The Second GO is publicly available through the GO Annotation Toolbox (GOAT.no): http://www.goat.no.

As the amount of information in the world is steadily increasing, there is a growing demand for tools for analysing the information. In this paper we investigate the problem of da~a mining, that is, constructing decision rules from n set of primitive input data. The main contention of the present work is that there is a need to be able to reason also in presence of inconsistencies, and that more general, possibly unsafe rules should be made available through the data mining process. Such rules are typically simpler in structure, and allow the user to reason in absence of information. A framework is suggested for the generation of propositional de.fa=it rules that reflect normal intradependencies in the dat~.

Online communities can be an attractive source of ideas for product and process innovations.However, innovative user-contributed ideas may be few. From a perspective of harnessing "big data" for inbound open innovation, the detection of good ideas in online communities is a problem of detecting rare events. Recent advances in text analytics and machine learning have made it possible to screen vast amounts of online information and automatically detect user-contributed ideas. However, it is still uncertain whether the ideas identified by such systems will also be regarded as sufficiently novel, feasible and valuable by firms who might decide to develop them further. A validation study is reported in which 200 posts from an online home brewing community were extracted by an automatic idea detection system. Two professionals from a brewing company evaluated the posts in terms of idea content, idea novelty, idea feasibility and idea value.The results suggest that the automatic idea detection system is sufficiently valid to be deployed for the harvesting and initial screening of ideas, and that the profile of the identified ideas (in terms of novelty, feasibility and value) follows the same pattern identified in studies of user ideation in general. | INTRODUCTIONBig data has been predicted to revolutionize innovation and how firms will create value for themselves, their customers and society (see, e.g., McAfee & Brynjolfsson, 2012). Artificial intelligence systems that leverage big data allow more and more tasks to be solved in an automatic manner. Whilst in the past these were predominantly tasks of a mundane and repetitive nature, advances in text analytics and machine learning have also made it possible to solve more complex problems (Christensen, Nørskov, Frederiksen, & Scholderer, 2017).A problem that continues to occupy scholars and practitioners of new product development is how to obtain and select ideas for new products (e.g., di Gangi, Wasko, & Hooker, 2010;Frederiksen & Knudsen, 2017;van den Ende, Frederiksen, & Prencipe, 2015). In the context of inbound open innovation, Ooms, Bell, and Kok (2015), for example, argue that firms can enhance their receptivity-i.e., their capacity to absorb more diverse external knowledge from more varied sources-by engaging with social media. Whilst this can in theory expand a firm's boundaries for information absorption, the extent of engagement with social media is still constrained by available staff time. Such constraints can to some degree be overcome if companies develop or adopt systems that automate parts of the absorption process.The aim of the research presented here is to show how the performance of automated systems in areas such as inbound open innovation can be evaluated. On one hand, the study should be seen as a feasibility study of whether automated detection of ideas for product and process innovations is actually possible. On the other hand, it should also be regarded as a validation study that probes the "veracity"and "value" aspects of big data (Gandomi ...

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