Genius Media Group Incorporated is a collaborative annotation platform and was founded by Mahbod Moghadam, Tom Lehman and Ilan Zechory with the aim to annotate lyrics, which have no license and can be interpreted in form of in- line annotation by members. The first version was launched October 2009 as Rap Exegesis, then it changed to Rap Genius in December 2009 and finally in July 2014 the title Genius was given. Genius members have six different roles that are closely tied to authorizations sequentially: Whitehat, Editor, Moderator, Verified Artist, Mediator and Staff. We monitored Genius activities on firehose for five weeks, collected 1.3 million activities, 762 thousand of them are annotation activities1. We registered 57 thousand unique users and found, that users generate on average 13.33 annotation activities in this period of analysis, which is 0.36 annotation activities per day. The distribution over user groups displays the roles Moderator, Staff, Artist, Mediator and Whitehat. Whitehat embodies the most registered user, but when it comes to drive Genius ahead, then those roles are presented in the following sequence: Artist, Staff, Mediator, Moderator, Editor and at the end is the role Whitehat. Intelligence Quotient (IQ) can be earned by the most of activities and indicate experience of a member. Although a count of IQs is required to do certain activities, for instance to post into forums, but it does not promote automatically a member’s role to become a higher member level. High-quality annotations and decision maker such as an Editor establish nomination criteria. Earning IQs implies to edit pages; a page is edited on average 295 times, which varies greatly from the me- dian (195) times. This indicates that some pages attract users more than others. For developers Genius provides API, documentation and support forum as well as there are sub- domains in different countries and languages. We attempt to discover members' collaboration by editing Genius pages and for this purpose we clarify the social, technical and participation architecture of Genius, such as member’s permissions as well as options, activity types and distribution of page edits. The following technical report is structured as follows: Section 2 introduces the social structure of Genius, how to interact with the user interface, being a member and the relation between member roles, annotation and earned IQs. Section 3 continues with the technical structure, in which Genius subdomains are presented, what technical options are there for developers to bind Genius services in applications, firehose as notifications process as well as demographic trends of users at Genius. Section 4 describes our member activities study on Genius and which new findings we determined. Finally, section 5 presents our conclusions.

Crowd ideation platforms provide a promising approach for supporting the idea generation process. Research has shown that presenting a set of similar or diverse ideas to users during ideation leads them to come-up with more creative ideas. In this paper, we describe Innovonto, a crowd ideation platform that leverages semantic web technologies and human collaboration to identify similar and diverse ideas in order to enhance the creativity of generated ideas. Therefore, the approach implemented captures first the conceptualization of users' ideas. Then, a matching system is employed to compute similarities between all ideas in near real time. Furthermore, this technical report outlines the results obtained from the evaluation of Innovonto platform. The hallway study, conducted at our research group, allowed us to test each step of Innovonto platform as well as the proposed approach in assessing similarities between ideas. As results, we received 20 ideas and 23 feedbacks from 9 users. The analysis of the results shows good performance of Innovonto steps and confirms the findings of existing research.

A primary goal of a research infrastructure for data management should be to enable efficient data discovery and integration of heterogeneous data. The German Federation for Biological Data (GFBio) was envisioned by this goal. The basic component, that enables such interoperability and serves as a backbone for such a platform, is the GFBio Terminology Service (GFBio TS). It acts as a semantic platform for accessing, developing and reasoning over terminological resources within the biological and environmental domain. A RESTful API gives access to these terminological resources in a uniform way regardless of their degree of complexity and whether they are internally stored or externally accessed through their web services. Additionally, a set of widgets with an intrinsic API connection are made available for an easy integration in applications and web interfaces. Based on the requirements of the GFBio partners, we describe the added value that is provided by the GFBio Terminology Service with practical scenarios but also, what challenges we still face. We conclude by describing our current activities and future developments.

Nowadays, a wide range of information sources are available due to the evolution of web and collection of data. Plenty of these information are consumable and usable by humans but not understandable and processable by machines. Some data may be directly accessible in web pages or via data feeds, but most of the meaningful existing data is hidden within deep web databases and enterprise information systems. Besides the inability to access a wide range of data, manual processing by humans is effortful, error-prone and not contemporary any more. Semantic web technologies deliver capabilities for machine-readable, exchangeable content and metadata for automatic processing of content. The enrichment of heterogeneous data with background knowledge described in ontologies induces re-usability and supports automatic processing of data. The establishment of “Corporate Smart Content” (CSC) - semantically enriched data with high information content with sufficient benefits in economic areas - is the main focus of this study. We describe three actual research areas in the field of CSC concerning scenarios and datasets applicable for corporate applications, algorithms and research. Aspect- oriented Ontology Development advances modular ontology development and partial reuse of existing ontological knowledge. Complex Entity Recognition enhances traditional entity recognition techniques to recognize clusters of related textual information about entities. Semantic Pattern Mining combines semantic web technologies with pattern learning to mine for complex models by attaching background knowledge. This study introduces the afore-mentioned topics by analyzing applicable scenarios with economic and industrial focus, as well as research emphasis. Furthermore, a collection of existing datasets for the given areas of interest is presented and evaluated. The target audience includes researchers and developers of CSC technologies - people interested in semantic web features, ontology development, automation, extracting and mining valuable information in corporate environments. The aim of this study is to provide a comprehensive and broad overview over the three topics, give assistance for decision making in interesting scenarios and choosing practical datasets for evaluating custom problem statements. Detailed descriptions about attributes and metadata of the datasets should serve as starting point for individual ideas and approaches.

In this report, we present our research results for the fourth half-year phase of the project Corporate Smart Content under the working package "Knowledge- based Mining of Complex Event Patterns". We present SpaceROAM, our new approach to Role Discovery that focuses on the domain of Collaborative Content Management Systems. The method tries to magnify users' characteristic features by positioning them among other one of the same user as well as among features of other users. Applied on real-life data, the method was able to recognize all typical roles and to detect new ones that differ from the known ones significantly. Finally, we provide a description of other research tasks that we started and whose results will be included in our next report.

In this report, we present our research results during the fourth half-year- phase of the project Corporate Smart Content under the working package ”Knowledge-based Mining of Complex Event Patterns”. We present here a novel approach for real-time extraction of news, based on user specifications and by using background knowledge from specific news domains. We create a powerful filtering service which limits the news data to the concrete and essential preferences of a user. In our approach, enrichment of real- time news with background knowledge is a preprocessing step. We use a Complex Event Processor to detect complex events from the enriched articles and match them to the user specified query. Each time a news article is matched, its result is notified to the user immediately. Our experimental evaluation shows that our approach is feasible for detecting news in real- time with high precision and recall.

Background: Data from software data repositories such as source code version archives and defect databases contains valuable information that can be used for insights (leading to subsequent improvements), in particular defect insertion circumstance analysis and defect prediction. The first step in such analyses is identifying defect-correcting changes in the version archive (bugfix commits) and linking them to corresponding entries in the defect database, thus establishing bugfix links, in order to enrich the content of the defect-correcting change with additional meta-data. Typically, identifying the bugfix commits in a version archive is done via heuristic string matching on the commit message. Research questions: Which filters could be used to obtain a set of bugfix links? How does one set the cutoff parameters of each? What effect (results loss and precision) does each filter then have? Which overall precision, results loss, and recall is achieved? Method: We analyze a comprehensive modular set of seven independent filters, including new ones that make use of reverse links. We describe and evaluate visual heuristics (based on simple diagnostic plots) for setting six filters' cutoff parameter. We apply these to a commercial repository from the Web CMS domain and validate the results with unprecendented precision by making use of a product expert to manually verify over 2500 links. Results: The parameter selection heuristics pick a very good parameter value in five of the six cases and a reasonably good one in the sixth. As a result, the combined filtering, called bflinks, proposes a set of bugfix links that has 93\% precision with only 7\% results loss. Conclusion: The modular filtering approach can provide high-quality results and can be adapted to repositories with different properties.

In this technical report, we present the results of the first milestone phase of the Corporate Smart Content sub-project "Smart Content Enrichment". We present analyses of the state of the art in the fields concerning the three working packages defined in the sub-project, which are aspect-oriented ontology development, complex entity recognition, and semantic event pattern mining. We compare the research approaches related to our three research subjects and outline briefly our future work plan.

Mobile robotics is a rapidly growing field and has countless applications including exploration, logistics, rescue operations, as well as domestic and military use. One particularly interesting example of ist use is the construction of autonomous, "self-driving" cars. Imagine that car accidents caused by human error are a thing of the past, or that your car can find ist own parking spot after you have left the vehicle. In many cases, mobile robots need to plan and make decisions autonomously while interacting with their environment. A necessary prerequisite for them to execute most non-trivial tasks is to have a concept of their environment and their location in it. Determining this location is a fundamental problem in mobile robotics known as localization. Autonomous cars need to know where they are on the road, both on a small scale to stay in lane and on a large scale to navigate.

We do digital research on the artist Mark Lombardi (1951-2000) as an experiment in methods for digital art history. We manually generate GraphML representations of the networks depicted in his drawings and publish them at http://www.lombardinetworks.net. Services on the data are implemented like textual search on labels of nodes or an index on what persons or institutions appear in which works. We visualize the networks with nodes linked to Wikipedia information about the actors. With calculations on the networks we generate synthetic drawings from multiple original works that overlap in actors.