Search

The use of social network theory and methods of analysis have been applied to different domains in recent years, including public health. The complete procedure for carrying out a social network analysis (SNA) is a time-consuming task that entails a series of steps in which the expert in social network analysis could make mistakes. This research presents a multi-domain knowledge model capable of automatically gathering data and carrying out different social network analyses in different domains, without errors and obtaining the same conclusions that an expert in SNA would obtain. The model is represented in an ontology called OntoSNAQA, which is made up of classes, properties and rules representing the domains of People, Questionnaires and Social Network Analysis. Besides the ontology itself, different rules are represented by SWRL and SPARQL queries. A Knowledge Based System was created using OntoSNAQA and applied to a real case study in order to show the advantages of the approach. Finally, the results of an SNA analysis obtained through the model were compared to those obtained from some of the most widely used SNA applications: UCINET, Pajek, Cytoscape and Gephi, to test and confirm the validity of the model.

Integration of heterogeneous data sources in a single representation is an active field with many different tools and techniques. In the case of text-based approaches—those that base the definition of the mappings and the integration on a DSL—there is a lack of usability studies. In this work we have conducted a usability experiment (n = 17) on three different languages: ShExML (our own language), YARRRML and SPARQL-Generate. Results show that ShExML users tend to perform better than those of YARRRML and SPARQL-Generate. This study sheds light on usability aspects of these languages design and remarks some aspects of improvement.

In this paper we provide a comprehensive introduction to knowledge graphs, which have recently garnered significant attention from both industry and academia in scenarios that require exploiting diverse, dynamic, large-scale collections of data. After some opening remarks, we motivate and contrast various graph-based data models and query languages that are used for knowledge graphs. We discuss the roles of schema, identity, and context in knowledge graphs. We explain how knowledge can be represented and extracted using a combination of deductive and inductive techniques. We summarise methods for the creation, enrichment, quality assessment, refinement, and publication of knowledge graphs. We provide an overview of prominent open knowledge graphs and enterprise knowledge graphs, their applications, and how they use the aforementioned techniques. We conclude with high-level future research directions for knowledge graphs., Comment: Revision from v5: Correcting errata from previous version for entailment/models, and some other minor typos

Interoperable information system for models within the same sector.The information will be shared through information models, Web Services.It is a low-cost, providing in the end a semi-automatic self-learning solution.The system is capable of interoperate a three-step matching process.The system lacks enough information, it will require human intervention. This paper deals with the exchange of information between universities problematic, but could influence other sectors like banking, health, etc. where there are not standards defined for information exchange. We propose a semi-automatic learning and assessment system that is capable of unifying the way in which each of the universities work by employing a three-step system that will be based on the exploitation of the information offered by the web services in their WSDL files and the way in which each of the universities that had been previously evaluated work. This three-step system would be focused on: (a) taking advantage of the information that is obtained on a structural level from the WSDL, which, using the String-Metrics (Cohen, Ravikumar, & Fienberg, 2003) application, will be able to connect the structures between universities, (b) reinforcing any prior knowledge with a classification system for cases that could not be matched, and (c) applying a reasoning system or rules to support the previous two steps. The main result obtained is a system that is able to interoperate two or more platforms between themselves to automatically share information in spite of their structural difference.

EDITOR'S SUMMARY Defined in 1999 and paired with XML, the Resource Description Framework (RDF) has been cast as an RDF Schema, producing data that is well-structured but not validated, permitting certain illogical relationships. When stakeholders convened in 2014 to consider solutions to the data validation challenge, a W3C working group proposed Resource Shapes and Shape Expressions to describe the properties expected for an RDF node. Resistance rose from concerns about data and schema reuse, key principles in RDF. Ideally data types and properties are designed for broad use, but they are increasingly adopted with local restrictions for specific purposes. Resource Shapes are commonly treated as record classes, standing in for data structures but losing flexibility for later reuse. Of various solutions to the resulting tensions, the concept of record classes may be the most reasonable basis for agreement, satisfying stakeholders' objectives while allowing for variations with constraints.

Over the past few years, Public Administrations have been providing systems for procedures and files electronic processing to ensure compliance with regulations and provide public services to citizens. Although each administration provides similar services to their citizens, these systems usually differ from the internal information management point of view since they usually come from different products and manufacturers. The common framework that regulations demand, and that Public Administrations must respect when processing electronic files, provides a unique opportunity for the development of intelligent agents in the field of administrative processes. However, for this development to be truly effective and applicable to the public sector, it is necessary to have a common representation model for these administrative processes. Although a lot of work has already been done in the development of public information reuse initiatives and common vocabularies standardization, this has not been carried out at the processes level. In this paper, we propose a semantic representation model of both processes models and processes for Public Administrations: the procedures and administrative files. The goal is to improve public administration open data initiatives and help to develop their sustainability policies, such as improving decision-making procedures and administrative management sustainability. As a case study, we modelled public administrative processes and files in collaboration with a Regional Public Administration in Spain, the Principality of Asturias, which enabled access to its information systems, helping the evaluation of our approach.

This work has been funded by a collaboration with the Izertis company funded by the Ministerio de Industria, Energia y Turismo in the call of Accion Estrategica de Economia y Sociedad Digital del 2014. (TSI-100600-2014-44) Title: Linked Open Data Learning (LOP): Enriqueciendo la experiencia formativa en eLearning; the Department of Science and Innovation (Spain) under the National Program for Research, Development and Innovation: project EDU2014-57571-P; the European Union, through the European Regional Development Funds (ERDF); and the Principality of Asturias, through its Science, Technology and Innovation Plan (grant GRUPIN14-100). The authors also want to thank the State High School teachers: Modesto and Maria Jose, as well as their students for their willingness and collaboration with the experiment described in this work.

This paper presents an extensible framework for the Spreading Activation technique. This technique is supported by the ONTOSPREAD framework enabling the development, configuration, customization, and execution of the Spreading Activation method on graph-based structures. It has been used for a long time to the efficient exploration of knowledge bases built on semantic networks in Information and Document Retrieval domains. The emerging Web of Data and the sheer mass of information now available make it possible the deployment of new services and applications based on the reuse of existing vocabularies and datasets. A large amount of this information is published using semantic web languages and formats such as RDF, implicit graph structures developed using W3C standard languages; but new flexible and scalable methods to create added-value services and exploit the data are required. That is why ONTOSPREAD is considered relevant in providing a new way to implement the double process of activation and spreading of concepts in graph-based structures, more specifically to browse and rank resources in the Web of Data realm. The original constraints like weight degradation according to the distance are provided in combination with others coming from the extension of this technique like the converging paths reward. Finally an evaluation methodology and two examples using the well-known ontologies GALEN and SNOMED CT are presented to validate the goodness, the improvement, and the capabilities of this technique.

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.

Program slicing is an important decomposition technique. It has been widely used in many software activities, such as software analyzing, understanding, debugging, testing, and maintenance. The current slicing methods and tools, however, are monolithic, and mainly based on program or system dependence graph. This paper presents a novel formal tool for program slicing. It abstracts the computation of program slicing as a language-independent slice monad transformer, which can be applied to the semantic descriptions of the program in a modular way, forming the corresponding slicing algorithms. Such algorithms allow program slices to be computed directly on abstract syntax, with no need to explicitly construct intermediate structures such as dependence graphs or to record an execution history. It has reusability and language-flexibility properties in comparison with the current program slicing methods/tools.

RDF is a graph based data model which is widely used for semantic web and linked data applications. In this paper we describe a Shape Expression definition language which enables RDF validation through the declaration of constraints on the RDF model. Shape Expressions can be used to validate RDF data, communicate expected graph patterns for interfaces and generate user interface forms. In this paper we describe the syntax and the formal semantics of Shape Expressions using inference rules. Shape Expressions can be seen as domain specific language to define Shapes of RDF graphs based on regular expressions.Attached to Shape Expressions are semantic actions which provide an extension point for validation or for arbitrary code execution such as those in parser generators. Using semantic actions, it is possible to augment the validation expressiveness of Shape Expressions and to transform RDF graphs in a easy way.We have implemented several validation tools that check if an RDF graph matches against a Shape Expressions schema and infer the corresponding Shapes. We have also implemented two extensions, called GenX and GenJ that leverage the predictability of the graph traversal and create ordered, closed content, XML/Json documents, providing a simple, declarative mapping from RDF data to XML and Json documents.

In this paper we describe the development of the Web Index linked data portal that represents statistical index data and the computations from which it has been obtained.

The idea of "Web of data"[21] has been widely enhanced by the establishment of Linked Data principles on the Web [5]. The emergence of Linking Open Data Project opens the doors to the concept of Linked Open Data, establishing the basis for publishing open data, usable by anyone on the Web. However, even though the form (Linked Data) and the goal (Web of data) have been formally defined, the definition of a components architecture to support the implementation of such technologies is still fuzzy, as is a methodology of implementation associated with this architecture. The problem is that the definition and methodology together should enable both publishing and maintenance of semantic data in a standardized way for a subset of publishers, namely public administrators. In this paper we describe a first approach to an adoption process of Web Semantic technologies, specifically with regard to tools and methods that enable the publication and maintenance of Linked Open Data within the context of public administration. To this end, we review the related basic concepts, define infrastructure, describe its components and functions, and propose a sequence to be followed. Finally, we present a case of the use of our methodology in the Library of Congress of Chile.

The processing of high amount of documents is a highly complex challenge, which becomes even more complicated when the goal is to extract the semantically relevant data within the documents. The large-scale processing of immense repositories of knowledge requires techniques which perform information extraction to facilitate the subsequent classification and indexing of texts. Having this into account, we propose the use of Dublin Core metadata for the classification of Software Engineering publications. Based on the information obtained from Dublin Core, we present a global repository that is populated automatically, which takes the form of an ontology which represents the distinct areas of Software Engineering knowledge inspired by SWEBOK (Software Engineering Body of Knowledge). Finally, the process of the classification of texts within the ontology is carried out in three steps: keyword analysis, processing of the document. We believe our proposal based on a linguistic text classification method, heuristics, and subsequently the intersection of the three techniques mentioned, generating more precise search results in response to user queries.

Nowadays there is a large number of Expert Systems available to users requiring the extraction of data relevant to specific domains, many of which are based on reasoning and inference. However, many of these tools offer slow execution time, resulting in delayed response times to the queries made by users. The strategy of caching to define specific patterns of results enables such systems to eliminate the requirement to repeat the same queries, speeding up the response time and eliminating redundancy. This paper proposes a caching strategy for an Expert System based on Semantic Web and reasoning and inference techniques. Caching strategies have previously been applied to simple XML queries. Performance has been evaluated using an existing system for medical diagnosis, which demonstrates the increased efficiency of the system.

We present a Language Prototyping System that facilitates the modular development of interpreters from semantic specifications. The theoretical basis of our system is the integration of ideas from generic programming and modular monadic semantics. The system is implemented as a domain-specific language embedded in Haskell and contains an interactive framework for language prototyping. In the monadic approach, the semantic spscification of a programming language is captured as a function Σ → MV where Σ represents the abstract syntax, M the computational monad, and V the domain value. In order to obtain more extensibility, we use folds or catamorphisms over the fixpoint of non-recursive pattern functors that capture the structure of the abstract syntax. For each pattern functor F, the semantic specifications are defined as independent F-Algebras whose carrier is M V, where M is the computational monad and V models the domain value. The copmputational monad M can itself be obtained from the composition of several monad transformers applied to a base monad, and the domain value V can be defined using extensible union types. In this paper, we also show that when the abstract syntax contains several categories, it is possible to define many-sorted algebras obtaining the same modularity.

Language processors are not only important for the construction of compilers and interpreters but also for an increasing number of applications that relay on XML parsers like e-commerce software or grammar checkers like word processors.

Compiler construction tools have proven to be very useful for developers in the traditional task of compiler construction and language processors that are present in editors, word processors or XML. In contrast to their importance, traditional tools like YACC have failed to take advantage of object-oriented techniques. This is particularly unfortunate because compilers, being complex systems, would definitely benefit from the modularity and code reuse that object orientation allows.In this paper, O2C2, an object-oriented framework generator, is presented. The tool has the potential to improve the quality of compiler construction by introducing the concept of frameworks in the field of compilers and language processors.