DeepaMehta

A crucial experience during my time at university— computer science (with focus on AI) and linguistics—was the documentary “Maschinenträume” (1988) by Peter Krieg. It features the long-term AI project “Cyc,” in which Doug Lenat and his team try to represent common sense knowledge in a computer. When Cyc started, in 1984, it was already known that many AI projects failed due to the machine’s lack of common sense knowledge. Common sense knowledge includes, for example, that two things cannot be in the same place at the same time, or that people die, or what happens at a children’s birthday party. During the night, while the researchers are sleeping, Cyc tries to create new knowledge from its programmed facts and rules. One morning the researchers were surprised by one of Cyc’s new findings: “Most people are famous.” Well, this was simply a result of the researchers having entered, besides themselves, only celebrities like, for example, Einstein, Gandhi, and the U.S. presidents. The machine-dreaming researchers, however, were in no way despondent about this obviously wrong finding, because they figured they would only have to enter the rest of the population, too. The underlying principle behind this thought is that it is possible to model the whole world in the form of ontologies. The meaning of the world can be captured in its entirety in the computer. From that moment the computer can know everything that humans know and can produce unlimited new insights. At the end of the film Peter Krieg nevertheless asks: “If one day the knowledge of the whole world is represented in a machine, what can humans do with it, the machine having never seen the world.”

CoolWikNews

This chapter deals with a semantic wiki application devoted to news publishing, Cool- WikNews. This semantic application offers the functionalities of a traditional wiki, but enhanced with semantic data. It focuses on the simplicity of both use and browsing, the accurate retrieval of information, and its flexibility to be applied in any domain apart from news publishing. In this chapter, definitions related with this topic will be explained, apart from describing the steps taken so far and the problems still to overcome. Inwards and advantages of CoolWikNews will be presented, paying more attention in how this application overcomes the problems arisen. The chapter concludes with future work and several remarks.

SWiM

In this chapter, we present the SWiM system, a prototype semantic wiki for collaboratively building, editing, and browsing mathematical knowledge. SWiM is based on the semantic wiki IkeWiki, but replaces the wiki text with OMDoc, a markup format and ontology language for mathematical documents as the underlying knowledge representation format. Our long-term objective is to evolve SWiM into an integrated platform for ontology-based added-value services. As a social semantic work environment, it will facilitate the creation of a shared, public collection of mathematical knowledge (e.g., for education) and serve scientists as a tool for collaborative development of new theories. We discuss the architecture of the SWiM system focusing on its conceptual base, the OMDoc system ontology. In contrast to other semantic wikis, SWiM uses the system ontology to operationalize the fragments and relations of the underlying representation format, not only the domain ontology, that is, the relations between the represented objects themselves. We will present the prototype implementation of the SWiM system and propose its further evolution into a service platform for science and technology.

A Semi-Automatic Semantic Annotation and Authoring Tool for a Library Help Desk Service

This chapter discusses how knowledge technologies can be utilized in creating help desk services on the Semantic Web. To ease the content indexer’s work, we propose semi-automatic semantic annotation of natural language text for annotating question-answer (QA) pairs, and case-based reasoning techniques for finding similar questions. To provide answers matching the content indexer’s and end-user’s information needs, methods for combining case-based reasoning with semantic search, linking, and authoring are proposed. We integrate different data sources by using large ontologies. Techniques to utilize these sources in authoring answers are suggested. A prototype implementation of a real life ontology-based help desk application, based on an existing national library help desk service in Finland, is presented as a proof of concept.

Improved Experience Transfer by Semantic Work Support

Active Knowledge Support for Integrated Operations (AKSIO) is a work process-enabled knowledge management system that supports experience transfer in operations of offshore oilfields. The system provides timely and contextual knowledge for work processes. Experience reports are processed and annotated by experts and linked to various resources and specialist knowledge networks. The system allows retrieval of experience reports through the support of a domain ontology. Core functionality of the system is provided by careful application of Semantic Web technology, including ontology-based annotation and contextual ontology-driven retrieval of content. The purpose of this chapter is to demonstrate how Semantic Web technology is an effective enabler of improved knowledge management processes in corporate environments.

Communication Systems for Semantic Work Environments

The paradigm of a flexible environment that supports the user in producing, organizing, and browsing the knowledge originates in the early 1940s, a long time before the first personal computers and new communication tools like the Internet became available. The conceptual design of Vannevar Bush’s memex (Bush, 1945) (an acronym for Memory Extender) is probably the most cited (e.g., Gemmell, Bell, Leuder, Drucker, & Wong, 2002) and criticized (e.g., Buckland, 1992) representative of such early conceptual work. In his article, Bush described the integrated work environment that was electronically linked to a repository of microfilms and able to display stored contents and automatically follow references from one document to another. A number of visionary ideas from this early conceptual work can be recognized in state-of-the-art information systems (cross-references between documents, browsing, keyword-based annotation of documents using the personal “codebook,” automatic generation of associative trails for content summarization, etc.).

Automatic Acquisition of Semantics from Text for Semantic Work Environments

This chapter presents an overview of techniques for semi-automatic extraction of semantics from text, to be integrated in a Semantic Work Environment. A focus is placed on the disambiguation of polysemous words, the automatic identification of entities of interest, and the annotation of relationships between them. The application of these topics falls into the intersection of three dynamic fields: the Semantic Web, Wiki and other work environments, and Information Extraction from text.

Added-Value

In this chapter we will look at users’ taking action processes in Semantic Work Environments. We argue that the underlying motivational problem between vast semantic potential and extra personal investment can be considered as a “Semantic Prisoner’s Dilemma” that builds on two competing value perspectives: The micro and macroperspective. The former informs a user’s decision for action, whereas the latter informs a designer’s decision for offering services. An in-depth analysis of the term “Added- Value” reveals its double relativity, which allows a sophisticated evaluation of such services from a microperspective. We use this property of double relativity for suggesting the “Added-Value Analysis” as a design method for getting people into Semantic Work Environments—showcasing its strength with a description of CPoint and ConneXions.

Personal Knowledge Management with Semantic Technologies

Managing and enabling knowledge is a key to success in our economy and society (Wenger, McDermott, & Snyder, 2002, p. 6). The problem of knowledge management can generally be tackled from two sides: top-down and bottom-up. Many approaches have been taken from the top down in which the organisation aimed to better manage their internal knowledge by installing central knowledge repositories. Many of these systems were less accepted than expected (Braganza & Mollenkramer, 2002). Along with the Web 2.0 notions of user-provided content and collective intelligence, more bottom-up approaches to knowledge management were developed. In this chapter we describe an individual-centric, bottom-up approach to personal knowledge management (PKM). PKM is the individual management of knowledge from a subjective perspective.

A Wiki on the Semantic Web

The wiki concept is more than 10 years old but has attained public success only recently, thanks to Wikipedia. However, in the intranet world, several studies have shown that the usage of wikis is subject to debate. Acceptance of such open, low-structured collaborative tools is not the rule. There are different reasons for explaining such low acceptance: social reasons (corporate culture may not be adapted) but also usability reasons (the wiki is not structured enough, it is hard to navigate and find relevant information, the wiki markup language used by most wiki engine makes people reluctant to contribute to the wiki, etc.). In this chapter we present SweetWiki, a new wiki engine that relies on Semantic Web technologies and addresses most usability problems that have been reported in Buffa and Gandon (2006), Chat and Nahaboo (2006), and Powers, (2005). SweetWiki is an example of an application reconciling two trends of the future Web: a semantically-augmented Web and a Web of social applications where every user is an active actor and provider. SweetWiki makes heavy use of Semantic Web concepts and languages and demonstrates how the use of such paradigms can improve navigation, search, and usability. By semantically annotating the resources of the wiki and by reifying the wiki object model itself, SweetWiki provides reasoning and querying capabilities. All the models are defined in OWL schemata capturing concepts of the wikis (wiki word, wiki page, forward and backward link, author, etc.) and concepts manipulated by the users (users’ folksonomy, external ontologies). These ontologies are exploited by an embedded semantic search engine (Corese) allowing us to support and ease the lifecycle of the wiki (e.g., restructuring pages), to propose new functionalities (e.g., semantic search, profile-based monitoring) and to allow for extensions (e.g., support new medias in pages, integrate legacy software). In SweetWiki we have paid special attention to preserve the essence of a wiki: simplicity and social dimension. Thus SweetWiki supports all the common wiki features such as easy page linking using WikiWords, versioning, and so forth, but also innovates, integrating a WYSIWYG editor extended to support social tagging functionalities, embedded SPARQL queries, and so forth, masking the OWL-based annotation implementation. Users can freely enter tags and an auto-completion mechanism suggests existing ones by issuing queries to identify existing concepts with compatible labels. Thus tagging is both easy and motivating (real time display of the number of related pages) and concepts are collected in folksonomies. Wiki pages are served directly in XHTML or in JSPX format, embedding semantic annotations ready to be reused by other Semantic Web software.