Wir sind Österreichs Forschungszentrum für Data-driven Business & Big Data Analytics.
Wir
machen
unsere
Kompetenzen
und
Innovationen
nach
Außen
hin
sichtbar.
Publikationen
Hier finden Sie von Know-Center MitarbeiterInnen verfasste wissenschaftliche Publikationen
2016
Lindstaedt Stefanie , Ley Tobias, Klamma Ralf, Wild Fridolin
Learning analytics for workplace and professional learning
Proceedings of the Sixth International Conference on Learning Analytics & Knowledge, ACM, 2016
Konferenz
Recognizing the need for addressing the rather fragmented character of research in this field, we have held a workshop on learning analytics for workplace and professional learning at the Learning Analytics and Knowledge (LAK) Conference. The workshop has taken a broad perspective, encompassing approaches from a number of previous traditions, such as adaptive learning, professional online communities, workplace learning and performance analytics. Being co-located with the LAK conference has provided an ideal venue for addressing common challenges and for benefiting from the strong research on learning analytics in other sectors that LAK has established. Learning Analytics for Workplace and Professional Learning is now on the research agenda of several ongoing EU projects, and therefore a number of follow-up activities are planned for strengthening integration in this emerging field.
2016
Malarkodi C. S., Lex Elisabeth, Sobha Lalitha Devi
Named Entity Recognition for the Agricultural Domain
17th International Conference on Intelligent Text Processing and Computational Linguistics (CICLING 2016); Research in Computing Science, CICLING 2016, Springer Lecture Notes in Computer Science, Konya, Turkey, 2016
Konferenz
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
2016
Trattner Christoph, Elsweiler David, Howard Simon
Estimating the Healthiness of Internet recipes: Implications for Recommender Systems and Meal Planning
British Medical Journal (BMJ) , Frontiers in Public Health, 2016
Konferenz
One government response to increasing incidence of lifestyle related illnesses, such as obesity, has been to encourage people to cook for themselves. The healthiness of home cooking will, nevertheless, depend on what people cook and how they cook it. In this article one common source of cooking inspiration - Internet-sourced recipes - is investigated in depth. The energy and macronutrient content of 5237 main meal recipes from the food website Allrecipes.com are compared with those of 100 main meal recipes from five bestselling cookery books from popular celebrity chefs and 100 ready meals from the three leading UK supermarkets. The comparison is made using nutritional guidelines published by the World Health Organisation and the UK Food Standards Agency. The main conclusions drawn from our analyses are that Internet recipes sourced from Allrecipes.com are less healthy than TV-chef recipes and ready meals from leading UK supermarkets. Only 6 out of 5237 Internet recipes fully complied with the WHO recommendations. Internet recipes were more likely to meet the WHO guidelines for protein than both other classes of meal (10.88% v 7% (TV), p<0.01; 10.86% v 9% (ready), p<0.01). However, the Internet recipes were less likely to meet the criteria for fat (14.28% v 24% (TV) v 37% (ready); p<0.01), saturated fat (25.05% v 33% (TV) v 34% (ready); p<0.01) and fibre (compared to ready meals 16.50% v 56%; p<0.01). More Internet recipes met the criteria for sodium density than ready meals (19.63% v 4%; p<0.01), but fewer than the TV-chef meals (19.32% v 36%; p<0.01). For sugar, no differences between Internet recipes and TV-chef recipes were observed (81.1% v 81% (TV); p=0.86), although Internet recipes were less likely to meet the sugar criteria than ready meals (81.1% v 83 % (ready); p<0.01). Repeating the analyses for each year of available data shows that the results are very stable over time.
2016
Tschinkel Gerwald, Hasitschka Peter, Sabol Vedran, Hafner R
Using Micro-Visualisations to Support Faceted Filtering of Recommender Results
Information Visualisation (IV), 2016 20th International Conference, IEEE, Lisbon, Portugal, 2016
Konferenz
Faceted search is a well known and broadly imple-
mented paradigm for filtering information with various
types of structured information. In this paper we introduce
a multiple-view faceted interface, consisting of one main
visualisation for exploring the data and multiple minia-
turised visualisations showing the filters. The Recommen-
dation Dashboard tool provides several interactive visual-
isations for analysing recommender results along various
faceted dimensions specific to cultural heritage and scien-
tific content. As our aim is to reduce the user load and opti-
mise the use of screen area, we permit only one main visu-
alisation to be visible at a time, and introduce the concept
of micro-visualisations – small, simplified views conveying
only the necessary information – to provide natural, easy
to understand representation of the the active filter set.
2016
Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Gamehoices for Implementing a Tangible Version of the Sokoban Game
2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR 2016, 2016
Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup.Index Terms: H.5.2 [HCI]: User Interfaces—Input devicesand strategies; H.5.1 [HCI]: Multimedia Information Systems—Artificial, augmented, and virtual realities
2016
Barreiros Carla, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Pre-attentive Features in Natural Augmented Reality Visualizations
2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings ISMAR, 2016 IEEE International Symposium on Mixed and Augmented Reality Adjunct Proceedings, Merida, Yucatan, Mexico, 2016
Konferenz
The movement towards cyberphysical systems and Industry 4.0promises to imbue each and every stage of production with a myr-iad of sensors. The open question is how people are to comprehendand interact with data originating from industrial machinery. Wepropose a metaphor that compares machines with natural beingsthat appeal to people by representing machine states with patternsoccurring in nature. Our approach uses augmented reality (AR)to represent machine states as trees of different shapes and col-ors (BioAR). We performed a study on pre-attentive processing ofvisual features in AR to determine if our BioAR metaphor con-veys fast changes unambiguously and accurately. Our results indi-cate that the visual features in our BioAR metaphor are processedpre-attentively. In contrast to previous research, for the BioARmetaphor, variations in form induced less errors than variations inhue in the target detection task.
2016
Luzhnica Granit, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Skin Reading: Encoding Text in a 6-Channel Haptic Display
Proceedings of the 2016 ACM International Symposium on Wearable Computing, ACM, Heidelberg, 2016
Konferenz
This paper investigates the communication of natural lan-guage messages using a wearable haptic display. Our re-search spans both the design of the haptic display, as wellas the methods for communication that use it. First, threewearable configurations are proposed basing on haptic per-ception fundamentals. To encode symbols, we devise an over-lapping spatiotemporal stimulation (OST) method, that dis-tributes stimuli spatially and temporally with a minima gap.An empirical study shows that, compared with spatial stimu-lation, OST is preferred in terms of recall. Second, we pro-pose an encoding for the entire English alphabet and a train-ing method for letters, words and phrases. A second study in-vestigates communication accuracy. It puts four participantsthrough five sessions, for an overall training time of approx-imately 5 hours per participant. Results reveal that after onehour of training, participants were able to discern 16 letters,and identify two- and three-letter words. They could discernthe full English alphabet (26letters,92%accuracy) after ap-proximately three hours of training, and after five hours par-ticipants were able to interpret words transmitted at an aver-age duration of0.6s per word
2016
Luzhnica Granit, Pammer-Schindler Viktoria, Fessl Angela, Mutlu Belgin, Veas Eduardo Enrique
Designing Generic Visualisations for Activity Log Data
Workshop on Awareness and Reflection in Technology Enhanced Learning (ARTEL16), CEUR-WS, Lyon, 2016
Konferenz
Especially in lifelong or professional learning, the picture of
a continuous learning analytics process emerges. In this proces
s, het-
erogeneous and changing data source applications provide data
relevant
to learning, at the same time as questions of learners to data cha
nge.
This reality challenges designers of analytics tools, as it req
uires ana-
lytics tools to deal with data and analytics tasks that are unk
nown at
application design time. In this paper, we describe a generic vi
sualiza-
tion tool that addresses these challenges by enabling the vis
ualization
of any activity log data. Furthermore, we evaluate how well parti
cipants
can answer questions about underlying data given such generic
versus
custom visualizations. Study participants performed better in
5 out of
10 tasks with the generic visualization tool, worse in 1 out of 1
0 tasks,
and without significant difference when compared to the visuali
zations
within the data-source applications in the remaining 4 of 10 ta
sks. The
experiment clearly showcases that overall, generic, standalon
e visualiza-
tion tools have the potential to support analytical tasks suffi
ciently well
2016
Eberhard Lukas, Trattner Christoph
Recommending Sellers to Buyers in Virtual Marketplaces Leveraging Social Information
WWW '16 Companion Proceedings of the 25th International Conference Companion on World Wide Web, WWW '16, Canton of Geneva, 2016
Konferenz
Social information such as stated interests or geographic check-insin social networks has shown to be useful in many recommendertasks recently. Although many successful examples exist, not muchattention has been put on exploring the extent to which social im-pact is useful for the task of recommending sellers to buyers in vir-tual marketplaces. To contribute to this sparse field of research wecollected data of a marketplace and a social network in the virtualworld of Second Life and introduced several social features andsimilarity metrics that we used as input for a user-basedk-nearestneighbor collaborative filtering method. As our results reveal, mostof the types of social information and features which we used areuseful to tackle the problem we defined. Social information suchas joined groups or stated interests are more useful, while otherssuch as places users have been checking in, do not help much forrecommending sellers to buyers. Furthermore, we find that some ofthe features significantly vary in their predictive power over time,while others show more stable behaviors. This research is rele-vant for researchers interested in recommender systems and onlinemarketplace research as well as for engineers interested in featureengineering.
2016
Trattner Christoph, Oberegger Alexander, Eberhard Lukas, Parra Denis, Marinho Leandro
Understanding the Impact of Weather for POI recomennder systems
RecTour’16,, ACM, Boston, 2016
Konferenz
POI (point of interest) recommender systems for location-based social network services, such as Foursquare or Yelp,have gained tremendous popularity in the past few years.Much work has been dedicated into improving recommenda-tion services in such systems by integrating different featuresthat are assumed to have an impact on people’s preferencesfor POIs, such as time and geolocation. Yet, little atten-tion has been paid to the impact of weather on the users’final decision to visit a recommended POI. In this paper wecontribute to this area of research by presenting the firstresults of a study that aims to predict the POIs that userswill visit based on weather data. To this end, we extend thestate-of-the-art Rank-GeoFM POI recommender algorithmwith additional weather-related features, such as tempera-ture, cloud cover, humidity and precipitation intensity. Weshow that using weather data not only significantly increasesthe recommendation accuracy in comparison to the origi-nal algorithm, but also outperforms its time-based variant.Furthermore, we present the magnitude of impact of eachfeature on the recommendation quality, showing the need tostudy the weather context in more detail in the light of POIrecommendation systems.
2016
Kusmierczyk Tomasz, Trattner Christoph, Nørvåg Kjetil
Understanding and Predicting Online Food Recipe Production Patterns
HT '16 Proceedings of the 27th ACM Conference on Hypertext and Social Media, ACM, Halifax, NS, Canada, 2016
Konferenz
Studying online food patterns has recently become an active fieldof research. While there are a growing body of studies that investi-gate how online food in consumed, little effort has been devoted yetto understand how online food recipes are being created. To con-tribute to this lack of knowledge in the area, we present in this paperthe results of a large-scale study that aims at understanding howhistorical, social and temporal factors impact on the online foodcreation process. Several experiments reveal the extent to whichvarious factors are useful in predicting future recipe production.
2016
Fessl Angela, Wesiak Gudrun, Pammer-Schindler Viktoria
A Reflective Quiz in a Professional Qualification Program for Stroke Nurses: A Field Trial
EC-TEL 2016 Proceedings, Springer Link, Springer-Verlag, Cham, 2016
Konferenz
Reflective learning is an important strategy to keep the vast body of theoretical knowledge fresh, stay up-to-date with new knowledge, and to relate theoretical knowledge to practical experience. In this work, we present a study situated in a qualification program for stroke nurses in Germany. In the seven-week study, $21$ stroke nurses used a quiz on medical knowledge as additional learning instrument. The quiz contained typical quiz questions (``content questions'') as well as reflective questions that aimed at stimulating nurses to reflect on the practical relevance of the learned knowledge.We particularly looked at how reflective questions can support the transfer of theoretical knowledge to practice.The results show that by playful learning and presenting reflective questions at the right time, participants were motivated to reflect, deepened their knowledge and related theoretical knowledge to practical experience. Subsequently, they were able to better understand patient treatments and increased their self-confidence.
2016
Dennerlein Sebastian, Lex Elisabeth, Ruiz-Calleja Adolfo, Ley Elisabeth
Visualizing workplace learning data with the SSS Dashboard
Learning Analytics for Workplace and Professional Learning (LA for Work) workshop at LAK 2016, CEUR Workshop Proceedings, Edinburgh, 2016
Konferenz
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.
2016
Luzhnica Granit, Simon Jörg Peter, Lex Elisabeth, Pammer-Schindler Viktoria
A Sliding Window Approach to Natural Hand Gesture Recognition using a Custom Data Glove
Proceedings of the IEEE 3DUI 2016 Symposium on 3D User Interfaces, IEEE, Greenville, SC, USA, 2016
Konferenz
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%.
2016
Santos Tiago, Kern Roman
A Literature Survey of Early Time Series Classification and Deep Learning
SamI40 workshop at i-KNOW'16, 2016
Konferenz
This paper provides an overview of current literature on timeseries classification approaches, in particular of early timeseries classification.A very common and effective time series classification ap-proach is the 1-Nearest Neighbor classifier, with differentdistance measures such as the Euclidean or dynamic timewarping distances. This paper starts by reviewing thesebaseline methods.More recently, with the gain in popularity in the applica-tion of deep neural networks to the field of computer vision,research has focused on developing deep learning architec-tures for time series classification as well. The literature inthe field of deep learning for time series classification hasshown promising results.Early time series classification aims to classify a time se-ries with as few temporal observations as possible, whilekeeping the loss of classification accuracy at a minimum.Prominent early classification frameworks reviewed by thispaper include, but are not limited to, ECTS, RelClass andECDIRE. These works have shown that early time seriesclassification may be feasible and performant, but they alsoshow room for improvement
2016
Lacic Emanuel, Kowald Dominik, Lex Elisabeth
High Enough? Explaining and Predicting Traveler Satisfaction Using Airline Reviews.
27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016
Konferenz
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.
2016
Dennerlein Sebastian, Ley Tobias, , Lex Elisabeth, Seitlinger Paul
Take up my Tags: Exploring Benefits of Collaborative Learning in a Social Tagging Field Study at the Workplace
European Conference on Technology Enhanced Learning (EC-TEL 2016), EC-TEL 2016, Springer-Verlag, Cham, 2016
Konferenz
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.
2016
Kowald Dominik, Lex Elisabeth
The Influence of Frequency, Recency and Semantic Context on the Reuse of Tags in Social Tagging Systems
27th ACM Conference on Hypertext and Hypermedia, Hypertext'2016, ACM, Halifax, 2016
Konferenz
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.
2016
Klampfl Stefan, Kern Roman
Reconstructing the Logical Structure of a Scientific Publication using Machine Learning
Semantic Web Challenges, Communications in Computer and Information Science, Springer Link, Springer-Verlag, 2016
Konferenz
Semantic enrichment of scientific publications has an increasing impact on scholarly communication. This document describes our contribution to Semantic Publishing Challenge 2016, which aims at investigating novel approaches for improving scholarly publishing through semantic technologies. We participated in Task 2 of this challenge, which requires the extraction of information from the content of a paper given as PDF. The extracted information allows answering queries about the paper’s internal organisation and the context in which it was written. We build upon our contribution to the previous edition of the challenge, where we categorised meta-data, such as authors and affiliations, and extracted funding information. Here we use unsupervised machine learning techniques in order to extend the analysis of the logical structure of the document as to identify section titles and captions of figures and tables. Furthermore, we employ clustering techniques to create the hierarchical table of contents of the article. Our system is modular in nature and allows a separate training of different stages on different training sets.
2016
Urak Günter, Ziak Hermann, Kern Roman
Do Ambiguous Words Improve Probing For Federated Search?
International Conference on Theory and Practice of Digital Libraries, TPDL 2016, Springer-Verlag, 2016
Konferenz
The core approach to distributed knowledge bases is federated search. Two of the main challenges for federated search are the source representation and source selection. Different solutions to these problems were proposed in the literature. Within this work we present our novel approach for query-based sampling by relying on knowledge bases. We show the basic correctness of our approach and we came to the insight that the ambiguity of the probing terms has just a minor impact on the representation of the collection. Finally, we show that our method can be used to distinguish between niche and encyclopedic knowledge bases.
2016
Horn Christopher, Gursch Heimo, Kern Roman, Cik Michael
QZTool – Automatically generated Origin-Destination Matrices from Cell Phone Trajectories
Advances in The Human Side of Service Engineering: Proceedings of the AHFE 2016 International Conference on Human Factors and Sustainable Infrastructure, July 27-31, 2016, Walt Disney World®, Florida, USA, Jerzy Charytonowicz (series Editor), Neville A. Stanton and Steven Landry and Giuseppe Di Bucchianico and Andrea Vallicelli, Springer International Publishing, Cham, Switzerland, 2016
Konferenz
Models describing human travel patterns are indispensable to plan and operate road, rail and public transportation networks. For most kind of analyses in the field of transportation planning, there is a need for origin-destination (OD) matrices, which specify the travel demands between the origin and destination zones in the network. The preparation of OD matrices is traditionally a time consuming and cumbersome task. The presented system, QZTool, reduces the necessary effort as it is capable of generating OD matrices automatically. These matrices are produced starting from floating phone data (FPD) as raw input. This raw input is processed by a Hadoop-based big data system. A graphical user interface allows for an easy usage and hides the complexity from the operator. For evaluation, we compare a FDP-based OD matrix to an OD matrix created by a traffic demand model. Results show that both matrices agree to a high degree, indicating that FPD-based OD matrices can be used to create new, or to validate or amend existing OD matrices.
2016
Falk Stefan, Rexha Andi, Kern Roman
Know-Center at SemEval-2016 Task 5: Using Word Vectors with Typed Dependencies for Opinion Target Expression Extraction
Conference: Proceedings of the 10th International Workshop on Semantic Evaluation (SemEval-2016), SemEval 2016, ACL Anthology, San Diego, USA, 2016
Konferenz
This paper describes our participation in SemEval-2016 Task 5 for Subtask 1, Slot 2.The challenge demands to find domain specific target expressions on sentence level thatrefer to reviewed entities. The detection of target words is achieved by using word vectorsand their grammatical dependency relationships to classify each word in a sentence into target or non-target. A heuristic based function then expands the classified target words tothe whole target phrase. Our system achievedan F1 score of 56.816% for this task.
2016
Dragoni Mauro, Rexha Andi, Kröll Mark, Kern Roman
Polarity Classification for Target Phrases in Tweets: A Word2Vec approach
The Semantic Web, ESWC 2016 Satellite Events, ESWC 2016, Springer-Verlag, Crete, Greece, 2016
Konferenz
Twitter is one of the most popular micro-blogging serviceson the web. The service allows sharing, interaction and collaboration viashort, informal and often unstructured messages called tweets. Polarityclassification of tweets refers to the task of assigning a positive or a nega-tive sentiment to an entire tweet. Quite similar is predicting the polarityof a specific target phrase, for instance@Microsoftor#Linux,whichiscontained in the tweet.In this paper we present a Word2Vec approach to automatically pre-dict the polarity of a target phrase in a tweet. In our classification setting,we thus do not have any polarity information but use only semantic infor-mation provided by a Word2Vec model trained on Twitter messages. Toevaluate our feature representation approach, we apply well-establishedclassification algorithms such as the Support Vector Machine and NaiveBayes. For the evaluation we used theSemeval 2016 Task #4dataset.Our approach achieves F1-measures of up to∼90 % for the positive classand∼54 % for the negative class without using polarity informationabout single words.
2016
Pimas Oliver, Klampfl Stefan, Kohl Thomas, Kern Roman, Kröll Mark
Generating Tailored Classification Schemas for German Patents
21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016
Konferenz
Patents and patent applications are important parts of acompany’s intellectual property. Thus, companies put a lot of effort indesigning and maintaining an internal structure for organizing their ownpatent portfolios, but also in keeping track of competitor’s patent port-folios. Yet, official classification schemas offered by patent offices (i) areoften too coarse and (ii) are not mappable, for instance, to a company’sfunctions, applications, or divisions. In this work, we present a first steptowards generating tailored classification. To automate the generationprocess, we apply key term extraction and topic modelling algorithmsto 2.131 publications of German patent applications. To infer categories,we apply topic modelling to the patent collection. We evaluate the map-ping of the topics found via the Latent Dirichlet Allocation method tothe classes present in the patent collection as assigned by the domainexpert.
2016
Steinbauer Florian, Kröll Mark
Sentiment Analysis for German Facebook Pages
21st International Conference on Applications of Natural Language to Information Systems, NLDB 2016, Springer-Verlag, Salford, UK, 2016
Konferenz
Social media monitoring has become an important means forbusiness analytics and trend detection, for instance, analyzing the senti-ment towards a certain product or decision. While a lot of work has beendedicated to analyze sentiment for English texts, much less effort hasbeen put into providing accurate sentiment classification for the Germanlanguage. In this paper, we analyze three established classifiers for theGerman language with respect to Facebook posts. We then present ourown hierarchical approach to classify sentiment and evaluate it using adata set of∼640 Facebook posts from corporate as well as governmentalFacebook pages. We compare our approach to three sentiment classifiersfor German, i.e. AlchemyAPI, Semantria and SentiStrength. With anaccuracy of 70 %, our approach performs better than the other classi-fiers. In an application scenario, we demonstrate our classifier’s abilityto monitor changes in sentiment with respect to the refugee crisis.
2016
Ziak Hermann, Rexha Andi, Kern Roman
KNOW At The Social Book Search Lab 2016 Mining Track
CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro,Craig Macdonald, Springer, Évora, Portugal, 2016
Konferenz
This paper describes our system for the mining task of theSocial Book Search Lab in 2016. The track consisted of two task, theclassification of book request postings and the task of linking book identifierswith references mentioned within the text. For the classificationtask we used text mining features like n-grams and vocabulary size, butalso included advanced features like average spelling errors found withinthe text. Here two datasets were provided by the organizers for this taskwhich were evaluated separately. The second task, the linking of booktitles to a work identifier, was addressed by an approach based on lookuptables. For the dataset of the first task our approach was ranked third,following two baseline approaches of the organizers with an accuracy of91 percent. For the second dataset we achieved second place with anaccuracy of 82 percent. Our approach secured the first place with anF-score of 33.50 for the second task.
2016
Kern Roman, Ziak Hermann
Query Splitting For Context-Driven Federated Recommendations
Database and Expert Systems Applications (DEXA), 2016 27th International Workshop on, IEEEE, Porto, Portugal, 2016
Konferenz
Context-driven query extraction for content-basedrecommender systems faces the challenge of dealing with queriesof multiple topics. In contrast to manually entered queries, forautomatically generated queries this is a more frequent problem. For instances if the information need is inferred indirectly viathe user's current context. Especially for federated search systemswere connected knowledge sources might react vastly differentlyon such queries, an algorithmic way how to deal with suchqueries is of high importance. One such method is to split mixedqueries into their individual subtopics. To gain insight how amulti topic query can be split into its subtopics we conductedan evaluation where we compared a naive approach against amore complex approaches based on word embedding techniques:One created using Word2Vec and one created using GloVe. Toevaluate these two approaches we used the Webis-QSeC-10 queryset, consisting of about 5,000 multi term queries. Queries of thisset were concatenated and passed through the algorithms withthe goal to split those queries again. Hence the naive approach issplitting the queries into several groups, according to the amountof joined queries, assuming the topics are of equal query termcount. In the case of the Word2Vec and GloVe based approacheswe relied on the already pre-trained datasets. The Google Newsmodel and a model trained with a Wikipedia dump and theEnglish Gigaword newswire text archive. The out of this datasetsresulting query term vectors were grouped into subtopics usinga k-Means clustering. We show that a clustering approach basedon word vectors achieves better results in particular when thequery is not in topical order. Furthermore we could demonstratethe importance of the underlying dataset.
2016
Gursch Heimo, Wuttei Andreas, Gangloff Theresa
Learning Systems for Manufacturing Management Support
Proceedings of the 1st International Workshop on Science, Application and Methods in Industry 4.0, Roman Kern, Gerald Reiner, Olivia Bluder, Graz, Austria, 2016
Konferenz
Highly optimised assembly lines are commonly used in various manufacturing domains, such as electronics, microchips, vehicles, electric appliances, etc. In the last decades manufacturers have installed software systems to control and optimise their shop foor processes. Machine Learning can enhance those systems by providing new insights derived from the previously captured data. This paper provides an overview of Machine Learning felds and an introduction to manufacturing management systems. These are followed by a discussion of research projects in the feld of applying Machine Learning solutions for condition monitoring, process control, scheduling, and predictive maintenance.
2016
Rexha Andi, Kern Roman, Dragoni Mauro , Kröll Mark
Exploiting Propositions for Opinion Mining
ESWC-16 Challenge on Semantic Sentiment Analysis, Springer Link, Springer-Verlag, Crete, Greece, 2016
Konferenz
With different social media and commercial platforms, users express their opinion about products in a textual form. Automatically extracting the polarity (i.e. whether the opinion is positive or negative) of a user can be useful for both actors: the online platform incorporating the feedback to improve their product as well as the client who might get recommendations according to his or her preferences. Different approaches for tackling the problem, have been suggested mainly using syntactic features. The “Challenge on Semantic Sentiment Analysis” aims to go beyond the word-level analysis by using semantic information. In this paper we propose a novel approach by employing the semantic information of grammatical unit called preposition. We try to drive the target of the review from the summary information, which serves as an input to identify the proposition in it. Our implementation relies on the hypothesis that the proposition expressing the target of the summary, usually containing the main polarity information.
2016
Gutounig Robert, Goldgruber Eva, Dennerlein Sebastian, Schweiger Stefan
Mehr als ein Kommunikationstool. Wissensmanagement-Potenziale von Social Software am Beispiel von Slack
Kremser Wissensmanagement-Tagen 2016, Edition Donau-Universität Krems , Krems, 2016
Konferenz
2016
Thalmann Stefan, Manhart Markus
Balancing Knowledge Protection and Sharing in Networks of SME
Proceedings of IKNOW 2016, 2016
Konferenz
Organizations join networks to acquire external knowledge. This is especially important for SMEs since they often lack resources and are dependent on external knowledge to achieve and sustain competitive advantage. However, finding the right balance between measures facilitating knowledge sharing and measures protecting knowledge is a challenge. Whilst sharing is the raison d’être of networks, neglecting knowledge protection can be also detrimental to network, e.g., lead to one-sided skimming of knowledge. We identified four practices SMEs currently apply to balance protection of competitive knowledge and knowledge sharing in the network: (a) share in subgroups with high trust, (b) share partial aspects of the knowledge base, (c) share with people with low proximities, and (d) share common knowledge and protect the crucial. We further found that the application of the practices depends on the maturity of the knowledge. Further, we discuss how the practices relate to organizational protection capabilities and how the network can provide IT to support the development of these capabilities.
2016
Thalmann Stefan, Ilvonen Ilona, Manhart Markus , Sillaber Christian
Knowledge Protection for Digital Innovations: Integrating Six Perspectives
Proceedings of the 11th Pre-ICIS Workshop on Information Security and Privacy, AIS Electronic Library (AISeL), Dublin, Ireland, 2016
Konferenz
New ways of combining digital and physical innovations, as well as intensified inter-organizational collaborations, create new challenges to the protection of organizational knowledge. Existing research on knowledge protection is at an early stage and scattered among various research domains. This research-in-progress paper presents a plan for a structured literature review on knowledge protection, integrating the perspectives of the six base domains of knowledge, strategic, risk, intellectual property rights, innovation, and information technology security management. We define knowledge protection as a set of capabilities comprising and enforcing technical, organizational, and legal mechanisms to protect tacit and explicit knowledge necessary to generate or adopt innovations.
2016
Cik Michael, Hebenstreit Cornelia, Horn Christopher, Schulze Gunnar, Traub Matthias, Schweighofer Erich, Hötzendorf Walter, Fellendorf Martin
Using cell phone and social media data to enhance safety at mega events
Transportation Research Board (TRB) 96th Annual Meeting, Washington DC, 2016
Konferenz
Guaranteeing safety during mega events has always played a role for organizers, their security guards and the action force. This work was realized to enhance safety at mega events and demonstrations without the necessity of fixed installations. Therefore a low cost monitoring system supporting the organization and safety personnel was developed using cell phone data and social media data in combination with safety concepts to monitor safety during the event in real time. To provide the achieved results in real time to the event and safety personnel an application for a Tablet-PC was established. Two representative events were applied as case studies to test and evaluate the results and to check response and executability of the app on site. Because data privacy is increasingly important, legal experts were closely involved and provided legal support.
2016
Ziak Hermann, Kern Roman
KNOW At The Social Book Search Lab 2016 Suggestion Track
CLEF 2016 Social Book Search Lab, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonal, CEUR Workshop Proceeding, Évora, Portugal, 2016
Konferenz
Within this work represents the documentation of our ap-proach on the Social Book Search Lab 2016 where we took part in thesuggestion track. The main goal of the track was to create book recom-mendation for readers only based on their stated request within a forum.The forum entry contained further contextual information, like the user’scatalogue of already read books and the list of example books mentionedin the user’s request. The presented approach is mainly based on themetadata included in the book catalogue provided by the organizers ofthe task. With the help of a dedicated search index we extracted severalpotential book recommendations which were re-ranked by the use of anSVD based approach. Although our results did not meet our expectationwe consider it as first iteration towards a competitive solution.
2016
Luzhnica Granit, Öjeling Christoffer, Veas Eduardo Enrique, Pammer-Schindler Viktoria
Technical Concept and Technology Choices for Implementing a Tangible Version of the Sokoban Game
Mixed and Augmented Reality (ISMAR-Adjunct), 2016 IEEE International Symposium on, IEEE, Merida, Mexico, 2016
Konferenz
This paper presents and discusses the technical concept of a virtualreality version of the Sokoban game with a tangible interface. Theunderlying rationale is to provide spinal-cord injury patients whoare learning to use a neuroprosthesis to restore their capability ofgrasping with a game environment for training. We describe as rel-evant elements to be considered in such a gaming concept: input,output, virtual objects, physical objects, activity tracking and per-sonalised level recommender. Finally, we also describe our experi-ences with instantiating the overall concept with hand-held mobilephones, smart glasses and a head mounted cardboard setup
2016
Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
Sense.me - Open Source Framework for the Exploration and Visualization of Eye Tracking Data
IEEEVis - Proc. IEEE Conference on Information Visualization, Baltimore, Maryland, USA, 2016
Konferenz
We present a new open-source prototype framework to exploreand visualize eye-tracking experiments data. Firstly, standard eyetrackersare used to record raw eye gaze data-points on user experiments.Secondly, the analyst can configure gaze analysis parameters,such as, the definition of areas of interest, multiple thresholdsor the labeling of special areas, and we upload the data to a searchserver. Thirdly, a faceted web interface for exploring and visualizingthe users’ eye gaze on a large number of areas of interest isavailable. Our framework integrates several common visualizationsand it also includes new combined representations like an eye analysisoverview and a clustered matrix that shows the attention timestrength between multiple areas of interest. The framework can bereadily used for the exploration of eye tracking experiments data.We make available the source code of our prototype framework foreye-tracking data analysis.
2016
Silva Nelson, Shao Lin, Schreck Tobias, Eggeling Eva, Fellner Dieter W.
Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking
FMT 2016 : 9th Forum Media Technology 2016, Wolfgang Aigner , Grischa Schmiedl , Kerstin Blumenstein , Matthias Zeppelzauer , Michael Iber, St. Pölten, 2016
Konferenz
Effective visual exploration of large data sets is an important problem. A standard tech- nique for mapping large data sets is to use hierarchical data representations (trees, or dendrograms) that users may navigate. If the data sets get large, so do the hierar- chies, and effective methods for the naviga- tion are required. Traditionally, users navi- gate visual representations using desktop in- teraction modalities, including mouse interac- tion. Motivated by recent availability of low- cost eye-tracker systems, we investigate ap- plication possibilities to use eye-tracking for controlling the visual-interactive data explo- ration process. We implemented a proof-of- concept system for visual exploration of hier- archic data, exemplified by scatter plot dia- grams which are to be explored for grouping and similarity relationships. The exploration includes usage of degree-of-interest based dis- tortion controlled by user attention read from eye-movement behavior. We present the basic elements of our system, and give an illustra- tive use case discussion, outlining the applica- tion possibilities. We also identify interesting future developments based on the given data views and captured eye-tracking information. (13) Visual Exploration of Hierarchical Data Using Degree-of-Interest Controlled by Eye-Tracking. Available from: https://www.researchgate.net/publication/309479681_Visual_Exploration_of_Hierarchical_Data_Using_Degree-of-Interest_Controlled_by_Eye-Tracking [accessed Oct 3, 2017].
2016
Berndt Rene, Silva Nelson, Edtmayr Thomas, Sunk Alexander, Krispel Ulrich, Caldera Christian, Eggeling Eva, Fellner Dieter W., Sihn Wilfried
VASCO - Mastering the Shoals of Value Stream Mapping
CONTENT 2016, The Eighth International conference on Creative Content Technologies, Hans-Werner Sehring, René Berndt, IARIA, Rome, Italy, 2016
Konferenz
Value stream mapping is a lean management method for analyzing and optimizing a series of events for production or services. Even today the first step in value stream analysis - the acquisition of the current state - is still created using pen & paper by physically visiting the production place. We capture a digital representation of how manufacturing processes look like in reality. The manufacturing processes can be represented and efficiently analyzed for future production planning by using a meta description together with a dependency graph. With our Value Stream Creator and explOrer (VASCO) we present a tool, which contributes to all parts of value stream analysis - from data acquisition, over planning, comparison with previous realities, up to simulation of future possible states.
2016
Gursch Heimo, Körner Stefan, Krasser Hannes, Kern Roman
Parameter Forecasting for Vehicle Paint Quality Optimisation
Mensch und Computer 2016 – Workshopband, Benjamin Weyers, Anke Dittmar, Gesellschaft für Informatik e.V., Aachen, 2016
Konferenz
Painting a modern car involves applying many coats during a highly complex and automated process. The individual coats not only serve a decoration purpose but are also curial for protection from damage due to environmental influences, such as rust. For an optimal paint job, many parameters have to be optimised simultaneously. A forecasting model was created, which predicts the paint flaw probability for a given set of process parameters, to help the production managers modify the process parameters to achieve an optimal result. The mathematical model was based on historical process and quality observations. Production managers who are not familiar with the mathematical concept of the model can use it via an intuitive Web-based Graphical User Interface (Web-GUI). The Web-GUI offers production managers the ability to test process parameters and forecast the expected quality. The model can be used for optimising the process parameters in terms of quality and costs.
2016
Gursch Heimo, Kern Roman
Internet of Things meets Big Data: An Infrastructure to Collect, Connect, and Analyse Sensor Data
VDE Kongress 2016: Internet der Dinge (VDE Kongress 2016), DE Verlag GmbH, Berlin - Offenbach, Congress Center Rosengarten, Mannheim, Germany, 2016
Konferenz
Many different sensing, recording and transmitting platforms are offered on today’s market for Internet of Things (IoT) applications. But taking and transmitting measurements is just one part of a complete system. Also long time storage and processing of recorded sensor values are vital for IoT applications. Big Data technologies provide a rich variety of processing capabilities to analyse the recorded measurements. In this paper an architecture for recording, searching, and analysing sensor measurements is proposed. This architecture combines existing IoT and Big Data technologies to bridge the gap between recording, transmission, and persistency of raw sensor data on one side, and the analysis of data on Hadoop clusters on the other side. The proposed framework emphasises scalability and persistence of measurements as well as easy access to the data from a variety of different data analytics tools. To achieve this, a distributed architecture is designed offering three different views on the recorded sensor readouts. The proposed architecture is not targeted at one specific use-case, but is able to provide a platform for a large number of different services.
2016
Goldgruber Eva, Gutounig Robert, Schweiger Stefan, Dennerlein Sebastian
Potential von "Slack" im E-Learning
E-Learning Tag 2016, 2016
Konferenz
2016
Dennerlein Sebastian, Gutounig Robert, Goldgruber Eva , Schweiger Stefan
Web 2.0 Messaging Tools for Knowledge Management? Exploring the Potentials of Slack
Proceedings of the 17th European Conference on Knowledge Management, Academic Conferences International Limited, Ulster University, Northern Ireland, 2016
Konferenz
There are many web-based tools like social networks, collaborative writing, or messaging tools that connectorganizations in accordance with web 2.0 principles. Slack is such a web 2.0 instant messaging tool. As per developer, itintegrates the entire communication, file-sharing, real-time messaging, digital archiving and search at one place. Usage inline with these functionalities would reflect expected appropriation, while other usage would account for unexpectedappropriation. We explored which factors of web 2.0 tools determine actual usage and how they affect knowledgemanagement (KM). Therefore, we investigated the relation between the three influencing factors, proposed tool utility fromdeveloper side, intended usage of key implementers, and context of application, to the actual usage in terms of knowledgeactivities (generate, acquire, organize, transfer and save knowledge). We conducted episodic interviews with keyimplementers in five different organizational contexts to understand how messaging tools affect KM by analyzing theappropriation of features. Slack was implemented with the intention to enable exchange between project teams, connectingdistributed project members, initiate a community of learners and establish a communication platform. Independent of thecontext, all key implementers agreed on knowledge transfer, organization and saving in accordance with Slack’s proposedutility. Moreover, results revealed that a usage intention of internal management does not lead to acquisition of externalknowledge, and usage intention of networking not to generation of new knowledge. These results suggest that it is not thecontext of application, but the intended usage that mainly affects the tool's efficacy with respect to KM: I.e. intention seemsto affect tool selection, first, explaining commonalities with respect to knowledge activities (expected appropriation) and,subsequently, intention also affects unexpected appropriation beyond the developers’ tool utility. A messaging tool is, hence,not only a messaging tool, but it is ‘what you make of it!’
2016
Mutlu Belgin, Sabol Vedran, Gursch Heimo, Kern Roman
From Data to Visualisations and Back: Selecting Visualisations Based on Data and System Design Considerations
arXiv, 2016
Konferenz
Graphical interfaces and interactive visualisations are typical mediators between human users and data analytics systems. HCI researchers and developers have to be able to understand both human needs and back-end data analytics. Participants of our tutorial will learn how visualisation and interface design can be combined with data analytics to provide better visualisations. In the first of three parts, the participants will learn about visualisations and how to appropriately select them. In the second part, restrictions and opportunities associated with different data analytics systems will be discussed. In the final part, the participants will have the opportunity to develop visualisations and interface designs under given scenarios of data and system settings.
2016
Kowald Dominik, Lex Elisabeth, Kopeinik Simone
Which Algorithms Suit Which Learning Environments? A Comparative Study of Recommender Systems in TEL
European Conference on Technology Enhanced Learning, EC-TEL'2016, Springer, Toledo, Spain, 2016
Konferenz
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.
2016
Rexha Andi, Klampfl Stefan, Kröll Mark, Kern Roman
Towards a more fine grained analysis of scientific authorship: Predicting the number of authors using stylometric features
BIR 2016 Workshop on Bibliometric-enhanced Information Retrieval, Atanassova, I.; Bertin, M.; Mayr, P., Springer, Padova, Italy, 2016
Konferenz
To bring bibliometrics and information retrieval closer together, we propose to add the concept of author attribution into the pre-processing of scientific publications. Presently, common bibliographic metrics often attribute the entire article to all the authors affecting author-specific retrieval processes. We envision a more finegrained analysis of scientific authorship by attributing particular segments to authors. To realize this vision, we propose a new feature representation of scientific publications that captures the distribution of tylometric features. In a classification setting, we then seek to predict the number of authors of a scientific article. We evaluate our approach on a data set of ~ 6100 PubMed articles and achieve best results by applying random forests, i.e., 0.76 precision and 0.76 recall averaged over all classes.
2016
Rexha Andi, Kröll Mark, Kern Roman
Social Media Monitoring for Companies: A 4W Summarisation Approach
European Conference on Knowledge Management, Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016
Konferenz
Monitoring (social) media represents one means for companies to gain access to knowledge about, for instance, competitors, products as well as markets. As a consequence, social media monitoring tools have been gaining attention to handle amounts of data nowadays generated in social media. These tools also include summarisation services. However, most summarisation algorithms tend to focus on (i) first and last sentences respectively or (ii) sentences containing keywords.In this work we approach the task of summarisation by extracting 4W (who, when, where, what) information from (social)media texts. Presenting 4W information allows for a more compact content representation than traditional summaries. Inaddition, we depart from mere named entity recognition (NER) techniques to answer these four question types by includingnon-rigid designators, i.e. expressions which do not refer to the same thing in all possible worlds such as “at the main square”or “leaders of political parties”. To do that, we employ dependency parsing to identify grammatical characteristics for each question type. Every sentence is then represented as a 4W block. We perform two different preliminary studies: selecting sentences that better summarise texts by achieving an F1-measure of 0.343, as well as a 4W block extraction for which we achieve F1-measures of 0.932; 0.900; 0.803; 0.861 for “who”, “when”, “where” and “what” category respectively. In a next step the 4W blocks are ranked by relevance. The top three ranked blocks, for example, then constitute a summary of the entire textual passage. The relevance metric can be customised to the user’s needs, for instance, ranked by up-to-dateness where the sentences’ tense is taken into account. In a user study we evaluate different ranking strategies including (i) up-todateness,(ii) text sentence rank, (iii) selecting the firsts and lasts sentences or (iv) coverage of named entities, i.e. based on the number of named entities in the sentence. Our 4W summarisation method presents a valuable addition to a company’s(social) media monitoring toolkit, thus supporting decision making processes.
2016
Pimas Oliver, Rexha Andi, Kröll Mark, Kern Roman
Profiling microblog authors using concreteness and sentiment - Know-Center at PAN 2016 author profiling
PAN 2016, Krisztian Balog, Linda Cappellato, Nicola Ferro, Craig Macdonald, Springer, Evora, Portugal, 2016
Konferenz
The PAN 2016 author profiling task is a supervised classification problemon cross-genre documents (tweets, blog and social media posts). Our systemmakes use of concreteness, sentiment and syntactic information present in thedocuments. We train a random forest model to identify gender and age of a document’sauthor. We report the evaluation results received by the shared task.
2016
Trattner Christoph, Kuśmierczyk Tomasz, Rokicki Markus, Herder Eelco
Plate and Prejudice: Gender Differences in Online Cooking
UMAP 2016, ACM, Halifax, NS, Canada , 2016
Konferenz
Historically, there have always been differences in how men andwomen cook or eat. The reasons for this gender divide have mostlygone in Western culture, but still there is qualitative and anecdotalevidence that men prefer heftier food, that women take care of everydaycooking, and that men cook to impress. In this paper, weshow that these differences can also quantitatively be observed in alarge dataset of almost 200 thousand members of an online recipecommunity. Further, we show that, using a set of 88 features, thegender of the cooks can be predicted with fairly good accuracy of75%, with preference for particular dishes, the use of spices andthe use of kitchen utensils being the strongest predictors. Finally,we show the positive impact of our results on online food reciperecommender systems that take gender information into account.
2016
Kern Roman, Klampfl Stefan, Rexha Andi
Identifying Referenced Text in ScientificPublications by Summarisation andClassification Techniques
BIRNDL 2016 Joint Workshop on Bibliometric-enhanced Information Retrieval and NLP for Digital Libraries, G. Cabanac, Muthu Kumar Chandrasekaran, Ingo Frommholz , Kokil Jaidka, Min-Yen Kan, Philipp Mayr, Dietmar Wolfram, ACM, New Jersey, USA, 2016
Konferenz
This report describes our contribution to the 2nd ComputationalLinguistics Scientific Document Summarization Shared Task (CLSciSumm2016), which asked to identify the relevant text span in a referencepaper that corresponds to a citation in another document that citesthis paper. We developed three different approaches based on summarisationand classification techniques. First, we applied a modified versionof an unsupervised summarisation technique, TextSentenceRank, to thereference document, which incorporates the similarity of sentences tothe citation on a textual level. Second, we employed classification to selectfrom candidates previously extracted through the original TextSentenceRankalgorithm. Third, we used unsupervised summarisation of therelevant sub-part of the document that was previously selected in a supervisedmanner.
2016
Gursch Heimo, Ziak Hermann, Kröll Mark, Kern Roman
Context-Driven Federated Recommendations for Knowledge Workers
Proceedings of the 17th European Conference on Knowledge Management (ECKM), Dr. Sandra Moffett and Dr. Brendan Galbraith, Academic Conferences and Publishing International Limited, Belfast, Northern Ireland, UK, 2016
Konferenz
Modern knowledge workers need to interact with a large number of different knowledge sources with restricted or public access. Knowledge workers are thus burdened with the need to familiarise and query each source separately. The EEXCESS (Enhancing Europe’s eXchange in Cultural Educational and Scientific reSources) project aims at developing a recommender system providing relevant and novel content to its users. Based on the user’s work context, the EEXCESS system can either automatically recommend useful content, or support users by providing a single user interface for a variety of knowledge sources. In the design process of the EEXCESS system, recommendation quality, scalability and security where the three most important criteria. This paper investigates the scalability aspect achieved by federated design of the EEXCESS recommender system. This means that, content in different sources is not replicated but its management is done in each source individually. Recommendations are generated based on the context describing the knowledge worker’s information need. Each source offers result candidates which are merged and re-ranked into a single result list. This merging is done in a vector representation space to achieve high recommendation quality. To ensure security, user credentials can be set individually by each user for each source. Hence, access to the sources can be granted and revoked for each user and source individually. The scalable architecture of the EEXCESS system handles up to 100 requests querying up to 10 sources in parallel without notable performance deterioration. The re-ranking and merging of results have a smaller influence on the system's responsiveness than the average source response rates. The EEXCESS recommender system offers a common entry point for knowledge workers to a variety of different sources with only marginally lower response times as the individual sources on their own. Hence, familiarisation with individual sources and their query language is not necessary.
2016
Rexha Andi, Dragoni Mauro, Kern Roman, Kröll Mark
An Information Retrieval Based Approach for Multilingual Ontology Matching
International Conference on Applications of Natural Language to Information Systems, Métais E., Meziane F., Saraee M., Sugumaran V., Vadera S. , Springer , Salford, UK, 2016
Konferenz
Ontology matching in a multilingual environment consists of finding alignments between ontologies modeled by using more than one language. Such a research topic combines traditional ontology matching algorithms with the use of multilingual resources, services, and capabilities for easing multilingual matching. In this paper, we present a multilingual ontology matching approach based on Information Retrieval (IR) techniques: ontologies are indexed through an inverted index algorithm and candidate matches are found by querying such indexes. We also exploit the hierarchical structure of the ontologies by adopting the PageRank algorithm for our system. The approaches have been evaluated using a set of domain-specific ontologies belonging to the agricultural and medical domain. We compare our results with existing systems following an evaluation strategy closely resembling a recommendation scenario. The version of our system using PageRank showed an increase in performance in our evaluations.
2016
Traub Matthias, Lacic Emanuel, Kowald Dominik, Kahr Martin, Lex Elisabeth
Need Help? Recommending Social Care Institutions
Workshop on Recommender Systems and Big Data Analytics co-located with i-know 2016 conference, RSBDA'16, ACM, Graz, 2016
Konferenz
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.
2016
Lacic Emanuel
Real-Time Recommendations in a Multi-Domain Environment
ACM Hypertext Doctoral Consortium, ACM, 2016
Konferenz
Recommender systems are acknowledged as an essential instru-
ment to support users in finding relevant information. However,
adapting to different domain specific data models is a challenge,
which many recommender frameworks neglect. Moreover, the ad-
vent of the big data era has posed the need for high scalability and
real-time processing of frequent data updates, and thus, has brought
new challenges for the recommender systems’ research community.
In this work, we show how different item, social and location data
features can be utilized and supported to provide real-time recom-
mendations. We further show how to process data updates online
and capture user’s real-time interest without recalculating recom-
mendations. The presented recommendation framework provides
a scalable and customizable architecture suited for providing real-
time recommendations to multiple domains. We further investigate
the impact of an increasing request load and show how the runtime
can be decreased by scaling the framework.
2016
Stanisavljevic Darko, Hasani-Mavriqi Ilire, Lex Elisabeth, Strohmaier M., Helic Denis
Semantic Stability in Wikipedia
Complex Networks and their Applications, Cherifi, H., Gaito, S., Quattrociocchi, W., Sala, A., Springer International Publishing AG, Cham, Switzerland, 2016
Konferenz
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.
2016
di Sciascio Maria Cecilia, Sabol Vedran, Veas Eduardo Enrique
Rank As You Go: User-Driven Exploration of Search Results
ACM IUI 2016, ACM New York, NY, USA ©201, New York, 2016
Konferenz
Whenever users engage in gathering and organizing new information, searching and browsing activities emerge at the core of the exploration process. As the process unfolds and new knowledge is acquired, interest drifts occur inevitably and need to be accounted for. Despite the advances in retrieval and recommender algorithms, real-world interfaces have remained largely unchanged: results are delivered in a relevance-ranked list. However, it quickly becomes cumbersome to reorganize resources along new interests, as any new search brings new results. We introduce uRank and investigate interactive methods for understanding, refining and reorganizing documents on-the-fly as information needs evolve. uRank includes views summarizing the contents of a recommendation set and interactive methods conveying the role of users' interests through a recommendation ranking. A formal evaluation showed that gathering items relevant to a particular topic of interest with uRank incurs in lower cognitive load compared to a traditional ranked list. A second study consisting in an ecological validation reports on usage patterns and usability of the various interaction techniques within a free, more natural setting.
