Abstract. AceWiki is a prototype that shows how a semantic wiki using controlled natural language — Attempto Controlled English (ACE) in our case — can make ontology management easy for everybody. Sentences in ACE can automatically be translated into first-order logic, OWL, or SWRL. AceWiki integrates the OWL reasoner Pellet and ensures that the ontology is always consistent. Previous results have shown that people with no background in logic are able to add formal knowledge to AceWiki without being instructed or trained in advance. 1

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.

One way to solve the knowledge acquisition bottleneck is to have ways to translate natural language sentences and discourses to a formal knowledge representation language, especially ones that are appropriate to express domain knowledge in sciences, such as Biology. While there have been several proposals, including by Montague (1970), to give model theoretic semantics for natural language and to translate natural language sentences and discourses to classical logic, none of these approaches use knowledge representation languages that can express domain knowledge involving normative statements and exceptions. In this paper we take a first step to illustrate how one can automatically translate natural language sentences about normative statements and exceptions to representations in the knowledge representation language Answer Set Programming (ASP). To do this, we use λ-calculus representation of words and their composition as dictated by a CCG grammar.

Abstract Policy rules are often written in organizations by a team of people in different roles and technical backgrounds. While user-generated content and community-driven ontologies become common practices in the semantic environments, machine-processable user-generated policies have been underexplored, and tool support for such policy acquisition is practically non-existent. We defined the concept and developed a tool for policy acquisition from the end users, grounded on the Semantic technologies. We describe a policy management environment (PME) for the Semantic Web and show its added value compared to existing policy-related developments. In particular, we detail a part of the PME, the policy acquisition tool that enables non-expert users to create and modify semantic policy rules. An empirical study has been conducted with 10 users, who were new to the semantic policy acquisition concept and the developed tool. The main task for the users was to model policies of two different scenarios using previously unknown to them ontologies. Overall, the users modeled successfully policies employing the tool, with minor deviations between their performance and feedback. Observation-based, quantitative and qualitative feedback on the concept and the implementation of the end-user policy acquisition tool is presented. 1

Abstract. A couple of semantic wikis have been proposed to serve as collaborative knowledge engineering environments – however, the knowledge of almost all systems is currently based on the expressiveness of OWL (Lite/DL). In this paper we present the concept of a semantic knowledge wiki that extends the capabilities of semantic wikis by strong problem-solving methods. We show how problem-solving knowledge is connected with standard ontological knowledge using an upper ontology. Furthermore, we discuss different possibilities to formalize problemsolving knowledge, for example by semantic knowledge annotation, structured text and explicit markups. 1