27 research outputs found
Knowledge Search within a Company-WIKI
The usage of Wikis for the purpose of knowledge management within a business company is only of value if the stored information can be found easily. The fundamental characteristic of a Wiki, its easy and informal usage, results in large amounts of steadily changing, unstructured documents. The widely used full-text search often provides search results of insufficient accuracy. In this paper, we will present an approach likely to improve search quality, through the use of Semantic Web, Text Mining, and Case Based Reasoning (CBR) technologies. Search results are more precise and complete because, in contrast to full-text search, the proposed knowledge-based search operates on the semantic layer
Is Evaluating Visual Search Interfaces in Digital Libraries Still an Issue?
Although various visual interfaces for digital libraries have been developed
in prototypical systems, very few of these visual approaches have been
integrated into today's digital libraries. In this position paper we argue that
this is most likely due to the fact that the evaluation results of most visual
systems lack comparability. There is no fix standard on how to evaluate visual
interactive user interfaces. Therefore it is not possible to identify which
approach is more suitable for a certain context. We feel that the comparability
of evaluation results could be improved by building a common evaluation setup
consisting of a reference system, based on a standardized corpus with fixed
tasks and a panel for possible participants.Comment: 10 pages, 2 figures, LWA Workshop 201
Enhancing new user cold-start based on decision trees active learning by using past warm-users predictions
The cold-start is the situation in which the recommender
system has no or not enough information about the (new) users/items, i.e. their ratings/feedback; hence, the recommendations are not accurate. Active learning techniques for recommender systems propose to interact
with new users by asking them to rate sequentially a few items while the system tries to detect her preferences. This bootstraps recommender systems and alleviate the new user cold-start. Compared to current state of the art, the presented approach takes into account the users' ratings
predictions in addition to the available users' ratings. The experimentation shows that our approach achieves better performance in terms of precision and limits the number of questions asked to the users
Anomaly and event detection for unsupervised athlete performance data
There are many projects today where data is collected automatically to provide input for various data mining algorithms. A problem with freshly generated datasets is their unsupervised nature, leading to difficulty in fitting predictive algorithms without substantial manual effort. One of the first steps in dataset preparation and mining is anomaly detection, where clear anomalies and outliers as well as events or changes in the pattern of the data are identified as a precursor to subsequent steps in data mining. In the research presented here, we provide a multi-step anomaly detection process which utilises different combinations of algorithms for the most accurate identification of outliers and events
A framework for using social media channels in knowledge exchange with customers
Abstract. Social media channels become more and more important for service providers in contacting customers. Given the variety of offers it is important to understand the contribution of social media channels to knowledge exchange with customers. We analyse the requirements of customer contact in service provision and develop a framework how different social media channels can be used for knowledge exchange. In particular, we show from the perspective of service providers how these organisations may apply different social media channels in different stages of service processes
Discovering Complex Incomplete Periodic Patterns through Logi-cal Derivations
Abstract Discovering complex and incomplete periodic patterns in the logs of events is a complicated and time consuming task. This work shows that it is possible to discover complex and incomplete periodic patterns through finding simple patterns first and through logical derivations of complex and incomplete patterns later on. The paper defines a syntax and semantics of a class of periodic patterns that frequently occur in the logs of events. A system of derivation rules proposed in the paper can be used to transform a set of periodic patterns into a logically equivalent set of patterns. The rules are used in the algorithms that derive complex and incomplete periodic patterns. A prototype implementation of the algorithms that discover complex and incomplete periodic patterns in the logs of events is presented
Exploiting past usersâ interests and predictions in an active learning method for dealing with cold start in recommender systems
This paper focuses on the new users cold-start issue in the context of recommender systems. New users who do not receive pertinent recommendations may abandon the system. In order to cope with this issue, we use active learning techniques. These methods engage the new users to interact with the system by presenting them with a questionnaire that aims to understand their preferences
to the related items. In this paper, we propose an active learning technique that exploits past usersâ interests and past usersâ predictions in order to identify the best questions to ask. Our technique achieves a better performance in terms of precision (RMSE), which leads to learn the usersâ preferences in less questions. The experimentations were carried out in a small and public dataset to prove the applicability for handling cold start issues
Posted, Visited, Exported: Altmetrics in the Social Tagging System BibSonomy
In social tagging systems, like Mendeley, CiteULike, and BibSonomy, users can post, tag, visit, or export scholarly publications. In this paper, we compare citations with metrics derived from usersâ activities (altmetrics) in the popular social bookmarking system BibSonomy. Our analysis, using a corpus of more than 250,000 publications published before 2010, reveals that overall, citations and altmetrics in BibSonomy are mildly correlated. Furthermore, grouping publications by user-generated tags results in topic-homogeneous subsets that exhibit higher correlations with citations than the full corpus. We find that posts, exports, and visits of publications are correlated with citations and even bear predictive power over future impact. Machine learning classifiers predict whether the number of citations that a publication receives in a year exceeds the median number of citations in that year, based on the usage counts of the preceding year. In that setup, a Random Forest predictor outperforms the baseline on average by seven percentage points
Technology Selection for Big Data and Analytical Applications
The term Big Data has become pervasive in recent years, as smart phones, televisions, washing machines, refrigerators, smart meters, diverse sensors, eyeglasses, and even clothes connect to the Internet. However, their generated data is essentially worthless without appropriate data analytics that utilizes information retrieval, statistics, as well as various other techniques. As Big Data is commonly too big for a single person or institution to investigate, appropriate tools are being used that go way beyond a traditional data warehouse and that have been developed in recent years. Unfortunately, there is no single solution but a large variety of different tools, each of which with distinct functionalities, properties and characteristics. Especially small and medium-sized companies have a hard time to keep track, as this requires time, skills, money, and specific knowledge that, in combination, result in high entrance barriers for Big Data utilization. This paper aims to reduce these barriers by explaining and structuring different classes of technologies and the basic criteria for proper technology selection. It proposes a framework that guides especially small and mid-sized companies through a suitable selection process that can serve as a basis for further advances