Ontologies are complex intellectual artifacts and creating them requires significant expertise and effort. While existing ontologyediting tools and methodologies propose ways of building ontologies in a normative way, empirical investigations of how experts actually construct ontologies "in the wild" are rare. Yet, understanding actual user behavior can play an important role in the design of effective tool support. Although previous empirical investigations have produced a series of interesting insights, they were exploratory in nature and aimed at gauging the problem space only. In this work, we aim to advance the state of knowledge in this domain by systematically defining and comparing a set of hypotheses about how users edit ontologies. Towards that end, we study the user editing trails of four real-world ontologyengineering projects. Using a coherent research framework, called Hyp-Trails, we derive formal definitions of hypotheses from the literature, and systematically compare them with each other. Our findings suggest that the hierarchical structure of an ontology exercises the strongest influence on user editing behavior, followed by the entity similarity, and the semantic distance of classes in the ontology. Moreover, these findings are strikingly consistent across all ontology-engineering projects in our study, with only minor exceptions for one of the smaller datasets. We believe that our results are important for ontology tools builders and for project managers, who can potentially leverage this information to create user interfaces and processes that better support the observed editing patterns of users.
With the widespread adoption of social media sites like Twitter and Facebook, there has been a shift in the way information is produced and consumed. Earlier, the only producers of information were traditional news organizations, which broadcast the same carefully-edited information to all consumers over mass media channels. Whereas, now, in online social media, any user can be a producer of information, and every user selects which other users she connects to, thereby choosing the information she consumes. Moreover, the personalized recommendations that most social media sites provide also contribute towards the information consumed by individual users. In this work, we define a concept of information diet — which is the topical distribution of a given set of information items (e.g., tweets) — to characterize the information produced and consumed by various types of users in the popular Twitter social media. At a high level, we find that (i) popular users mostly produce very specialized diets focusing on only a few topics; in fact, news organizations (e.g., NYTimes) produce much more focused diets on social media as compared to their mass media diets, (ii) most users' consumption diets are primarily focused towards one or two topics of their interest, and (iii) the personalized recommendations provided by Twitter help to mitigate some of the topical imbalances in the users' consumption diets, by adding information on diverse topics apart from the users' primary topics of interest.
Nowadays, human movement in urban spaces can be traced digitally in many cases. It can be observed that movement patterns are not constant, but vary across time and space. In this work, we characterize such spatio-temporal patterns with an innovative combination of two separate approaches that have been utilized for studying human mobility in the past. First, by using non-negative tensor factorization (NTF), we are able to cluster human behavior based on spatio-temporal dimensions. Second, for understanding these clusters, we propose to use HypTrails, a Bayesian approach for expressing and comparing hypotheses about human trails. To formalize hypotheses we utilize data that is publicly available on the Web, namely Foursquare data and census data provided by an open data platform. By applying this combination of approaches to taxi data in Manhattan, we can discover and characterize different patterns in human mobility that cannot be identified in a collective analysis. As one example, we can find a group of taxi rides that end at locations with a high number of party venues (according to Foursquare) on weekend nights. Overall, our work demonstrates that human mobility is not one-dimensional but rather contains different facets both in time and space which we explain by utilizing online data. The findings of this paper argue for a more fine-grained analysis of human mobility in order to make more informed decisions for e.g., enhancing urban structures, tailored traffic control and location-based recommender systems.
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