AceWiki: A Natural and Expressive Semantic Wiki

We present AceWiki, a prototype of a new kind of semantic wiki using the controlled natural language Attempto Controlled English (ACE) for representing its content. ACE is a subset of English with a restricted grammar and a formal semantics. The use of ACE has two important advantages over existing semantic wikis. First, we can improve the usability and achieve a shallow learning curve. Second, ACE is more expressive than the formal languages of existing semantic wikis. Our evaluation shows that people who are not familiar with the formal foundations of the Semantic Web are able to deal with AceWiki after a very short learning phase and without the help of an expert.Comment: To be published as: Proceedings of Semantic Web User Interaction at CHI 2008: Exploring HCI Challenges, CEUR Workshop Proceeding

Collaborative Authoring of Adaptive Educational Hypermedia by Enriching a Semantic Wiki’s Output

This research is concerned with harnessing collaborative approaches for the authoring of Adaptive Educational Hypermedia (AEH) systems. It involves the enhancement of Semantic Wikis with pedagogy aware features to this end. There are many challenges in understanding how communities of interest can efficiently collaborate for learning content authoring, in introducing pedagogy to the developed knowledge models and in specifying user models for efficient delivery of AEH systems. The contribution of this work will be the development of a model of collaborative authoring which includes domain specification, content elicitation, and definition of pedagogic approach. The proposed model will be implemented in a prototype AEH authoring system that will be tested and evaluated in a formal education context

How Controlled English can Improve Semantic Wikis

The motivation of semantic wikis is to make acquisition, maintenance, and mining of formal knowledge simpler, faster, and more flexible. However, most existing semantic wikis have a very technical interface and are restricted to a relatively low level of expressivity. In this paper, we explain how AceWiki uses controlled English - concretely Attempto Controlled English (ACE) - to provide a natural and intuitive interface while supporting a high degree of expressivity. We introduce recent improvements of the AceWiki system and user studies that indicate that AceWiki is usable and useful

TiFi: Taxonomy Induction for Fictional Domains [Extended version]

Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin

Automatic detection of accommodation steps as an indicator of knowledge maturing

Jointly working on shared digital artifacts – such as wikis – is a well-tried method of developing knowledge collectively within a group or organization. Our assumption is that such knowledge maturing is an accommodation process that can be measured by taking the writing process itself into account. This paper describes the development of a tool that detects accommodation automatically with the help of machine learning algorithms. We applied a software framework for task detection to the automatic identification of accommodation processes within a wiki. To set up the learning algorithms and test its performance, we conducted an empirical study, in which participants had to contribute to a wiki and, at the same time, identify their own tasks. Two domain experts evaluated the participants’ micro-tasks with regard to accommodation. We then applied an ontology-based task detection approach that identified accommodation with a rate of 79.12%. The potential use of our tool for measuring knowledge maturing online is discussed

BlogForever D2.6: Data Extraction Methodology

This report outlines an inquiry into the area of web data extraction, conducted within the context of blog preservation. The report reviews theoretical advances and practical developments for implementing data extraction. The inquiry is extended through an experiment that demonstrates the effectiveness and feasibility of implementing some of the suggested approaches. More specifically, the report discusses an approach based on unsupervised machine learning that employs the RSS feeds and HTML representations of blogs. It outlines the possibilities of extracting semantics available in blogs and demonstrates the benefits of exploiting available standards such as microformats and microdata. The report proceeds to propose a methodology for extracting and processing blog data to further inform the design and development of the BlogForever platform

Collaborative editing of knowledge resources for cross-lingual text mining

The need to smoothly deal with textual documents expressed in different languages is increasingly becoming a relevant issue in modern text mining environments. Recently the research on this field has been considerably fostered by the necessity for Web users to easily search and browse the growing amount of heterogeneous multilingual contents available on-line as well as by the related spread of the Semantic Web. A common approach to cross-lingual text mining relies on the exploitation of sets of properly structured multilingual knowledge resources. The involvement of huge communities of users spread over different locations represents a valuable aid to create, enrich, and refine these knowledge resources. Collaborative editing Web environments are usually exploited to this purpose. This thesis analyzes the features of several knowledge editing tools, both semantic wikis and ontology editors, and discusses the main challenges related to the design and development of this kind of tools. Subsequently, it presents the design, implementation, and evaluation of the Wikyoto Knowledge Editor, called also Wikyoto. Wikyoto is the collaborative editing Web environment that enables Web users lacking any knowledge engineering background to edit the multilingual network of knowledge resources exploited by KYOTO, a cross-lingual text mining system developed in the context of the KYOTO European Project. To experiment real benefits from social editing of knowledge resources, it is important to provide common Web users with simplified and intuitive interfaces and interaction patterns. Users need to be motivated and properly driven so as to supply information useful for cross-lingual text mining. In addition, the management and coordination of their concurrent editing actions involve relevant technical issues. In the design of Wikyoto, all these requirements have been considered together with the structure and the set of knowledge resources exploited by KYOTO. Wikyoto aims at enabling common Web users to formalize cross-lingual knowledge by exploiting simplified language-driven interactions. At the same time, Wikyoto generates the set of complex knowledge structures needed by computers to mine information from textual contents. The learning curve of Wikyoto has been kept as shallow as possible by hiding the complexity of the knowledge structures to the users. This goal has been pursued by both enhancing the simplicity and interactivity of knowledge editing patterns and by using natural language interviews to carry out the most complex knowledge editing tasks. In this context, TMEKO, a methodology useful to support users to easily formalize cross-lingual information by natural language interviews has been defined. The collaborative creation of knowledge resources has been evaluated in Wikyoto