, with the application of similarity measures to perform concepts and instances relationship alignments. Moreover, external knowledge, in the form of WordNet dictionary is then used to solve usual matching problems encountered with synonyms, polesemy, homonyms, etc. Illustrative examples are then presented to support the developed approach.

Computational ontologies are machine-processable structures which represent particular domains of interest. They integrate knowledge which can be used by humans or machines for decision making and problem solving. The main aim of this systematic review is to investigate the role of formal ontologies in information systems development, i.e., how these graphs-based structures can be beneficial during the analysis and design of the information systems. Specific online databases were used to identify studies focused on the interconnections between ontologies and systems engineering. One-hundred eighty-seven studies were found during the first phase of the investigation. Twenty-seven studies were examined after the elimination of duplicate and irrelevant documents. Mind mapping was substantially helpful in organising the basic ideas and in identifying five thematic groups that show the main roles of formal ontologies in information systems development. Formal ontologies are mainly used in the interoperability of information systems, human resource management, domain knowledge representation, the involvement of semantics in unified modelling language (UML)-based modelling, and the management of programming code and documentation. We explain the main ideas in the reviewed studies and suggest possible extensions to this research.

The term Big Data refers to the large volume of data produced and made available in digital environments. Throughout the last years new models of representation have been proposed to improve the forms of representation of information in digital environments. The present work is linked to an ongoing research project, funded by FAPESP and CNPq agencies, and aims to analyze the principles underlying Big Data and its relationship with the new Resource Description Framework (RDF) representation patterns; Simple Knowledge Organization System (SKOS) and Onto-logy Web Language (OWL). The research has a theoretical character and a qualitative approach, as it seeks to present characteristics aimed at describing, understanding and explaining the relationships between Big Data and the new models of representation. From the theoretical survey carried out, it was verified that the representation models analyzed contribute to the interconnection of large volumes of data without losing the context in which they originated, favoring a better understanding of Big Data and the new paradigms of representation in digital environments. O termo Big Data se refere ao grande volume de dados produzidos e disponibilizados em ambientes digitais. Ao longo dos últimos anos novos modelos de representação têm sido propostos no intuito de aperfeiçoar as formas de representação de informações em ambientes digitais. O presente trabalho está vinculado a um projeto de pesquisa em andamento, financiado pelas agências FAPESP e CNPq, e possui como objetivo analisar os princípios que fundamentam o Big Data e sua relação com os novos padrões de representação Resource Description Framework (RDF); Simple Knowledge Organization System (SKOS) e Ontology Web Language (OWL). A pesquisa possui caráter teórico e abordagem qualitativa, pois busca apresentar características voltadas à descrição, compreensão e explicação das relações do Big Data com os novos modelos de representação. A partir do levantamento teórico realizado, foi verificado que os modelos de representação analisados contribuem para interligar grandes volumes de dados sem perder o contexto no qual são originados, favorecendo um melhor entendimento do Big Data e os novos paradigmas de representação em ambientes digitais.

This paper introduces Arachne, an RDF rule engine with support for efficient reasoning with large OWL RL terminologies. Arachne is being used by the Gene Ontology (GO) Consortium to provide real-time reasoning within the Noctua modeling tool while creating GO "Causal Activity Models".

Background Data-driven cell classification is becoming common and is now being implemented on a massive scale by projects such as the Human Cell Atlas. The scale of these efforts poses a challenge. How can the results be made searchable and accessible to biologists in general? How can they be related back to the rich classical knowledge of cell-types, anatomy and development? How will data from the various types of single cell analysis be made cross-searchable? Structured annotation with ontology terms provides a potential solution to these problems. In turn, there is great potential for using the outputs of data-driven cell classification to structure ontologies and integrate them with data-driven cell query systems. Results Focusing on examples from the mouse retina and Drosophila olfactory system, I present worked examples illustrating how formalization of cell ontologies can enhance querying of data-driven cell-classifications and how ontologies can be extended by integrating the outputs of data-driven cell classifications. Conclusions Annotation with ontology terms can play an important role in making data driven classifications searchable and query-able, but fulfilling this potential requires standardized formal patterns for structuring ontologies and annotations and for linking ontologies to the outputs of data-driven classification.

Fecha de lectura de Tesis: 10 de diciembre de 2018 La cantidad de información que se genera en la Web se ha incrementado en los últimos años. La mayor parte de esta información se encuentra accesible en texto, siendo el ser humano el principal usuario de la Web. Sin embargo, a pesar de todos los avances producidos en el área del procesamiento del lenguaje natural, los ordenadores tienen problemas para procesar esta información textual. En este cotexto, existen dominios de aplicación en los que se están publicando grandes cantidades de información disponible como datos estructurados como en el área de las Ciencias de la Vida. El análisis de estos datos es de vital importancia no sólo para el avance de la ciencia, sino para producir avances en el ámbito de la salud. Sin embargo, estos datos están localizados en diferentes repositorios y almacenados en diferentes formatos que hacen difícil su integración. En este contexto, el paradigma de los Datos Vinculados como una tecnología que incluye la aplicación de algunos estándares propuestos por la comunidad W3C tales como HTTP URIs, los estándares RDF y OWL. Haciendo uso de esta tecnología, se ha desarrollado esta tesis doctoral basada en cubrir los siguientes objetivos principales: 1) promover el uso de los datos vinculados por parte de la comunidad de usuarios del ámbito de las Ciencias de la Vida 2) facilitar el diseño de consultas SPARQL mediante el descubrimiento del modelo subyacente en los repositorios RDF 3) crear un entorno colaborativo que facilite el consumo de Datos Vinculados por usuarios finales, 4) desarrollar un algoritmo que, de forma automática, permita descubrir el modelo semántico en OWL de un repositorio RDF, 5) desarrollar una representación en OWL de ICD-10-CM llamada Dione que ofrezca una metodología automática para la clasificación de enfermedades de pacientes y su posterior validación haciendo uso de un razonador OWL.

Part 1: Social Media - Games - Ontologies; International audience; Diagrams are widely used in our day to day communication. A knowledge of the spatial patterns used in diagrams is essential to read and understand them. In the context of diagrams, spatial patterns mean accepted spatial arrangements of graphical and textual elements used to represent diagram-specific concepts. In order to assist with the automated understanding of diagrams by computer applications, this paper presents an ontology-based approach to recognise diagram-specific concepts from the spatial patterns in diagrams. Specifically, relevant spatial patterns of diagrams are encoded in an ontology, and the automated instance classification feature of the ontology reasoners is utilised to map spatial patterns to diagram-specific concepts depicted in a diagram. A prototype of this approach to support automated recognition of UML and domain concepts from class diagrams and its performance are also discussed in this paper. This paper concludes with a reflection of the strengths and limitations of the proposed approach.

Most raptor species rely on perches for hunting, resting, preening and roosting and in many agricultural areas the availability of adequate perches can limit raptor abundance and diversity. This has negative implications for both raptor conservation and the natural pest control services they can provide for farmers. Installing artificial perches on agricultural lands can therefore benefit both raptors and farmers. However, installing perches under current guidelines is difficult on California’s 38 million acres of rangelands, where rocky soil can be restrictive to anchoring poles belowground. We developed a novel method for modifying existing fenceposts to support raptor perches. These raptor perches are relatively light, easy to construct, can be transported in multiple pieces and assembled in the field. We installed 16 artificial raptor perches in four representative habitats on a California ranch to 1) determine if raptors will use artificial perches in each habitat; 2) test raptor preferences for different perch configurations; and 3) observe which raptor species utilize perches. Here, we share our perch design as well as our results from monitoring perches between August and November 2017. We found that American kestrels, great horned owls, barn owls, and red-tailed hawks utilized perches, as did a number of non-raptor species. Perch use by raptors was highest in an irrigated pasture and at a mid-elevation grassland site located on a hill. Perches at a low-elevation, unirrigated pasture with no slope and perches located in higher elevation oak woodland sites were used significantly less. In all habitats raptors preferred to utilize 15-foot perches over 20-foot perches. Raptors rarely utilized a lower perch when two were available on a single pole, suggesting that this added feature is not necessary to attract the species we observed.

The current Digital Libraries (DLs) usually return as answer of a user's query a ranked list of the resources included in the DLs but no semantic relation among the resources are reported. Using the Semantic Web technologies it is possible to improve these search functionalities introducing narratives as new search method. As narratives we intend semantic networks of events that are linked to the objects of the DLs and are endowed with a set of semantic relations that connect an event to another. These semantic networks may help the users to obtain a more complete knowledge on the subject of their searches. In this paper, we present a software architecture for building narratives in order to introduce them in DLs. Our architecture is composed of several tools (automatic and semi-automatic tools) for creating, storing and visualizing narratives. When possible, we reused open source components already available on-line, and for the software we developed, we freely distribute it for research aims.

Ontologies are complex systems of axioms in which unanticipated consequences of changes are both frequent, and diffcult for ontology authors to apprehend. The effects of modelling actions range from unintended inferences to outright defects such as incoherency or even inconsistency. One of the central ontology authoring activities is verifying that a particular modelling step has had the intended consequences, often with the help of reasoners. For users ofProtégé, this involves, for example, exploring the inferred class hierarchy.This paper provides evidence that making entailment set changes explicit to authors significantly improves the understanding of authoring actions regarding both correctness and speed. This is tested by means of the InferenceInspector, a Protégé plugin we created that provides authors with specific details about the effects of an authoring action. We empirically validate the effectiveness of the Inference Inspector in two studies. In a first, exploratory study we determine the feasibility of the Inference Inspector for supporting verification and isolating authoring actions. In a second, controlled study we formally evaluate the Inference Inspector and determine that making changes to key entailment sets explicit significantly improves author verification compared to the standard static hierarchy/framebased approach. We discuss the advantages of the Inference Inspector for different types of verification questions and find that our approach is best suited for verifying added restrictions where no new signature, such as class names, is introduced, with a 42% improvement in verification correctness.

Representing uncertain information is crucial for modeling real world domains. In this paper we present a technique for the integration of probabilistic information in Description Logics (DLs) that is based on the distribution semantics for probabilistic logic programs. In the resulting approach, that we called DISPONTE, the axioms of a probabilistic knowledge base (KB) can be annotated with a real number between 0 and 1. A probabilistic knowledge base then denes a probability distribution over regular KBs called worlds and the probability of a given query can be obtained from the joint distribution of the worlds and the query by marginalization. We present the algorithm BUNDLE for computing the probability of queries from DISPONTE knowledge bases. The algorithm exploits an underlying DL reasoner, such as Pellet, that is able to return explanations for queries. The explanations are encoded in a Binary Decision Diagram from which the probability of the query is computed. The experimentation of BUNDLE on probabilistic knowledge bases shows that it can handle knowledge bases of realistic size.