Sasa Nesic

A survey of social software engineering

Software engineering is a complex socio-technical activity, due to the need for discussing and sharing knowledge among team members. This has raised the need for effective ways of sharing ideas, knowledge, and artifacts among groups and their members. The social aspect of software engineering process also demands computer support to facilitate the development by means of collaborative tools, applications and environments. In this paper, we present a survey of relevant works from psychology, mathematics and computer science studies. The combination of these fields provides the required infrastructure for engineering social and collaborative applications as well as the software engineering process. We also discuss possible solutions for the encountered shortcomings, and how they can improve software development.

Semantic Document Model to Enhance Data and Knowledge Interoperability

To enable document data and knowledge to be efficiently shared and reused across application, enterprise, and community boundaries, desktop documents should be completely open and queryable resources, whose data and knowledge are represented in a form understandable to both humans and machines. At the same time, these are the requirements that desktop documents need to satisfy in order to contribute to the visions of the Semantic Web. With the aim of achieving this goal, we have developed the Semantic Document Model (SDM), which turns desktop documents into Semantic Documents as uniquely identified and semantically annotated composite resources, that can be instantiated into human-readable (HR) and machine-processable (MP) forms. In this paper, we present the SDM along with an RDF and ontology-based solution for the MP document instance. Moreover, on top of the proposed model, we have built the Semantic Document Management System (SDMS), which provides a set of services that exploit the model. As an application example that takes advantage of SDMS services, we have extended MS Office with a set of tools that enables users to transform MS Office documents (e.g., MS Word and MS PowerPoint) into Semantic Documents, and to search local and distant semantic document repositories for document content units (CUs) over Semantic Web protocols.

A Model for Semantic Annotation of Environmental Resources: The TaToo Semantic Framework

During the past years huge amounts of resources in the environmental domain have been published on the internet. To facilitate search and discovery of relevant data among an ever increasing mass, the use of tags has been suggested. Yet, the use of non-formal tags for annotating resources allows simple categorization and search capabilities, but it does not provide the means to create cross-domain annotations. On the other hand, ontologies are a shared and formal conceptualization of a given domain and they can be used to formalise tags. The use of formal semantics for tagging allows taking advantage of the reasoning and inference power of the ontologies to create richer resource annotations enhancing the discovery process. In the environmental domain there is a clear need of frameworks and tools allowing formal tagging and discovery. In this paper we discuss about the definition of a Semantic Framework helping the tagging and discovery process of environmental resources. Moreover, we also report on the definition of a model to describe environmental resources allowing cross-domain annotation and search.

Towards a Semantically Unified Environmental Information Space

In recent years we have witnessed a proliferation of environmental information on the Web thanks to advances in automated data acquisition and to the widespread use of computer based models and decision support systems processing environmental data. The number of environmental data providers has been also increasing. However, each provider manages its own data sets encoded into specific data formats and unaware of related and relevant data managed by other providers. Also, most of the environmental data providers store their data into huge, centralized repositories, which makes the access and discovery of desired data difficult. The Linked Data principles along with the Semantic Web technologies have been recognized as a promising solution to both environmental data integration and discovery. Unique identification of environmental data by HTTP dereferencable URIs, semantic annotation of environmental data by shared domain conceptualizations (ontologies), and interlinking of related environmental data by typed (semantic) links will enable the integration of disconnected environmental data sets into the semantically unified environmental information space. Semantic annotations and semantic links will then enable semantic discovery of environmental data over such unified information space. In this paper, we try to identify a number of requirements that environmental data providers should satisfy in order to make their data fully contribute to this vision. In particular, we are focused on requirements regarding environmental data identification, representation, annotation and linking.

Ontology-based content model for scalable content reuse

The paper presents Abstract Compound Content Model (ACCM), a generic content model which we have devel-oped aiming to facilitate interoperability, repurposing and integration of diverse platform specific content models. Based on this model we have developed the ACCM ontology in order to turn the ACCMs elements (i.e., content units and content aggregations) into resources that can be directly accessed and thus reused. The paper also presents our current work on the implementation of an ACCM-based content management system that enables efficient storage, indexing, search and retrieval of content units as they are defined in the ACCM ontology.

An Ontology-Based Framework for Authoring Assisted by Recommendation

In this paper, we propose the use of Semantic Web technologies to bridge the gap between authoring systems and authors. The core part of our solution is the ontology-based framework that captures the information about the interaction between learning objects (LOs) and four main roles in the educational process (content author, instructional designer, teacher, and learner), and then according to this information proposes the most relevant learning content to the author. The central part of the framework is the Request- Recommendation ontology that formalizes the authors request along with a set of learning content proposals (recommendation), as a response to that request. Furthermore, we propose the use of a weighting scheme to calculate the weight of the content proposals and thus enable their ranking within the recommendation.

Publishing and Linking Semantically Annotated Agro-environmental Resources to LOD with AGROPub

Publishing agro-environmental resources to a linked open data (LOD) cloud requires publishers to adopt a set of universally recognized linked data principles. These principles, along with semantic annotations based on shared domain ontologies can ensure the semantic integration of agro-environmental resources. In this paper we present a resource-publishing system, called AGROPub, that we developed to aid agro-environmental resource providers to annotate, publish and integrate their resources to LOD. The system comprises services and tools that enable resource providers to annotate their resources by relevant concepts from selected agro-environmental domain ontologies, to generate and publish RDF descriptions of the resources to LOD and to link the published resources to related resources from LOD. In addition to the services and tools dedicated to resource providers, AGROPub provides services and tools that enable consumers of the agro-environmental resources to search and annotate published resources by adding their own annotations as well as to evaluate them based on given criteria.

An Architecture for the Semantic Enhancement of Environmental Resources

The vision of a Single Information Space in Europe for the Environment (SISE) requires seamless access to environmental resources, including data, models and services. Standardization organizations like OGC and OASIS have laid the foundations for interoperability on a syntactic level for many aspects of distributed environmental information systems (e.g. OGC SWE for sensor information). At the same time, the EC has undertaken a considerable effort to commit European stakeholders to offering their environmental information in such a way that it is accessible by interested parties, both on the scientific level by supporting research projects, like ORCHESTRA and SANY, and on the legal level by introducing directives (such as the INSPIRE directive). This development, amongst others, has led to the present situation in which a large number of environmental information sources are available. However, to implement the vision of the SISE it is not enough to publish resources. Environmental information must be discoverable, and it must be ‘understandable’ in different contexts in order to be used effectively by parties of various thematic domains. Therefore, in order to foster the implementation of SISE, semantic interoperability is a necessary element. Key to semantic interoperability is the presence of meta-information which describes the concepts of the environmental resources. Producing this meta-information puts a heavy technological burden on the individual resource providers such that it seems unlikely that enough semantic meta-information will ever be made available to reach semantic interoperability and thus accomplish the vision of SISE unless other ways to provide this essential meta-information are found. In this paper we introduce an architecture, developed in the FP7 project TaToo (247893), which tries to overcome the aforementioned obstacles by providing the possibility to easily annotate and rate environmental information resources, even by parties which do not own the resource, and transparently equipping this information with domain knowledge and thus enhancing discoverability and usability of resources with semantic technologies. The objective of the architecture is to seamlessly blend in with existing infrastructures by making use of de facto standards while offering support for discovery, annotation and validation of environmental resources through open interfaces.

Semantic Document Management for Collaborative Learning Object Authoring

In this paper, we propose the use of semantic documents as learning objects. The core part of our solution is a semantic document model that allows for unique identification of document content units (CUs) and their annotation with different types of metadata. On the top this model, we have developed the semantic document management system (SDMS), which enables efficient collaborative authoring of learning objects within a social network of content authors, by reusing document CUs based on the accumulated metadata.

Extending MS Office for Sharing Document Content Units over the Semantic Web

In this paper, we present an extension to MS Office that enables users to search and retrieve document content units (e.g., paragraphs, images, tables, slides, etc.) from documents, which are stored on userpsilas individual desktops organized in a peer-to-peer fashion. We first introduce the semantic document model (SDM) that turns MS Office documents (i.e., MS Word and MS PowerPoint) into semantic Web resources, making document content to be accessible and query-able as RDF data. Then we describe the developed tools, which extend Office applications with support for ontology-based, distributed search of semantic documents stored in local RDF repositories over se-mantic Web protocols.