User-system cooperation in document annotation based on information extraction

The process of document annotation for the Semantic Web is complex and time consuming, as it requires a great deal of manual annotation. Information extraction from texts (IE) is a technology used by some very recent systems for reducing the burden of annotation. The integration of IE systems in annotation tools is quite a new development and there is still the necessity of thinking the impact of the IE system on the whole annotation process. In this paper we initially discuss a number of requirements for the use of IE as support for annotation. Then we present and discuss a model of interaction that addresses such issues and Melita, an annotation framework that implements a methodology for active annotation for the Semantic Web based on IE. Finally we present an experiment that quantifies the gain in using IE as support to human annotators.peer-reviewe

Ontologies and the Semantic Web for Digital Investigation Tool Selection

The nascent field of digital forensics is heavily influenced by practice. Much digital forensics research involves the use, evaluation, and categorization of the multitude of tools available to researchers and practitioners. As technology evolves at an increasingly rapid pace, the digital forensics field must constantly adapt by creating and evaluating new tools and techniques to perform forensic analysis on many disparate systems such as desktops, notebook computers, mobile devices, cloud, and personal wearable sensor devices, among many others. While researchers have attempted to use ontologies to classify the digital forensics domain on various dimensions, no ontology of digital forensic tools has been developed that defines the capabilities and relationships among the various digital forensic tools. To address this gap, this work develops an ontology using Resource Description Framework (RDF) and Ontology Web Language (OWL) which is searchable via SP ARQL ( an RDF query language) and catalogues common digital forensic tools. Following the concept of ontology design patterns, our ontology has a modular design to promote integration with existing ontologies. Furthermore, we progress to a semantic web application that employs reasoning in order to aid digital investigators with selecting an appropriate tool. This work serves as an important step towards building the knowledge of digital forensics tools. Additionally, this research sets the preliminary stage to bringing semantic web technology to the digital forensics domain as well as facilitates expanding the developed ontology to other tools and features, relationships, and forensic techniques

A Double Classification of Common Pitfalls in Ontologies

The application of methodologies for building ontologies has improved the ontology quality. However, such a quality is not totally guaranteed because of the difficulties involved in ontology modelling. These difficulties are related to the inclusion of anomalies or worst practices in the modelling. In this context, our aim in this paper is twofold: (1) to provide a catalogue of common worst practices, which we call pitfalls, and (2) to present a double classification of such pitfalls. These two products will serve in the ontology development in two ways: (a) to avoid the appearance of pitfalls in the ontology modelling, and (b) to evaluate and correct ontologies to improve their quality

An Approach to Cope with Ontology Changes for Ontology-based Applications

Keeping track of ontology changes is becoming a critical issue for ontology-based applications because updating an ontology that is in use may result in inconsistencies between the ontology and the knowledge base, dependent ontologies and dependent applications/services. Current research concentrates on the creation of ontologies and how to manage ontology changes in terms of the attempts to ease the communications between ontology versions and keep consistent with the instances, and there is little work available on controlling the impact to dependent applications/services which is the aims of the system presented in this paper. The approach we propose in this paper is to manually capture and log ontology changes, use this log to analyse incoming RDQL queries and amend them as necessary. Revised queries can then be used to query the knowledge base of the applications/services. We present the infrastructure of our approach based on the problems and scenarios identified within ontology-based systems. We discuss the issues met during our design and implementation, and consider some problems whose solutions will be beneficial to the development of our approach

Spatial groundings for meaningful symbols

The increasing availability of ontologies raises the need to establish relationships and make inferences across heterogeneous knowledge models. The approach proposed and supported by knowledge representation standards consists in establishing formal symbolic descriptions of a conceptualisation, which, it has been argued, lack grounding and are not expressive enough to allow to identify relations across separate ontologies. Ontology mapping approaches address this issue by exploiting structural or linguistic similarities between symbolic entities, which is costly, error-prone, and in most cases lack cognitive soundness. We argue that knowledge representation paradigms should have a better support for similarity and propose two distinct approaches to achieve it. We first present a representational approach which allows to ground symbolic ontologies by using Conceptual Spaces (CS), allowing for automated computation of similarities between instances across ontologies. An alternative approach is presented, which considers symbolic entities as contextual interpretations of processes in spacetime or Differences. By becoming a process of interpretation, symbols acquire the same status as other processes in the world and can be described (tagged) as well, which allows the bottom-up production of meaning

Semantic Network Analysis of Ontologies

A key argument for modeling knowledge in ontologies is the easy re-use and re-engineering of the knowledge. However, current ontology engineering tools provide only basic functionalities for analyzing ontologies. Since ontologies can be considered as graphs, graph analysis techniques are a suitable answer for this need. Graph analysis has been performed by sociologists for over 60 years, and resulted in the vivid research area of Social Network Analysis (SNA). While social network structures currently receive high attention in the Semantic Web community, there are only very few SNA applications, and virtually none for analyzing the structure of ontologies. We illustrate the benefits of applying SNA to ontologies and the Semantic Web, and discuss which research topics arise on the edge between the two areas. In particular, we discuss how different notions of centrality describe the core content and structure of an ontology. From the rather simple notion of degree centrality over betweenness centrality to the more complex eigenvector centrality, we illustrate the insights these measures provide on two ontologies, which are different in purpose, scope, and size

Bibliographic ontologies and the semantic web: limitations and research proposals

A literatura sobre dados e ontologias bibliográficos na Web Semântica identifica problemas, não ao nível das instâncias de dados ou da sua publicação em conjuntos isolados, mas sim relativamente às ontologias que descrevem os conceitos que lhe estão subjacentes, com reflexo na qualidade da interoperabilidade semântica e na partilha de ontologias entre sistemas. Aborda-se a adequação à Web Semântica dos modelos conceptuais e limitações de FRBR - Functional Requirements for Bibliographic Records (IFLA 1998, 2018) ; a ausência de enquadramento conceptual comum; a insuficiência de mecanismos semânticos; a baixa e deficiente reutilização de vocabulários externos; e a inadequação das metodologias de mapeamento aplicadas. Apresenta-se um projeto de investigação que propõe uma solução para os problemas semânticos de partilha de ontologias, através da criação de um modelo de referência conceptualmente enquadrador e de uma ontologia de referência que funcione como instrumento de relacionamento semântico de alto nível e de validação de dados, recorrendo à linguagem SHACL - Shapes Constraint Language (KNUBLAUCH e KONTOKOSTAS, 2017).info:eu-repo/semantics/publishedVersio

Blending the physical and the digital through conceptual spaces

The rise of the Internet facilitates an ever increasing growth of virtual, i.e. digital spaces which co-exist with the physical environment, i.e. the physical space. In that, the question arises, how physical and digital space can interact synchronously. While sensors provide a means to continuously observe the physical space, several issues arise with respect to mapping sensor data streams to digital spaces, for instance, structured linked data, formally represented through symbolic Semantic Web (SW) standards such as OWL or RDF. The challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the vast variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an approach which allows to refine symbolic concepts as CS and to ground ontology instances to so-called prototypical members which are vectors in the CS. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of CS members, the most similar instance can be identified. In that, we provide a means to bridge between the physical space, as observed by sensors, and the digital space made up of symbolic representations

Bridging between sensor measurements and symbolic ontologies through conceptual spaces

The increasing availability of sensor data through a variety of sensor-driven devices raises the need to exploit the data observed by sensors with the help of formally specified knowledge representations, such as the ones provided by the Semantic Web. In order to facilitate such a Semantic Sensor Web, the challenge is to bridge between symbolic knowledge representations and the measured data collected by sensors. In particular, one needs to map a given set of arbitrary sensor data to a particular set of symbolic knowledge representations, e.g. ontology instances. This task is particularly challenging due to the potential infinite variety of possible sensor measurements. Conceptual Spaces (CS) provide a means to represent knowledge in geometrical vector spaces in order to enable computation of similarities between knowledge entities by means of distance metrics. We propose an ontology for CS which allows to refine symbolic concepts as CS and to ground instances to so-called prototypical members described by vectors. By computing similarities in terms of spatial distances between a given set of sensor measurements and a finite set of prototypical members, the most similar instance can be identified. In that, we provide a means to bridge between the real-world as observed by sensors and symbolic representations. We also propose an initial implementation utilizing our approach for measurement-based Semantic Web Service discovery

Use of ontologies and the semantic web for qualifications framework transparency

The problem of correlating and comparing the levels of the European and national qualifications framework and the potential of the Semantic Web technologies for solving this problem were explored. We substantiated the need for creating models and methods, aimed at providing transparency of the European and national qualifications frameworks and the development of tools for implementing these methods. Authors proposed a reference model of the qualifications frame-work that formalizes knowledge of basic information objects relating to learning outcomes and their epresentation in the qualifications frameworks. The specific feature of this model implies using atomic competencies: semantics of information objects of different classes is formalized through the set of such atomic competences that are associated with different properties of these objects. This should provide for the automatic matching of these information objects on the level of knowledge. The methods of quantitative estimation of semantic proximity between information objects of different classes of ontological models, which corresponds to different problems, are proposed in the work. This allows identifying a similarity between learning outcomes, which are described with the use of descriptors of different qualification frameworks. Information regarding atomic competences is obtained from the national and European standards, qualifications frameworks, spe-ciality descriptions, etc. They may be automatically supplemented via analysis of relevant information of Web-resources that contain semantic markup. The work considers in detail the mechanism of integration of the reference information model of competences with technological en-vironment Semantic MediaWiki: ontological concepts and relations are used for semantic markup of Wiki-pages by categories and se-mantic properties. This allows running a variety of semantic queries to the content of pages, relating to learning outcomes. Examples of such queries are given and their expressive power is analyzed. An example of using the ontological model of competences for improving semantic Web-search for the information for the purpose of supplementing and updating Wiki-pages was studied. The ontol-ogy potential in specification of information needs and the increased intersection of the obtained results is demonstrated with the ex-ample of the semantic search engine MAIPS. Keywords: qualifications framework, ontology of competences, Wiki, semantic markup, semantic search