Distributed Human Computation (DHC) holds great promise for using computers and humans together to scaling up the kinds of tasks that only humans do well. Currently, the literature describing DHC efforts so far is segmented. Projects that stem from different perspectives frequently do not cite each other. This can be especially problematic for researchers trying to understand the current body of work in order to push forward with new ideas. Also, as DHC matures into a standard topic within humancomputer interaction and computer science, educators will require a common vocabulary to teach from. As a starting point, we offer a taxonomy which classifies and compares DHC systems and ideas. We describe the key characteristics and compare and contrast the differing approaches.

Abstract. Most of the challenges faced when building the Semantic Web require a substantial amount of human labor and intelligence. Despite significant advancement in ontology learning and human language technology, the tasks of ontology construction, semantic annotation, and establishing alignments between multiple ontologies remain highly dependent on human intelligence. This means that individuals need to contribute time and sometimes other resources. Unfortunately, we observe a serious lack of user involvement in the aforementioned tasks, which may be due to the absence of motivations for people who contribute. As a novel solution, we (1) propose to masquerade the core tasks of weaving the Semantic Web behind online, multi-player game scenarios, in order to create proper incentives for human users to get involved. Doing so, we adopt the findings from the already famous “games with a purpose ” by von Ahn, who has shown that presenting a useful task, which requires human intelligence, in the form of an online game can motivate a large amount of people to work heavily on this task, and this for free. Then, we (2) describe our generic OntoGame platform, and (3) several gaming scenarios for various tasks plus our respective prototypes. Based on the analysis of user data and interviews with players, we provide preliminary evidence that users (4) enjoy the games and are willing to dedicate their time to those games, (5) are able to produce high-quality conceptual choices. Eventually we show how users entertaining themselves by online games can unknowingly help weave and maintain the Semantic Web. 1.

Abstract. Collective Intelligence takes advantage of collaboration, competition and integration. It often uses mixed groups of humans and computers to research in new unexplored ways. Ontologies, which are the main building block of the Semantic Web, are usually prepared by domain experts. We introduce a novel approach, which employs Collective Intelligence, towards building simple domain ontologies through a game called OntoPair, an entertaining web-based game that is able to build simple OWL-based ontologies based on collected information from players. The game collects properties and common-sense facts regarding an object by means of some fixed templates and translates them into OWL representation by aid of a mediator/mapper and builds simple domain ontologies after refinement in several iterations. We define the game and preform a small experiment that proves our idea. 1

A pre-requisite for the Semantic Web to become a reality is the availability of ontologies [1] and meta-data. In many cases, it might be necessary to align between different ontologies in order to ensure interoperability. The research in the area of semantic content authoring has brought up an inventory of mature techniques and tools for semantic content creation. However, there is a severe lack of semantic data available on the Web: one can only find few well-maintained ontologies, respective alignments and very little semantic annotation. For instance, a search on Watson 1 or Swoogle 2 for a tourism ontology does not deliver a proper tourism ontology even though travel and tourism ontologies have been created in many academic projects in the last couple years. Furthermore, one can observe very little involvement of Web users in the process of semantic content creation. However, this involvement is urgently needed: there are tasks that are trivial for a human user but still difficult for a computer [2, 3]. Conceptual modeling and semantic annotation are tasks that depend on human intelligence: even though approaches for automating these activities exist, the problem has not been solved completely yet and human input is required at some stage. Therefore, we are now confronted with the situation that even though the technology

Abstract: The concept of co-production was originally introduced by political science to explain citizen participation in the provision of public goods. The concept was quickly adopted in business research targeting the question how users could be voluntarily integrated into industrial production settings to improve the development of goods and services on an honorary basis. With the emergence of Social Software and web-based collaborative infrastructures the concept of co-production gains importance as a theoretical framework for the collaborative production of web content and services. Current research in human computation has adopted the concept for the semantic enrichment of web content by collaborative tagging. This article argues that co-production is a powerful concept, which helps to explain the emergence of user generated content and the partial transformation of orthodox business models in the content industries.

{dustin, lieber, kcarnold}

Understanding other people’s goals is an essential part of interpersonal interactions. This capability enables a person to naturally predict another person’s future actions in a situation and produce appropriate joint or shared actions. In like manner, a human-like planning agent (or sociable robot) should be able to understand the user’s action goal and come up with subgoal-based plans to achieve the goal. In this paper we focus on how the agent can automatically construct the subgoalbased action hierarchy corresponding to the user’s high-level goal. As a first step, we implement an actionplanning engine based on ConceptNet, and indicate the drawbacks of using ConceptNet for this purpose. Also, we present the structure of a new goal-oriented commonsense-reasoning knowledgebase for the agent’s action-goal representation and action planning.

Access to knowledge about common human goals has been found critical for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about common human goals represents a major challenge. In a departure from existing approaches, this paper investigates a novel resource for knowledge acquisition: The utilization of search query logs for this task. By relating goals contained in search query logs with goals contained in existing commonsense knowledge bases such as ConceptNet, we aim to shed light on the usefulness of search query logs for capturing knowledge about common human goals. The main contribution of this paper consists of insights generated from an empirical study comparing common human goals contained in two large search query logs (AOL and Microsoft Research) with goals contained in the commonsense knowledge base ConceptNet. The paper sketches ways how goals from search query logs could be used to address the goal acquisition and goal coverage problem related to commonsense knowledge bases.

Knowledge about human goals has been found to be an important kind of knowledge for a range of challenging problems, such as goal recognition from peoples ’ actions or reasoning about human goals. Necessary steps towards conducting such complex tasks involve (i) acquiring a broad range of human goals and (ii) making them accessible by structuring and storing them in a knowledge base. In this work, we focus on extracting goal knowledge from weblogs, a largely untapped resource that can be expected to contain a broad variety of human goals. We annotate a small sample of weblogs and devise a set of simple lexico-syntactic patterns that indicate the presence

processing from an interdisciplinary perspective. Constraints are widely used in linguistics, computer science, and psychology. How they are used, however, varies widely according to the research domain: natural language processing, knowledge representation, cognitive modelling, problem solving mechanisms, etc. These different perspectives are complementary, each one adding a piece to the puzzle. For example, linguistics proposes in-depth descriptions implementing constraints in order to filter out structures by means of description languages, constraint ranking, etc. The constraint programming paradigm, on the other hand, shows that constraints have to be taken as a systematic whole and can thus play a role in building the structures (or can even replace structures). Finally, psycholinguistics experiment have been made, investigating the role of constraint systems for cognitive processes in comprehension and production, as well as addressing how they can be acquired. The years ’ collocation with the CONTEXT’11 conference underlines the application of constraints for context comprehension and discourse modelling. Previous CSLP workshops were held in 2008 (with ESSLLI, in Hamburg,