Know Center Research

Publikationen

Kowald Dominik, Lex Elisabeth, Kopeinik Simone
2016
In recent years, a number of recommendation algorithmshave been proposed to help learners find suitable learning resources online.Next to user-centered evaluations, offline-datasets have been usedto investigate new recommendation algorithms or variations of collaborativefiltering approaches. However, a more extensive study comparinga variety of recommendation strategies on multiple TEL datasets ismissing. In this work, we contribute with a data-driven study of recommendationstrategies in TEL to shed light on their suitability forTEL datasets. To that end, we evaluate six state-of-the-art recommendationalgorithms for tag and resource recommendations on six empiricaldatasets: a dataset from European Schoolnets TravelWell, a dataset fromthe MACE portal, which features access to meta-data-enriched learningresources from the field of architecture, two datasets from the socialbookmarking systems BibSonomy and CiteULike, a MOOC dataset fromthe KDD challenge 2015, and Aposdle, a small-scale workplace learningdataset. We highlight strengths and shortcomings of the discussed recommendationalgorithms and their applicability to the TEL datasets.Our results demonstrate that the performance of the algorithms stronglydepends on the properties and characteristics of the particular dataset.However, we also find a strong correlation between the average numberof users per resource and the algorithm performance. A tag recommenderevaluation experiment reveals that a hybrid combination of a cognitiveinspiredand a popularity-based approach consistently performs best onall TEL datasets we utilized in our study.
Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth
2016
In this paper, we present work-in-progress on a recommender system designed to help people in need find the best suited social care institution for their personal issues. A key requirement in such a domain is to assure and to guarantee the person's privacy and anonymity in order to reduce inhibitions and to establish trust. We present how we aim to tackle this barely studied domain using a hybrid content-based recommendation approach. Our approach leverages three data sources containing textual content, namely (i) metadata from social care institutions, (ii) institution specific FAQs, and (iii) questions that a specific institution has already resolved. Additionally, our approach considers the time context of user questions as well as negative user feedback to previously provided recommendations. Finally, we demonstrate an application scenario of our recommender system in the form of a real-world Web system deployed in Austria.
Stanisavljevic Darko, Hasani-Mavriqi Ilire, Lex Elisabeth, Strohmaier M., Helic Denis
2016
In this paper we assess the semantic stability of Wikipedia by investigat-ing the dynamics of Wikipedia articles’ revisions over time. In a semantically stablesystem, articles are infrequently edited, whereas in unstable systems, article contentchanges more frequently. In other words, in a stable system, the Wikipedia com-munity has reached consensus on the majority of articles. In our work, we measuresemantic stability using the Rank Biased Overlap method. To that end, we prepro-cess Wikipedia dumps to obtain a sequence of plain-text article revisions, whereaseach revision is represented as a TF-IDF vector. To measure the similarity betweenconsequent article revisions, we calculate Rank Biased Overlap on subsequent termvectors. We evaluate our approach on 10 Wikipedia language editions includingthe five largest language editions as well as five randomly selected small languageeditions. Our experimental results reveal that even in policy driven collaborationnetworks such as Wikipedia, semantic stability can be achieved. However, there aredifferences on the velocity of the semantic stability process between small and largeWikipedia editions. Small editions exhibit faster and higher semantic stability than large ones. In particular, in large Wikipedia editions, a higher number of successiverevisions is needed in order to reach a certain semantic stability level, whereas, insmall Wikipedia editions, the number of needed successive revisions is much lowerfor the same level of semantic stability.
Dennerlein Sebastian, Lex Elisabeth, Ruiz-Calleja Adolfo, Ley Elisabeth
2016
This paper reports the design and development of a visual Dashboard, called the SSS Dashboard, which visualizes data from informal workplace learning processes from different viewpoints. The SSS Dashboard retrieves its data from the Social Semantic Server (SSS), an infrastructure that integrates data from several workplace learning applications into a semantically-enriched Artifact-Actor Network. A first evaluation with end users in a course for professional teachers gave promising results. Both a trainer and a learner could understand the learning process from different perspectives using the SSS Dashboard. The results obtained will pave the way for the development of future Learning Analytics applications that exploit the data collected by the SSS.
Malarkodi C. S., Lex Elisabeth, Sobha Lalitha Devi
2016
Agricultural data have a major role in the planning and success of rural development activi ties. Agriculturalists, planners, policy makers, gover n- ment officials, farmers and researchers require relevant information to trigger decision making processes. This paper presents our approach towards extracting named entities from real - world agricultura l data from different areas of agricu l- ture using Conditional Random Fields (CRFs). Specifically, we have created a Named Entity tagset consisting of 19 fine grained tags. To the best of our knowledge, there is no specific tag set and annotated corpus avail able for the agricultural domain. We have performed several experiments using different combination of features and obtained encouraging results. Most of the issues observed in an error analysis have been addressed by post - processing heuristic rules, which resulted in a significant improvement of our system’s accuracy
Luzhnica Granit, Simon Jörg Peter, Lex Elisabeth, Pammer-Schindler Viktoria
2016
This paper explores the recognition of hand gestures based on a dataglove equipped with motion, bending and pressure sensors. We se-lected 31 natural and interaction-oriented hand gestures that canbe adopted for general-purpose control of and communication withcomputing systems. The data glove is custom-built, and contains13 bend sensors, 7 motion sensors, 5 pressure sensors and a magne-tometer. We present the data collection experiment, as well as thedesign, selection and evaluation of a classification algorithm. As weuse a sliding window approach to data processing, our algorithm issuitable for stream data processing. Algorithm selection and featureengineering resulted in a combination of linear discriminant anal-ysis and logistic regression with which we achieve an accuracy ofover 98. 5% on a continuous data stream scenario. When removingthe computationally expensive FFT-based features, we still achievean accuracy of 98. 2%.
Lacic Emanuel, Kowald Dominik, Lex Elisabeth
2016
Air travel is one of the most frequently used means of transportation in our every-day life. Thus, it is not surprising that an increasing number of travelers share their experiences with airlines and airports in form of online reviews on the Web. In this work, we thrive to explain and uncover the features of airline reviews that contribute most to traveler satisfaction. To that end, we examine reviews crawled from the Skytrax air travel review portal. Skytrax provides four review categories to review airports, lounges, airlines and seats. Each review category consists of several five-star ratings as well as free-text review content. In this paper, we conducted a comprehensive feature study and we find that not only five-star rating information such as airport queuing time and lounge comfort highly correlate with traveler satisfaction but also textual features in the form of the inferred review text sentiment. Based on our findings, we created classifiers to predict traveler satisfaction using the best performing rating features. Our results reveal that given our methodology, traveler satisfaction can be predicted with high accuracy. Additionally, we find that training a model on the sentiment of the review text provides a competitive alternative when no five star rating information is available. We believe that our work is of interest for researchers in the area of modeling and predicting user satisfaction based on available review data on the Web.
Dennerlein Sebastian, Ley Tobias, , Lex Elisabeth, Seitlinger Paul
2016
In the digital realm, meaning making is reflected in the reciprocal manipulation of mediating artefacts. We understand uptake, i.e. interaction with and understanding of others’ artefact interpretations, as central mechanism and investigate its impact on individual and social learning at work. Results of our social tagging field study indicate that increased uptake of others’ tags is related to a higher shared understanding of collaborators as well as narrower and more elaborative exploration in individual information search. We attribute the social and individual impact to accommodative processes in the high uptake condition.
Kowald Dominik, Lex Elisabeth
2016
In this paper, we study factors that in uence tag reuse behavior in social tagging systems. Our work is guided by the activation equation of the cognitive model ACT-R, which states that the usefulness of information in human memory depends on the three factors usage frequency, recency and semantic context. It is our aim to shed light on the in uence of these factors on tag reuse. In our experiments, we utilize six datasets from the social tagging systems Flickr, CiteULike, BibSonomy, Delicious, LastFM and MovieLens, covering a range of various tagging settings. Our results con rm that frequency, recency and semantic context positively in uence the reuse probability of tags. However, the extent to which each factor individually in uences tag reuse strongly depends on the type of folksonomy present in a social tagging system. Our work can serve as guideline for researchers and developers of tag-based recommender systems when designing algorithms for social tagging environments.