Scientific Knowledge Object Patterns

Web technology is revolutionizing the way diverse scientific knowledge is produced and disseminated. In the past few years, a handful of discourse representation models have been proposed for the externalization of the rhetoric and argumentation captured within scientific publications. However, there hasn’t been a unified interoperable pattern that is commonly used in practice by publishers and individual users yet. In this paper, we introduce the Scientific Knowledge Object Patterns (SKO Patterns) towards a general scientific discourse representation model, especially for managing knowledge in emerging social web and semantic web. © ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version is going to be published in "Proceedings of 15th European Conference on Pattern Languages of Programs", (2011) http://portal.acm.org/event.cfm?id=RE197&CFID=8795862&CFTOKEN=1476113

A short survey of discourse representation models

With the advancement of technology and the wide adoption of ontologies as knowledge representation formats, in the last decade, a handful of models were proposed for the externalization of the rhetoric and argumentation captured within scientific publications. Conceptually, most of these models share a similar representation form of the scientific publication, i.e. as a series of interconnected elementary knowledge items. The main differences are given by the terminology used, the types of rhetorical and/or argumentation relations connecting the knowledge items and the foundational theories supporting these relations. This paper analyzes the state of the art and provides a concise comparative overview of the five most prominent discourse representation models, with the goal of sketching an unified model for discourse representation

Semantic Web technologies in software engineering

Over the years, the software engineering community has developed various tools to support the specification, development, and maintainance of software. Many of these tools use proprietary data formats to store artifacts which hamper interoperability. However, the Semantic Web provides a common framework that allows data to be shared and reused across application, enterprise, and community boundaries. Ontologies are used define the concepts in the domain of discourse and their relationships and as such provide the formal vocabulary applications use to exchange data. Beside the Web, the technologies developed for the Semantic Web have proven to be useful also in other domains, especially when data is exchanged between applications from different parties. Software engineering is one of these domains in which recent research shows that Semantic Web technologies are able to reduce the barriers of proprietary data formats and enable interoperability. In this tutorial, we present Semantic Web technologies and their application in software engineering. We discuss the current status of ontologies for software entities, bug reports, or change requests, as well as semantic representations for software and its documentation. This way, architecture, design, code, or test models can be shared across application boundaries enabling a seamless integration of engineering results

ODEWiki: A Semantic Wiki That Interoperates with the ODESeW Semantic Portal

We present ODEWiki, a technology for the development of Semantic Wikis, which has a combined set of added-value features over other existing semantic wikis in the state of the art. Namely, ODEWiki interoperates with an existing semantic portal technology (ODESeW), it manages inconsistencies raised because of the distributed nature of knowledge base development and maintenance, it uses RDFa for the annotation of the resulting wiki pages, it follows a WYSIWYG approach, and it allows decoupling wiki pages and ontology instances, that is, a wiki page may contain one or several ontology instances. Although some of these features appear in some of the state-of-the-art semantic wikis, but they are not combined together in a single solution

Enriching ontological user profiles with tagging history for multi-domain recommendations

Many advanced recommendation frameworks employ ontologies of various complexities to model individuals and items, providing a mechanism for the expression of user interests and the representation of item attributes. As a result, complex matching techniques can be applied to support individuals in the discovery of items according to explicit and implicit user preferences. Recently, the rapid adoption of Web2.0, and the proliferation of social networking sites, has resulted in more and more users providing an increasing amount of information about themselves that could be exploited for recommendation purposes. However, the unification of personal information with ontologies using the contemporary knowledge representation methods often associated with Web2.0 applications, such as community tagging, is a non-trivial task. In this paper, we propose a method for the unification of tags with ontologies by grounding tags to a shared representation in the form of Wordnet and Wikipedia. We incorporate individuals' tagging history into their ontological profiles by matching tags with ontology concepts. This approach is preliminary evaluated by extending an existing news recommendation system with user tagging histories harvested from popular social networking sites

Towards analytics and collaborative exploration of social and linked media for technology-enchanced learning scenarios

Social Web applications such as "Flickr", "Youtube" and "Slideshare" offer a vast body of multimedial knowledge, discoverable through the appropriate search interfaces and API's. This extensive information source, however, is largely unstructured and the available metadata is typically limited to title, tags and description for a resource. On the other hand, Linked Web Data is both structured and well described through a variety of metadata. Combining those sources opens promising direction for knowledge discovery and, at the same time, new challenges for collaborative searching in various Technology-Enchanced Learning Scenarios. In this paper, we explore how to support (collaborative) search in such scenarios through an initial analysis of the Web data landscape and introduce early results from efforts on exploiting Linked Data techniques to solve critical issues in this context

CONGAS: a collaborative ontology development framework based on Named GrAphS

The process of ontology development involves a range of skills and know-how often requiring team work of different people, each of them with his own way of contributing to the definition and formalization of the domain representation. For this reason, collaborative development is an important feature for ontology editing tools, and should take into account the different characteristics of team participants, provide them with a dedicated working environment allowing to express their ideas and creativity, still protecting integrity of the shared work. In this paper we present CONGAS, a collaborative version of the Knowledge Management and Acquisition platform Semantic Turkey which, exploiting the potentialities brought by recent introduction of context management into RDF triple graphs, offers a collaborative environment where proposals for ontology evolution can emerge and coexist, be evaluated by team users, trusted across different perspectives and eventually converged into the main development stream

A semantic approach for learning objects repositories with knowledge reuse

In this paper we discuss how the inclusion of semantic functionalities in a Learning Objects Repository allows a better characterization of the learning materials enclosed and improves their retrieval through the adoption of some query expansion strategies. Thus, we started to regard the use of ontologies to automatically suggest additional concepts when users are filling some metadata fields and add new terms to the ones initially provided when users specify the keywords with interest in a query. Dealing with different domain areas and having considered impractical the development of many different ontologies, we adopted some strategies for reusing ontologies in order to have the knowledge necessary in our institutional repository. In this paper we make a review of the area of knowledge reuse and discuss our approach

Cognitive Process Designer -An Open-Source Tool to Capture Processes according to the Linked Data Principles Demo Paper

Abstract. Processes need to be captured in a structured way in order to analyze them by using computer-assisted methods. This circumstance becomes more important as the process becomes complex. Although there are standardized formats, they do not capture semantics of input/output parameters, involved persons or references to external data sources. Existing solutions provide tools to capture processes locally and specify new properties to extend the semantics of process languages. However, a collaborative platform to capture, discuss and share information is more advantageous, because processes are usually used and maintained collaboratively. In addition, users cannot define own semantics for their use-case scenarios and the proposed semantics and processes are not published according to the Linked Data principles. To address these problems we 1) provide an open-source tool to capture BPMN processes graphically in a Semantic MediaWiki; 2) allow users to define own semantics and 3) publish the information according to the Linked Data principles