Community Question Answering (CQA) websites provide a rapidly growing source of information in many areas. This rapid growth, while offering new opportunities, puts forward new challenges. In most CQA implementations there is little effort in directing new questions to the right group of experts. This means that experts are not provided with questions matching their expertise, and therefore new matching questions may be missed and not receive a proper answer. We focus on finding experts for a newly posted question. We investigate the suitability of two statistical topic models for solving this issue and compare these methods against more traditional Information Retrieval approaches. We show that for a dataset constructed from the Stackoverflow website, these topic models outperform other methods in retrieving a candidate set of best experts for a question. We also show that the Segmented Topic Model gives consistently better performance compared to the Latent Dirichlet Allocation Model.
Outliers are anomalous and interesting objects that are notably different from the rest of the data. The outlier detection task has sometimes been considered as removing noise from the data. However, it is usually the significantly interesting deviations that are of most interest. Different outlier detection techniques work with various data formats. The outlier detection process needs to be sensitive to the nature of the underlying data. Most of the previous work on outlier detection was designed for propositional data. This dissertation focuses on developing outlier detection methods for structured data, more specifically object-relational data. Object-relational data can be viewed as a heterogeneous network with different classes of objects and links. We develop two new approaches to unsupervised outlier detection; both approaches leverage the statistical information obtained from a statistical-relational model. The first method develops a propositionalization approach to summarize information from object-relational data in a single data table. We use Markov Logic Network (MLN) structure learning to construct the features for the single data table and to mitigate the loss of information that usually happens when features are generated by manual aggregation. By using propositionalization as a pipeline, we can apply many previous outlier detection methods that were designed for single-table data. Our second outlier detection method ranks the objects as potential outliers in an object-oriented data model. Our key idea is to compare the feature distribution of a potential outlier object with the feature distribution of the objects class. We introduce a novel distribution divergence concept that is suitable for outlier detection. Our methods are validated on synthetic datasets and on real-world data sets about soccer matches and movies. iii To Ali, and to my parents iv "I wish I had an answer to that question because I'm tired of answering that question".
This paper is based on a previous publication [29]. Our work extends exception mining and outlier detection to the case of object-relational data. Object-relational data represent a complex heterogeneous network [12], which comprises objects of different types, links among these objects, also of different types, and attributes of these links. This special structure prohibits a direct vectorial data representation. We follow the well-established Exceptional Model Mining framework, which leverages machine learning models for exception mining: A object is exceptional to the extent that a model learned for the object data differs from a model learned for the general population. Exceptional objects can be viewed as outliers. We apply stateof-the-art probabilistic modelling techniques for object-relational data that construct a graphical model (Bayesian network), which compactly represents probabilistic associations in the data. A new metric, derived from the learned object-relational model, quantifies the extent to which the individual association pattern of a potential outlier deviates from that of the whole population. The metric is based on the likelihood ratio of two parameter vectors: One that represents the population associations, and another that represents the individual associations. Our method is validated on synthetic datasets and on real-world data sets about soccer matches and movies. Compared to baseline methods, our novel transformed likelihood ratio achieved the best detection accuracy on all datasets.
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