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Yes / Yes / The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing a colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion.

Dynamic query expansion is a method of automatically identifying terms relevant to a target domain based on an incomplete query input. With the explosive growth of online media, such tools are essential for efficient search result refining to track emerging themes in noisy, unstructured text streams. It's crucial for large-scale predictive analytics and decision-making, systems which use open source indicators to find meaningful information rapidly and accurately. The problems of information overload and semantic mismatch are systemic during the Information Retrieval (IR) tasks undertaken by such systems. In this dissertation, we develop approaches to dynamic query expansion algorithms that can help improve the efficacy of such systems using only a small set of seed queries and requires no training or labeled samples. We primarily investigate four significant problems related to the retrieval and assessment of event-related information, viz. (1) How can we adapt the query expansion process to support rank-based analysis when tracking a fixed set of entities? A scalable framework is essential to allow relative assessment of emerging themes such as airport threats. (2) What visual knowledge discovery framework to adopt that can incorporate users' feedback back into the search result refinement process? A crucial step to efficiently integrate real-time `situational awareness' when monitoring specific themes using open source indicators. (3) How can we contextualize query expansions? We focus on capturing semantic relatedness between a query and reference text so that it can quickly adapt to different target domains. (4) How can we synchronously perform knowledge discovery and characterization (unstructured to structured) during the retrieval process? We mainly aim to model high-order, relational aspects of event-related information from microblog texts. / Ph. D. / Analysis of real-time, social media can provide critical insights into ongoing societal events. Where consequences and implications of specific events include monetary losses, threats to critical infrastructure and national security, disruptions to daily life, and a potential to cause loss of life and physical property. It is imperative for developing good ‘ground truth’ to develop adequate data-driven information systems, i.e., an authoritative record of events reported in the media cataloged alongside important dimensions. Availability of high-quality ground truth events can support various analytic efforts, e.g., identifying precursors of attacks, developing predictive indicators using surrogate data sources, and tracking the progression of events over space and time. A dynamic search result refinement is useful for expanding a general set of user queries into a more relevant collection. The challenges of information overload and misalignment of context between the user query and retrieved results can overwhelm both human and machine. In this dissertation, we focus our efforts on these specific challenges. With the ever-increasing volume of user-generated data large-scale analysis is a tedious task. Our first focus is to develop a scalable model that dynamically tracks and ranks evolving topics as they appear in social media. Then to simplify the cognitive tasks involving sense-making of evolving themes, we take a visual approach to retrieve situationally critical and emergent information effectively. This visual analytics approach learns from user’s interactions during the exploratory process and then generates a better representation of the data. Thus, improving the situational understanding and usability of underlying data models. Such features are crucial for big-data based decision & support systems. To make the event-focused retrieval process more robust, we developed a context-rich procedure that adds new relevant key terms to the user’s original query by utilizing the linguistic structures in text. This context-awareness allows the algorithm to retrieve those relevant characteristics that can help users to gain adequate information from social media about real-world events. Online social commentary about events is very informal and can be incomplete. However, to get the complete picture and adequately describe these events we develop an approach that models the underlying relatedness of information and iteratively extract meaning and denotations from event-related texts. We learn how to express the high-order relationships between events and entities and group them to identify those attributes that best explain the events the user is trying to uncover. In all the augmentations we develop, our strategy is to allow only very minimal human supervision using just a small set of seed event triggers and requires no training or labeled samples. We show a comprehensive evaluation of these augmentations on real-world domains - threats on airports, cyber attacks, and protests. We also demonstrate their applicability as for real-time analysis that provides vital event characteristics, and contextually consistent information can be a beneficial aid for emergency responders.

Twitter has become a popular data source in the recent decade and garnered a significant amount of attention as a surrogate data source for many important forecasting problems. Strong correlations have been observed between Twitter indicators and real-world trends spanning elections, stock markets, book sales, and flu outbreaks. A key ingredient to all methods that use Twitter for forecasting is to agree on a domain-specific vocabulary to track the pertinent tweets, which is typically provided by subject matter experts (SMEs). The language used in Twitter drastically differs from other forms of online discourse, such as news articles and blogs. It constantly evolves over time as users adopt popular hashtags to express their opinions. Thus, the vocabulary used by forecasting algorithms needs to be dynamic in nature and should capture emerging trends over time. This thesis proposes a novel unsupervised learning algorithm that builds a dynamic vocabulary using Probabilistic Soft Logic (PSL), a framework for probabilistic reasoning over relational domains. Using eight presidential elections from Latin America, we show how our query expansion methodology improves the performance of traditional election forecasting algorithms. Through this approach we demonstrate how we can achieve close to a two-fold increase in the number of tweets retrieved for predictions and a 36.90% reduction in prediction error. / Master of Science

Probabilistic databases (PDBs) are usually incomplete, e.g., contain only the facts that have been extracted from the Web with high confidence. However, missing facts are often treated as being false, which leads to unintuitive results when querying PDBs. Recently, open-world probabilistic databases (OpenPDBs) were proposed to address this issue by allowing probabilities of unknown facts to take any value from a fixed probability interval. In this paper, we extend OpenPDBs by Datalog± ontologies, under which both upper and lower probabilities of queries become even more informative, enabling us to distinguish queries that were indistinguishable before. We show that the dichotomy between P and PP in (Open)PDBs can be lifted to the case of first-order rewritable positive programs (without negative constraints); and that the problem can become NP^PP-complete, once negative constraints are allowed. We also propose an approximating semantics that circumvents the increase in complexity caused by negative constraints.

In this paper, we explore how ontological knowledge expressed via existential rules can be combined with possibilistic networks (i) to represent qualitative preferences along with domain knowledge, and (ii) to realize preference-based answering of conjunctive queries (CQs). We call these combinations ontological possibilistic networks (OP-nets). We define skyline and k-rank answers to CQs under preferences and provide complexity (including data tractability) results for deciding consistency and CQ skyline membership for OP-nets. We show that our formalism has a lower complexity than a similar existing formalism.

Forming the foundations of large-scale knowledge bases, probabilistic databases have been widely studied in the literature. In particular, probabilistic query evaluation has been investigated intensively as a central inference mechanism. However, despite its power, query evaluation alone cannot extract all the relevant information encompassed in large-scale knowledge bases. To exploit this potential, we study two inference tasks; namely finding the most probable database and the most probable hypothesis for a given query. As natural counterparts of most probable explanations (MPE) and maximum a posteriori hypotheses (MAP) in probabilistic graphical models, they can be used in a variety of applications that involve prediction or diagnosis tasks. We investigate these problems relative to a variety of query languages, ranging from conjunctive queries to ontology-mediated queries, and provide a detailed complexity analysis.

Effective database management has become essential for modern organizations, especially to reduce costs while maintaining optimal performance. This project explores practical strategies to reduce response times, improve resource efficiency and improve data integrity in a database for the chemical management sector. The techniques include normalization, data type optimization, and query optimization using indexes. Tools like EXPLAIN are used to understand the optimizer's logic regarding the selection of scan types and how it can be influenced to make better decisions. MySqlSlap is used for load testing to verify the effects of the changes, such as reduced latency, improved memory management, and improved resource utilization.