Representation Independent Analytics Over Structured Data

Database analytics algorithms leverage quantifiable structural properties of the data to predict interesting concepts and relationships. The same information, however, can be represented using many different structures and the structural properties observed over particular representations do not necessarily hold for alternative structures. Thus, there is no guarantee that current database analytics algorithms will still provide the correct insights, no matter what structures are chosen to organize the database. Because these algorithms tend to be highly effective over some choices of structure, such as that of the databases used to validate them, but not so effective with others, database analytics has largely remained the province of experts who can find the desired forms for these algorithms. We argue that in order to make database analytics usable, we should use or develop algorithms that are effective over a wide range of choices of structural organizations. We introduce the notion of representation independence, study its fundamental properties for a wide range of data analytics algorithms, and empirically analyze the amount of representation independence of some popular database analytics algorithms. Our results indicate that most algorithms are not generally representation independent and find the characteristics of more representation independent heuristics under certain representational shifts

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

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

Complaint-driven Training Data Debugging for Query 2.0

As the need for machine learning (ML) increases rapidly across all industry sectors, there is a significant interest among commercial database providers to support "Query 2.0", which integrates model inference into SQL queries. Debugging Query 2.0 is very challenging since an unexpected query result may be caused by the bugs in training data (e.g., wrong labels, corrupted features). In response, we propose Rain, a complaint-driven training data debugging system. Rain allows users to specify complaints over the query's intermediate or final output, and aims to return a minimum set of training examples so that if they were removed, the complaints would be resolved. To the best of our knowledge, we are the first to study this problem. A naive solution requires retraining an exponential number of ML models. We propose two novel heuristic approaches based on influence functions which both require linear retraining steps. We provide an in-depth analytical and empirical analysis of the two approaches and conduct extensive experiments to evaluate their effectiveness using four real-world datasets. Results show that Rain achieves the highest recall@k among all the baselines while still returns results interactively.Comment: Proceedings of the 2020 ACM SIGMOD International Conference on Management of Dat

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

Ranking for Scalable Information Extraction

Information extraction systems are complex software tools that discover structured information in natural language text. For instance, an information extraction system trained to extract tuples for an Occurs-in(Natural Disaster, Location) relation may extract the tuple from the sentence: "A tsunami swept the coast of Hawaii." Having information in structured form enables more sophisticated querying and data mining than what is possible over the natural language text. Unfortunately, information extraction is a time-consuming task. For example, a state-of-the-art information extraction system to extract Occurs-in tuples may take up to two hours to process only 1,000 text documents. Since document collections routinely contain millions of documents or more, improving the efficiency and scalability of the information extraction process over these collections is critical. As a significant step towards this goal, this dissertation presents approaches for (i) enabling the deployment of efficient information extraction systems and (ii) scaling the information extraction process to large volumes of text. To enable the deployment of efficient information extraction systems, we have developed two crucial building blocks for this task. As a first contribution, we have created REEL, a toolkit to easily implement, evaluate, and deploy full-fledged relation extraction systems. REEL, in contrast to existing toolkits, effectively modularizes the key components involved in relation extraction systems and can integrate other long-established text processing and machine learning toolkits. To define a relation extraction system for a new relation and text collection, users only need to specify the desired configuration, which makes REEL a powerful framework for both research and application building. As a second contribution, we have addressed the problem of building representative extraction task-specific document samples from collections, a step often required by approaches for efficient information extraction. Specifically, we devised fully automatic document sampling techniques for information extraction that can produce better-quality document samples than the state-of-the-art sampling strategies; furthermore, our techniques are substantially more efficient than the existing alternative approaches. To scale the information extraction process to large volumes of text, we have developed approaches that address the efficiency and scalability of the extraction process by focusing the extraction effort on the collections, documents, and sentences worth processing for a given extraction task. For collections, we have studied both (adaptations of) state-of-the art approaches for estimating the number of documents in a collection that lead to the extraction of tuples as well as information extraction-specific approaches. Using these estimations we can identify the collections worth processing and ignore the rest, for efficiency. For documents, we have developed an adaptive document ranking approach that relies on learning-to-rank techniques to prioritize the documents that are likely to produce tuples for an extraction task of choice. Our approach revises the (learned) ranking decisions periodically as the extraction process progresses and new characteristics of the useful documents are revealed. Finally, for sentences, we have developed an approach based on the sparse group selection problem that identifies sentences|modeled as groups of words|that best characterize the extraction task. Beyond identifying sentences worth processing, our approach aims at selecting sentences that lead to the extraction of unseen, novel tuples. Our approaches are lightweight and efficient, and dramatically improve the efficiency and scalability of the information extraction process. We can often complete the extraction task by focusing on just a very small fraction of the available text, namely, the text that contains relevant information for the extraction task at hand. Our approaches therefore constitute a substantial step towards efficient and scalable information extraction over large volumes of text