Recently, Semantic Wikis showed reasonable success as collaboration platforms in the context of social semantic applications. In this paper, we present a novel approach, that interprets the concept of Semantic Wikis as a knowledge engineering environment, that effectively help to build decision-support systems. We introduce the Semantic Wiki KnowWE, that provides the possibility to define and maintain ontologies together with strong problem-solving knowledge. Thus, the wiki can be used to collaboratively build decision-support systems. These enhancements require extensions of the standard Semantic Wiki architecture by a task ontology for problem-solving and an adapted reasoning process. We discuss these extensions in detail, and we describe a case study in the field of medical emergency systems.

/pdf/knowwe-a-semantic-wiki-for-knowledge-engineering-5fnja64bxa.pdf

KnowWE: a Semantic Wiki for knowledge engineering

I here give an overview of Wikipedia and wiki research and tools. Well over 1,000 reports have been published in the eld and there exist dedicated scientic meetings for Wikipedia research. It is not possible to give a complete review of all material published. This overview serves to describe some key areas of research. i

https://papers.ssrn.com/sol3/Delivery.cfm/SSRN_ID2129874_code1466802.pdf?abstractid=2129874&mirid=5

Wikipedia research and tools: Review and comments

Perspectives on Ontology Learning brings together researchers and practitioners from different communities − natural language processing, machine learning, and the semantic web − in order to give an interdisciplinary overview of recent advances in ontology learning. Starting with a comprehensive introduction to the theoretical foundations of ontology learning methods, the edited volume presents the state-of-the-tart in automated knowledge acquisition and maintenance. It outlines future challenges in this area with a special focus on technologies suitable for pushing the boundaries beyond the creation of simple taxonomical structures, as well as on problems specifically related to knowledge modeling and representation using the Web Ontology Language. Perspectives on Ontology Learning is designed for researchers in the field of semantic technologies and developers of knowledge-based applications. It covers various aspects of ontology learning including ontology quality, user interaction, scalability, knowledge acquisition from heterogeneous sources, as well as the integration with ontology engineering methodologies.

Perspectives on Ontology Learning

In this paper, we introduce interlinking multimedia (iM), a pragmatic way to apply the linked data principles to fragments of multimedia items. We report on use cases showing the need for retrieving and describing multimedia fragments. We then introduce the principles for interlinking multimedia in the Web of Data, discussing potential solutions which sometimes highlight controversial debates regarding what the various representations of a Web resource span. We finally present methods for enabling a widespread use of interlinking multimedia.

/pdf/interlinking-multimedia-how-to-apply-linked-data-principles-1hxxyz7ldb.pdf

Interlinking Multimedia How to Apply Linked Data Principles to Multimedia Fragments

As management of architectural knowledge becomes vital for improving an organization’s architectural capabilities, support for (semi-) automating this management is required. There exist already several tools that specialize in architecture knowledge management, as well as generic technologies that can potentially be used for this purpose. Both tools and technologies cover a wide number of potential use cases for architecture knowledge management. In this chapter, we survey the existing tool support and related technologies for different architecture knowledge management strategies, and present them according to the use cases they offer.

/pdf/tools-and-technologies-for-architecture-knowledge-management-4f45jtgchp.pdf

Tools and technologies for architecture knowledge management

Lean knowledge management is today implemented mostly through wikis, which let users enter text and other data, such as files, and connect the content through hyperlinks. Easy setup and a huge variety of editing support are primary reasons for wiki use in all types of intranet- and Internet-based information sharing (see P. Louridas, "Using Wikis in Software Development," IEEE Software, Mar. 2006, pp. 88- 91). The drawbacks show up when you need to structure data as opposed to just edit text. Many wikis have tons of useful content, but the volume and lack of structure make it inaccessible over time. This is where semantic wikis enter the picture. Sebastian Schaffert and his colleagues describe them here and explain how to model wiki knowledge and content for improved usability. I look forward to hearing from both readers and prospective authors about this column and the technologies you want to know more about.

https://ieeexplore.ieee.org/iel5/52/4548393/04548399.pdf

Semantic Wikis

Das Thema ,,Semantic Wikis“ als Verknupfung von Wiki-Konzepten mit semantischen Technologien gewann in den vergangenen Jahren zunehmend an Bedeutung, weil es ,,soziale Intelligenz“ und ,,kunstliche Intelligenz“ miteinander verknupft. Ziel des Artikels ist es, einen Uberblick uber das Thema Semantic Wikis zu schaffen. Wir gehen dabei insbesondere auch auf Anwendungsfelder ein und geben einen kurzen Vergleich ausgewahlter Systeme.

Semantic Wiki

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