Reasoning & Querying – State of the Art

Various query languages for Web and Semantic Web data, both for practical use and as an area of research in the scientific community, have emerged in recent years. At the same time, the broad adoption of the internet where keyword search is used in many applications, e.g. search engines, has familiarized casual users with using keyword queries to retrieve information on the internet. Unlike this easy-to-use querying, traditional query languages require knowledge of the language itself as well as of the data to be queried. Keyword-based query languages for XML and RDF bridge the gap between the two, aiming at enabling simple querying of semi-structured data, which is relevant e.g. in the context of the emerging Semantic Web. This article presents an overview of the field of keyword querying for XML and RDF

VISUAL ANALYTICS FOR OPEN-ENDED TASKS IN TEXT MINING

Overview of documents using topic modeling and multidimensional scaling is helpful in understanding topic distribution. While we can spot clusters visually, it is challenging to characterize them. My research investigates an interactive method to identify clusters by assigning attributes and examining the resulting distributions. ParallelSpaces examines the understanding of topic modeling applied to Yelp business reviews, where businesses and their reviews each constitute a separate visual space. Exploring these spaces enables the characterization of each space using the other. However, the scatterplot-based approach in ParallelSpaces does not generalize to categorical variables due to overplotting. My research proposes an improved layout algorithm for those cases in our follow-up work, Gatherplots, which eliminate overplotting in scatterplots while maintaining individual objects. Another limitation in clustering methods is the fixed number of clusters as a hyperparameter. TopicLens is a Magic Lens-type interaction technique, where the documents under the lens are clustered according to topics in real time. While ParallelSpaces help characterize the clusters, the attributes are sometimes limited. To extend the analysis by creating a custom mixture of attributes, CommentIQ is a comment moderation tool where moderators can adjust model parameters according to the context or goals. To help users analyze documents semantically, we develop a technique for user-driven text mining by building a dictionary for topics or concepts in a follow-up study, ConceptVector, which uses word embedding to generate dictionaries interactively and uses those dictionaries to analyze the documents. My dissertation contributes interactive methods to overview documents to integrate the user in text mining loops that currently are non-interactive. The case studies we present in this dissertation provide concrete and operational techniques for directly improving several state-of-the-art text mining algorithms. We summarize those generalizable lessons and discuss the limitations of the visual analytics approach

Toward Sustainable Recommendation Systems

Recommendation systems are ubiquitous, acting as an essential component in online platforms to help users discover items of interest. For example, streaming services rely on recommendation systems to serve high-quality informational and entertaining content to their users, and e-commerce platforms recommend interesting items to assist customers in making shopping decisions. Further-more, the algorithms and frameworks driving recommendation systems provide the foundation for new personalized machine learning methods that have wide-ranging impacts. While successful, many current recommendation systems are fundamentally not sustainable: they focus on short-lived engagement objectives, requiring constant fine-tuning to adapt to the dynamics of evolving systems, or are subject to performance degradation as users and items churn in the system. In this dissertation research, we seek to lay the foundations for a new class of sustainable recommendation systems. By sustainable, we mean a recommendation system should be fundamentally long-lived, while enhancing both current and future potential to connect users with interesting content. By building such sustainable recommendation systems, we can continuously improve the user experience and provide a long-lived foundation for ongoing engagement. Building on a large body of work in recommendation systems, with the advance in graph neural networks, and with recent success in meta-learning for ML-based models, this dissertation focuses on sustainability in recommendation systems from the following three perspectives with corresponding contributions: • Adaptivity: The first contribution lies in capturing the temporal effects from the instant shifting of users’ preferences to the lifelong evolution of users and items in real-world scenarios, leading to models which are highly adaptive to the temporal dynamics present in online platforms and provide improved item recommendation at different timestamps. • Resilience: Secondly, we seek to identify the elite users who act as the “backbone” recommendation systems shape the opinions of other users via their public activities. By investigating the correlation between user’s preference on item consumption and their connections to the “backbone”, we enable recommendation models to be resilient to dramatic changes including churn in new items and users, and frequently updated connections between users in online communities. • Robustness: Finally, we explore the design of a novel framework for “learning-to-adapt” to the imperfect test cases in recommendation systems ranging from cold-start users with few interactions to casual users with low activity levels. Such a model is robust to the imperfection in real-world environments, resulting in reliable recommendation to meet user needs and aspirations

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

Semantic Similarity of Spatial Scenes

The formalization of similarity in spatial information systems can unleash their functionality and contribute technology not only useful, but also desirable by broad groups of users. As a paradigm for information retrieval, similarity supersedes tedious querying techniques and unveils novel ways for user-system interaction by naturally supporting modalities such as speech and sketching. As a tool within the scope of a broader objective, it can facilitate such diverse tasks as data integration, landmark determination, and prediction making. This potential motivated the development of several similarity models within the geospatial and computer science communities. Despite the merit of these studies, their cognitive plausibility can be limited due to neglect of well-established psychological principles about properties and behaviors of similarity. Moreover, such approaches are typically guided by experience, intuition, and observation, thereby often relying on more narrow perspectives or restrictive assumptions that produce inflexible and incompatible measures. This thesis consolidates such fragmentary efforts and integrates them along with novel formalisms into a scalable, comprehensive, and cognitively-sensitive framework for similarity queries in spatial information systems. Three conceptually different similarity queries at the levels of attributes, objects, and scenes are distinguished. An analysis of the relationship between similarity and change provides a unifying basis for the approach and a theoretical foundation for measures satisfying important similarity properties such as asymmetry and context dependence. The classification of attributes into categories with common structural and cognitive characteristics drives the implementation of a small core of generic functions, able to perform any type of attribute value assessment. Appropriate techniques combine such atomic assessments to compute similarities at the object level and to handle more complex inquiries with multiple constraints. These techniques, along with a solid graph-theoretical methodology adapted to the particularities of the geospatial domain, provide the foundation for reasoning about scene similarity queries. Provisions are made so that all methods comply with major psychological findings about people’s perceptions of similarity. An experimental evaluation supplies the main result of this thesis, which separates psychological findings with a major impact on the results from those that can be safely incorporated into the framework through computationally simpler alternatives

Improving Deep Reinforcement Learning Using Graph Convolution and Visual Domain Transfer

Recent developments in Deep Reinforcement Learning (DRL) have shown tremendous progress in robotics control, Atari games, board games such as Go, etc. However, model free DRL still has limited use cases due to its poor sampling efficiency and generalization on a variety of tasks. In this thesis, two particular drawbacks of DRL are investigated: 1) the poor generalization abilities of model free DRL. More specifically, how to generalize an agent\u27s policy to unseen environments and generalize to task performance on different data representations (e.g. image based or graph based) 2) The reality gap issue in DRL. That is, how to effectively transfer a policy learned in a simulator to the real world. This thesis makes several novel contributions to the field of DRL which are outlined sequentially in the following. Among these contributions is the generalized value iteration network (GVIN) algorithm, which is an end-to-end neural network planning module extending the work of Value Iteration Networks (VIN). GVIN emulates the value iteration algorithm by using a novel graph convolution operator, which enables GVIN to learn and plan on irregular spatial graphs. Additionally, this thesis proposes three novel, differentiable kernels as graph convolution operators and shows that the embedding-based kernel achieves the best performance. Furthermore, an improvement upon traditional $n$-step $Q$-learning that stabilizes training for VIN and GVIN is demonstrated. Additionally, the equivalence between GVIN and graph neural networks is outlined and shown that GVIN can be further extended to address both control and inference problems. The final subject which falls under the graph domain that is studied in this thesis is graph embeddings. Specifically, this work studies a general graph embedding framework GEM-F that unifies most of the previous graph embedding algorithms. Based on the contributions made during the analysis of GEM-F, a novel algorithm called WarpMap which outperforms DeepWalk and node2vec in the unsupervised learning settings is proposed. The aforementioned reality gap in DRL prohibits a significant portion of research from reaching the real world setting. The latter part of this work studies and analyzes domain transfer techniques in an effort to bridge this gap. Typically, domain transfer in RL consists of representation transfer and policy transfer. In this work, the focus is on representation transfer for vision based applications. More specifically, aligning the feature representation from source domain to target domain in an unsupervised fashion. In this approach, a linear mapping function is considered to fuse modules that are trained in different domains. Proposed are two improved adversarial learning methods to enhance the training quality of the mapping function. Finally, the thesis demonstrates the effectiveness of domain alignment among different weather conditions in the CARLA autonomous driving simulator

Visualization of large amounts of multidimensional multivariate business-oriented data

Many large businesses store large amounts of business-oriented data in data warehouses. These data warehouses contain fact tables, which themselves contain rows representing business events, such as an individual sale or delivery. This data contains multiple dimensions (independent variables that are categorical) and very often also contains multiple measures (dépendent variables that are usually continuous), which makes it complex for casual business users to analyze and visualize. We propose two techniques, GPLOM and VisReduce, that respectively handle the visualization front-end of complex datasets and the back-end processing necessary to visualize large datasets. Scatterplot matrices (SPLOMs), parallel coordinates, and glyphs can all be used to visualize the multiple measures in multidimensional multivariate data. However, these techniques are not well suited to visualizing many dimensions. To visualize multiple dimensions, “hierarchical axes” that “stack dimensions” have been used in systems like Polaris and Tableau. However, this approach does not scale well beyond a small number of dimensions. Emerson et al. (2013) extend the matrix paradigm of the SPLOM to simultaneously visualize several categorical and continuous variables, displaying many kinds of charts in the matrix depending on the kinds of variables involved. We propose a variant of their technique, called the Generalized Plot Matrix (GPLOM). The GPLOM restricts Emerson et al. (2013)’s technique to only three kinds of charts (scatterplots for pairs of continuous variables, heatmaps for pairs of categorical variables, and barcharts for pairings of categorical and continuous variable), in an effort to make it easier to understand by casual business users. At the same time, the GPLOM extends Emerson et al. (2013)’s work by demonstrating interactive techniques suited to the matrix of charts. We discuss the visual design and interactive features of our GPLOM prototype, including a textual search feature allowing users to quickly locate values or variables by name. We also present a user study that compared performance with Tableau and our GPLOM prototype, that found that GPLOM is significantly faster in certain cases, and not significantly slower in other cases. Also, performance and responsiveness of visual analytics systems for exploratory data analysis of large datasets has been a long standing problem, which GPLOM also encounters. We propose a method called VisReduce that incrementally computes visualizations in a distributed fashion by combining a modified MapReduce-style algorithm with a compressed columnar data store, resulting in significant improvements in performance and responsiveness for constructing commonly encountered information visualizations, e.g., bar charts, scatterplots, heat maps, cartograms and parallel coordinate plots. We compare our method with one that queries three other readily available database and data warehouse systems — PostgreSQL, Cloudera Impala and the MapReduce-based Apache Hive — in order to build visualizations. We show that VisReduce’s end-to-end approach allows for greater speed and guaranteed end-user responsiveness, even in the face of large, long-running queries