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Knowledge representation languages often only define a fixed set of features for modelling knowledge and do not offer ways for extending this set of features. However, the provided features do not always lend themselves to capture complex conceptual models. As a result, one often has to combine available features in ingenious ways to solve non-trivial modelling problems. To communicate the purpose of such combinations in an intention revealing manner, it would be advantageous to encapsulate and structure them in terms of higher-level modelling constructs. This motivates the introduction of high-level languages for knowledge representation languages. In analogy to high-level programming languages that abstract over low-level features of machine languages, high-level modelling languages abstract over low-level features of knowledge representation languages. In particular, recurring modelling solutions, that combine low-level language features in some specific manner can be specified as reusable patterns in a high-level modelling language. This thesis is about such patterns and regularities in ontologies. Its primary focus is the analysis of patterns and regularities in terms of formally specified qualities and properties. This formal treatment provides the foundation for precise statements and hypotheses that can be verified and tested empirically. Frameworks, methodologies, and techniques for detecting, discovering, and encoding patterns and regularities in ontologies are presented. They are used to drive large-scale empirical investigations on patterns and regularities in biomedical ontologies. These investigations suggest that many ontologies follow a complex design based on highly interrelated patterns and regularities. The automated identification of such patterns and regularities as well as the analysis of their interrelations open up possibilities for pattern-based services that have a great potential for improving ontology comprehension and maintenance in practice.

In computer science, an ontology is a machine-processable representation of knowledge about some domain. Ontologies are encoded in ontology languages, such as the Web Ontology Language (OWL) based on Description Logics (DLs). An ontology is a set of logical statements, called axioms. Some axioms make universal statements, e.g. all fathers are men, while others record data, i.e. facts about specific individuals, e.g. Bob is a father. A set of universal statements is called TBox, as it encodes terminology, i.e. schema-level conceptual relationships, and a set of facts is called ABox, as it encodes instance-level assertions. Ontologies are extensively developed and widely used in domains such as biology and medicine. Manual engineering of a TBox is a difficult task that includes modelling conceptual relationships of the domain and encoding those relationships in the ontology language, e.g. OWL. Hence, it requires the knowledge of domain experts and skills of ontology engineers combined together. In order to assist engineering of TBoxes and potentially automate it, acquisition (or induction) of axioms from data has attracted research attention and is usually called Ontology Learning (OL). This thesis investigates the problem of OL from general principles. We formulate it as General Terminology Induction that aims at acquiring general, expressive TBox axioms (called general terminology) from data. The thesis addresses and investigates in depth two main questions: how to rigorously evaluate the quality of general TBox axioms and how to efficiently construct them. We design an approach for General Terminology Induction and implement it in an algorithm called DL-Miner. We extensively evaluate DL-Miner, compare it with other approaches, and run case studies together with domain experts to gain insight into its potential applications. The thesis should be of interest to ontology developers seeking automated means to facilitate building or enriching ontologies. In addition, as our experiments show, DL-Miner can deliver valuable insights into the data, i.e. can be useful for data analysis and debugging.

Classification is a core reasoning service provided by most OWL reasoners. Classification in general is hard - up to 2NExptime for SROIQ(D), the Description Logic which is underpinning the Web Ontology Language (OWL). While it has been shown that classification is practical for a wide range of inputs, there are still ontologies for which classification takes an unreasonable amount of time for purposes such as ontology engineering (frequent classifications after updates). A natural optimisation strategy is divide and conquer, that is, to decompose the ontology into subsets which are hopefully easier to classify and whose classifications can be combined into a complete classification of the whole ontology. Unfortunately, an arbitrary subset may not be self-contained, i.e. it might be missing information that is needed to determine entailments over its signature. Moreover, such a subset can be potentially harder to classify than the whole ontology. In order to mitigate those problems, classification preserving decompositions (CPDs) must be designed with care that they support complete classification which is, in practice, more efficient than monolithic classification. Locality-based modules are subsets of an ontology that provide certain guarantees with respect to the entities (concepts, roles) in its signature - in particular, modules are self-contained. In this thesis we explore the use of syntactic locality-based modules for underpinning classification-preserving decompositions. In particular, we empirically explore their potential to avoid subsumption tests and reduce subsumption test hardness and weigh those benefits against detrimental effects such as overhead (for example the time it takes to compute the decomposition) and redundancy (a consequence of potentially overlapping chunks in the decomposition). The main contributions of this thesis are an in-depth empirical characterisation of these effects, an extensible framework for observing CPDs in action up until a granularity of individual subsumption tests, a large, public corpus of observations and its analysis and insights on experimental methodologies around OWL reasoning.

Drug repositioning is the discovery of new indications for approved or failed drugs. This practice is commonly done within the drug discovery process in order to adjust or expand the application line of an active molecule. Nowadays, an increasing number of computational methodologies aim at predicting repositioning opportunities in an automated fashion. Some approaches rely on the direct physical interaction between molecules and protein targets (docking) and some methods consider more abstract descriptors, such as a gene expression signature, in order to characterise the potential pharmacological action of a drug (Chapter 1). On a fundamental level, repositioning opportunities exist because drugs perturb multiple biological entities, (on and off-targets) themselves involved in multiple biological processes. Therefore, a drug can play multiple roles or exhibit various mode of actions responsible for its pharmacology. The work done for my thesis aims at characterising these various modes and mechanisms of action for approved drugs, using a mathematical framework called description logics. In this regard, I first specify how living organisms can be compared to complex black box machines and how this analogy can help to capture biomedical knowledge using description logics (Chapter 2). Secondly, the theory is implemented in the Functional Therapeutic Chemical Classification System (FTC - https://www.ebi.ac.uk/chembl/ftc/), a resource defining over 20,000 new categories representing the modes and mechanisms of action of approved drugs. The FTC also indexes over 1,000 approved drugs, which have been classified into the mode of action categories using automated reasoning. The FTC is evaluated against a gold standard, the Anatomical Therapeutic Chemical Classification System (ATC), in order to characterise its quality and content (Chapter 3). Finally, from the information available in the FTC, a series of drug repositioning hypotheses were generated and made publicly available via a web application (https://www.ebi.ac.uk/chembl/research/ftc-hypotheses). A subset of the hypotheses related to the cardiovascular hypertension as well as for Alzheimer’s disease are further discussed in more details, as an example of an application (Chapter 4). The work performed illustrates how new valuable biomedical knowledge can be automatically generated by integrating and leveraging the content of publicly available resources using description logics and automated reasoning. The newly created classification (FTC) is a first attempt to formally and systematically characterise the function or role of approved drugs using the concept of mode of action. The open hypotheses derived from the resource are available to the community to analyse and design further experiments.

With the growing popularity of the Web Ontology Language (OWL) as a logic-based ontology language, as well as advancements in the language itself, the need for more sophisticated and up-to-date ontology engineering services increases as well. While, for instance, there is active focus on new reasoners and optimisations, other services fall short of advancing at the same rate (it suffices to compare the number of freely-available reasoners with ontology editors). In particular, very little is understood about how ontologies evolve over time, and how reasoners’ performance varies as the input changes. Given the evolving nature of ontologies, detecting and presenting changes (via a so-called diff) between them is an essential engineering service, especially for version control systems or to support change analysis. In this thesis we address the diff problem for description logic (DL) based ontologies, specifically OWL 2 DL ontologies based on the SROIQ DL. The outcomes are novel algorithms employing both syntactic and semantic techniques to, firstly, detect axiom changes, and what terms had their meaning affected between ontologies, secondly, categorise their impact (for example, determining that an axiom is a stronger version of another), and finally, align changes appropriately, i.e., align source and target of axiom changes (so the stronger axiom with the weaker one, from our example), and axioms with the terms they affect. Subsequently, we present a theory of reasoner performance heterogeneity, based on field observations related to reasoner performance variability phenomena. Our hypothesis is that there exist two kinds of performance behaviour: an ontology/reasoner combination can be performance-homogeneous or performance-heterogeneous. Finally, we verify that performance-heterogeneous reasoner/ontology combinations contain small, performance-degrading sets of axioms, which we call hot spots. We devise a performance hot spot finding technique, and show that hot spots provide a promising basis for engineering efficient reasoners.

The Web Ontology Language OWL is based on the highly expressive description logic SROIQ, which allows OWL ontology users to employ out-of-the-box reasoners to compute information that is not only explicitly asserted, but entailed by the ontology. Explanation facilities for entailments of OWL ontologies form an essential part of ontology development tools, as they support users in detecting and repairing errors in potentially large and highly complex ontologies, thus helping to ensure ontology quality. Justifications, minimal subsets of an ontology that are sufficient for an entailment to hold, are currently the prevalent form of explanation in OWL ontology development tools. They have been found to significantly reduce the time and effort required to debug erroneous entailments. A large number of entailments, however, have not only one but many justifications, which can make it considerably more challenging for a user to find a suitable repair for the entailment.In this thesis, we investigate the relationships between multiple justifications for both single and multiple entailments, with the goal of exploiting this justificatory structure in order to devise new coping strategies for multiple justifications. We describe various aspects of the justificatory structure of OWL ontologies, such as shared axiom cores and structural similarities. We introduce a model for measuring user effort in the debugging process and propose debugging strategies that exploit the justificatory structure in order to reduce user effort. Finally, an analysis of a large corpus of ontologies from the biomedical domain reveals that OWL ontologies used in practice frequently exhibit a rich justificatory structure.

Ontologies are machine processable artifacts and the core structures of the Semantic Web. OWL (Web Ontology Language) is a W3C Recommendation language for developing ontologies; it is based on Description Logics, allowing for precise knowledge representation and sound and complete automated reasoning over the collection of axioms in an OWL document. Although ontologies are useful for sharing terminologies, their design and reuse are difficult and time consuming processes. Despite the efforts of the community towards the development of OWL ontologies, there is a lack of methods and tools for reusing and inspecting ontologies, i.e., reverse engineering methods. This thesis focuses on the area by investigating the detection of regularities in ontologies, for the purpose of abstracting sets of axioms into patterns that can be verified and reused. Its main contribution is the Regularity Inspector for Ontologies (RIO) framework, which implements methods to find syntactic regularities (repetitive structures in the asserted axioms) and semantic regularities (repetitive structures in the entailments) in an ontology. Regularity detection is achieved through the use of cluster analysis for detecting similarities in sets of axioms. This thesis provides experimental evidence for the effectiveness of regularity analysis for the inspection of patterns, and the discovery of modeling irregularities (often modelling errors) during quality assurance for real, large ontologies. In particular, empirical analysis showed that RIO could successfully detect regularities in ontologies, revealing the patterns adopted by the developers. It can be also used to trace pattern deviations as part of checking conformance to an intended design template during quality assurance of an ontology. This work has been motivated by the existence of pattern based systematic development methodologies and the lack of methods for discovering patterns in existing ontologies --- the natural complement of these pattern based development methodologies.

Modem construction projects, characterised by severe fragmentation from both geographical and disciplinary perspectives, require accurate and timely sharing of information. Traditional information management systems operate on a textual basis and do not always consider the meaning of information. Current web-based information management technology supports information communication to a reasonable extent but still has many limitations, such as the lack of semanticawareness and poor interoperability of software applications. This research argues that Semantic Web technologies can enhance the efficiency of information management in construction projects by providing content-based and contextspecific information to project team members, and supporting the interoperation between independent applications. A Semantic Web-based Information Management System (Sams) for construction projects was created to demonstrate the above concept. The approach adopted for this research involved creating a new framework for Semantic Web-based information management. This extensible system framework enables the system to merge diverse construction information sources, ontologies and end-user applications into the overall Semantic Web environment. The semantic components developed in this research included a project document's annotation model, a project partner's user profile model, and several lightweight IFC-based ontologies for documented information management. This supports intelligent information management and interoperation between heterogeneous information sources and applications. The system framework, prototype annotations, and ontologies were applied to a concept demonstrator that illustrated how the project documents were annotated, accessed, converted, categorised, and retrieved on the basis of content and context. The demonstrator (named SwiMS) acts as a middleware, which mediates between user needs and the information sources. Information in project partners' documents were mapped and accessed intelligently. This involved the use of rule-based filtering and thus prevented the users from being overwhelmed by irrelevant documents or missing relevant ones in heterogeneous and distributed information sources. It also enabled the adaptation of documents to individual contexts and preferences, and the dynamic composition of various document management services. Evaluation of the system framework and demonstrator revealed that the system enhances the efficiency of construction information management, with the three most beneficial areas being project knowledge management, collaborative design and communication between project team members. The Swims annotations, ontologies and deductive rules are important technologies provide an innovative approach to managing construction information. These enable the information in construction documents, both structured documents and un-structured documents, to be interpretable by computers. This ensures the efficiency and precision of construction information management.

Owl pellet dissection can reveal important information about owl diet and the organisms found in the owls’ hunting grounds. This method only works when identifiable bones are found in the pellet. Benamane et al. (2019) used eDNA techniques to identify the organisms owls consumed based off of genomic evidence. Because of the differences between the study location and central Ohio, a method validation study was performed. Owl pellets were collected and dissected. Skulls were identified. eDNA analysis was run on the same pellets, to compare results. No mammalian DNA was able to be amplified via PCR after extraction. This method seems to struggle in imperfect conditions. Further studies, with better handling and storage, should be conducted to investigate this issue further.

For the purpose of this review, viral marketing is explained as the application of five connected components: Web 2.0, social media, virality, memes and marketing itself. In essence, memes disseminate virally through social media networks on the internet in ways that marketers can harness to reach consumers. But viral marketing is much more than creating funny image macros and hoping they go viral. The relevant components and their roles in viral marketing will be defined and explained in subsequent sections.The second portion of this paper will be dedicated to two brands that utilize viral marketing in intelligent ways. While the two topics of the case studies, Fortnite and Overwatch League, are very different in their approaches to viral marketing, both harness the power of viral marketing’s five components. While Fortnite utilizes a far-reaching approach, Overwatch League focuses on building a community that, to the members, feels closely-knit. Both strategies are proving successful for the brands and provide ample, vivid examples for how each component of viral marketing can be enacted. For Fortnite, this includes embracing user-generated content and partnering with EDM artist Marshmello. For Overwatch League, this means closing the gap between players and fans. Both unique strategies have earned their place in this paper for their understanding and implementation of viral marketing.Ultimately, my goal is to show that viral marketing is a legitimate strategy for the future and that there are pioneers in this moment achieving incredible feats through the approach.

Studying movement ecology is important not only in understanding the distribution of a species, but in understanding the magnitude of migration through certain regions, as well as explaining regional differences in demographics. The Northern Saw-whet Owl (Aegolius acadicus) is a small, migratory forest owl found throughout much of North America. Despite being captured widely during fall migration, the species’ movement ecology is poorly understood. Exploratory studies outside the saw-whet owl’s normal range have successfully captured the species during fall migration. In the Ozark Highlands ecoregion of the central United States, their status has been considered vagrant during fall and winter. Since 2010, saw-whet owls have been captured successfully in the region, historically considered south of their normal range. We sought to assess fall migration and demographics of 412 saw-whet owls captured at four study sites in the Ozark Highlands of Missouri, northern Arkansas, and eastern Oklahoma. Saw-whet owls were captured from mid-October to early-December in the study region. Capture rate varied by latitude and among sites. Demography of owls travelling through the region varied by migration type, with an increase in captures and proportion of hatch-year individuals during irruption years. In addition to likely being a regular fall migrant through the Ozarks, we documented saw-whet owls wintering in the southwestern Ozarks. During autumn migration of 2016 and 2017, we deployed 27 radio transmitters on saw-whet owls captured at our Arkansas banding site. Detections were obtained from 17 tagged individuals during the following winters. Saw-whet owls remained in the region from 1 to 112 days after release, suggesting the species winters to some extent in northwestern Arkansas. Based on assessment of landscape and habitat variables at diurnal roost sites, the species seems to prefer open shortleaf pine (Pinus echinata) habitat. Further study is required to determine the full extent of the saw-whet owl’s winter range; however, ample pine forest and cedar glades could provide optimal wintering habitat throughout the Ozark Highlands.

The processing of structured and semi-structured content on the Web has been gaining attention with the rapid progress in the Linking Open Data project and the development of commercial knowledge graphs. Knowledge graphs capture domain-specific or encyclopedic knowledge in the form of a data layer and add rich and explicit semantics on top of the data layer to infer additional knowledge. The data layer of a knowledge graph represents entities and their descriptions. The semantic layer on top of the data layer is called the schema (ontology), where relationships of the entity descriptions, their classes, and the hierarchy of the relationships and classes are defined. Today, there exist large knowledge graphs in the research community (e.g., encyclopedic datasets like DBpedia and Yago) and corporate world (e.g., Google knowledge graph) that encapsulate a large amount of knowledge for human and machine consumption. Typically, they consist of millions of entities and billions of facts describing these entities. While it is good to have this much knowledge available on the Web for consumption, it leads to information overload, and hence proper summarization (and presentation) techniques need to be explored. In this dissertation, we focus on creating both \textit{comprehensive} and \textit{concise} entity summaries at: (i) the single entity level and (ii) the multiple entity level. To summarize a single entity, we propose a novel approach called FACeted Entity Summarization (FACES) that considers importance, which is computed by combining popularity and uniqueness, and diversity of facts getting selected for the summary. We first conceptually group facts using semantic expansion and hierarchical incremental clustering techniques and form facets (i.e., groupings) that go beyond syntactic similarity. Then we rank both the facts and facets using Information Retrieval (IR) ranking techniques to pick the highest ranked facts from these facets for the summary. The important and unique contribution of this approach is that because of its generation of facets, it adds diversity into entity summaries, making them comprehensive. For creating multiple entity summaries, we propose RElatedness-based Multi-Entity Summarization (REMES) approach that simultaneously processes facts belonging to the given entities using combinatorial optimization techniques. In this process, we maximize diversity and importance of facts within each entity summary and relatedness of facts between the entity summaries. The proposed approach uniquely combines semantic expansion, graph-based relatedness, and combinatorial optimization techniques to generate relatedness-based multi-entity summaries. Complementing the entity summarization approaches, we introduce a novel approach using light Natural Language Processing (NLP) techniques to enrich knowledge graphs by adding type semantics to literals. This makes datatype properties semantically rich compared to having only implementation types. As a result of the enrichment process, we could use both object and datatype properties in the entity summaries, which improves coverage. Moreover, the added type semantics can be useful in other applications like dataset profiling and data integration. We evaluate the proposed approaches against the state-of-the-art methods and highlight their capabilities for single and multiple entity summarization.

This thesis entitled A Performance Analysis Framework for Coreference Resolution Algorithms, focuses on the topic of coreference resolution of semantic datasets. In order for Big Data analytics to be effective, it is essential to develop automated algorithms capable of integrating multiple datasets that contain data about a particular person or other entity. Accomplishing this necessitates coreference resolution; for example, determining that J. Doe in one dataset refers to the same person as Jonathan Doe Jr. in another dataset. There are many existing coreference resolution algorithms, but there are only a few basic design decisions to be made by such systems when it comes to how to compare two individual instances. An analysis framework is presented that assesses the impact of different choices for these design decisions on coreference resolution in terms of precision, recall, and F-measure.

In this thesis, an ontology for the geological mapping domain is constructed using the Protégé ontology editor. The Geological Mapping ontology is developed using terms and relationships, and their properties, as they relate to creating a geologic map. This vocabulary is semantically modeled in the ontology using Web Ontology Language (OWL). The purpose of this thesis is to exemplify how an ontology can be designed and developed to represent geological knowledge as it relates to mapping.

Data integration is concerned with providing a unified access to data residing at multiple sources. Such a unified access is realized by having a global schema and a set of mappings between the global schema and the local schemas of each data source, which specify how user queries at the global schema can be translated into queries at the local schemas. Data sources are typically developed and maintained independently, and thus, highly heterogeneous. This causes difficulties in integration because of the lack of interoperability in the aspect of architecture, data format, as well as syntax and semantics of the data.This dissertation represents a study on how small, self-contained ontologies, called ontology design patterns, can be employed to provide semantic interoperability in a cross-repository data integration system. The idea of this so-called ontology pattern- based data integration is that a collection of ontology design patterns can act as the global schema that still contains sufficient semantics, but is also flexible and simple enough to be used by linked data providers. On the one side, this differs from existing ontology-based solutions, which are based on large, monolithic ontologies that provide very rich semantics, but enforce too restrictive ontological choices, hence are shunned by many data providers. On the other side, this also differs from the purely linked data based solutions, which do offer simplicity and flexibility in data publishing, but too little in terms of semantic interoperability.We demonstrate the feasibility of this idea through the actual development of a large scale data integration project involving seven ocean science data repositories from five institutions in the U.S. In addition, we make two contributions as part of this dissertation work, which also play crucial roles in the aforementioned data integration project. First, we develop a collection of more than a dozen ontology design patterns that capture the key notions in the ocean science occurring in the participating data repositories. These patterns contain axiomatization of the key notions and were developed with an intensive involvement from the domain experts. Modeling of the patterns was done in a systematic workflow to ensure modularity, reusability, and flexibility of the whole pattern collection. Second, we propose the so-called pattern views that allow data providers to publish their data in very simple intermediate schema and show that they can greatly assist data providers to publish their data without requiring a thorough understanding of the axiomatization of the patterns.

With increasing data volume, automatically sharing data becomes more valuable because it decreases storage requirements by reducing data replication. Database integration is an important problem to address in current real world applications. The schema integration problem is as follows: given a group of schemas, the goal is to create a merged schema that represents all of the underlying schemas. The construction of the unified schema should not result in loss of information, and it should grant the capability to query the underlying databases either individually or collectively. Integration of multiple heterogeneous data sources usually includes an initial step to combine the schemas of the sources so that they form a unified view, which can be used to give users the illusion (and simplicity) of interacting with one single target combined from all the sources. Users are presented with a uniform logical representation of data that is physically spread over heterogeneous data sources. For this, an integrated schema) has to be created that incorporates all the data contents of the sources. Although there have been some projects on integration of the relational schemas but there is no universal tool for achieving the schema integration using ontologies.In this work, we propose an approach and develop a tool to achieve relational database schema integration using ontologies. Our focus is on improving the automation for integrating relational database schemas by using ontologies. An ontology represents a shared, explicit specification of a conceptualization of the domain of knowledge [G93]. Our schema integration approach is a combination of three different approaches that are adapted from previous research efforts. First we convert relations in the schema into their respective ontologies written in the ontology language OWL. After creating individual ontologies, we then merge them using ontology merging techniques to obtain a unified single ontology. Finally, the unified ontology is converted into a relational database schema. We develop a semi-automated tool that takes two or more relational schemas as input and converts them into their respective ontologies using pre-defined mapping rules. These ontologies are merged into a single unified ontology and we convert this merged ontology into a relational schema. Our approach is demonstrated using two example data sources and our integration tool RIO. Finally, we evaluate the quality of the merged schemas using the approach proposed by Pavlic et al. [PK+08].

Hydrogeologists collect data through studies that originate from a diverse and growing set of instruments that measure, for example, geochemical constituents of surface and groundwater. Databases store and publish the collected data on the Web, and the volume of data is quickly increasing, which makes accessing data problematic and time consuming for individuals. One way to overcome this problem is to develop ontology to formally and explicitly represent the domain (e.g., contaminant hydrogeology) knowledge. Using OWL and RDF, contaminant hydrogeology ontology (CHO) is developed to manage hydrological spatial data for Georgia, USA. CHO is a conceptual computer model for the contaminant hydrogeology domain in which concepts (e.g. contaminant, aquifer) and their relationships (e.g. pollutes) are formerly and explicitly defined. Cyberinfrastructure for exposing CHO and datasets (i.e., CHKb) as Linked Data on the Web is developed. Cyberinfrastructure consists of storing, managing, querying, and visualizing CHKb that can be accessed from URL: cho.gsu.edu.

As an expressive knowledge representation language for Semantic Web, Web Ontology Language (OWL) plays an important role in areas like science and commerce. The problem of maintaining integrity constraints arises because OWL employs the Open World Assumption (OWA) as well as the Non-Unique Name Assumption (NUNA). These assumptions are typically suitable for representing knowledge distributed across the Web, where the complete knowledge about a domain cannot be assumed, but make it challenging to use OWL itself for closed world integrity constraint validation. Integrity constraints (ICs) on ontologies have to be enforced; otherwise conflicting results would be derivable from the same knowledge base (KB). The current trends of incorporating ICs into OWL are based on its query language SPARQL, alternative semantics, or logic programming. These methods usually suffer from limited types of constraints they can handle, and/or inherited computational expensiveness. This dissertation presents a comprehensive and efficient approach to maintaining integrity constraints. The design enforces data consistency throughout the OWL life cycle, including the processes of OWL generation, maintenance, and interactions with other ontologies. For OWL generation, the Paraconsistent model is used to maintain integrity constraints during the relational database to OWL translation process. Then a new rule-based language with set extension is introduced as a platform to allow users to specify constraints, along with a demonstration of 18 commonly used constraints written in this language. In addition, a new constraint maintenance system, called Jena2Drools, is proposed and implemented, to show its effectiveness and efficiency. To further handle inconsistencies among multiple distributed ontologies, this work constructs a framework to break down global constraints into several sub-constraints for efficient parallel validation.

The terms Semantic Web and OWL are relatively new and growing concepts in the World Wide Web. Because these concepts are so new there are relatively few applications and/or tools for utilizing the potential power of this new concept. Although there are many components to the Semantic Web, this thesis will focus on the research question, "How do we go about developing a web crawler for the Semantic Web that locates and retrieves OWL documents." Specifically for this thesis, we hypothesize that by giving URIs to OWL documents, including all URIs from within these OWL documents, priority over other types of references, then we will locate more OWL documents than by any other type of traversal. We reason that OWL documents have proportionally more references to other OWL documents than non-OWL documents do, so that by giving them priority we should have located more OWL files when the crawl terminates, than by any other traversal method.In order to develop such an OWL priority queue, we needed to develop some heuristics to predict OWL documents during real-time parsing of Semantic Web documents. These heuristics are based on filename extensions and OWL language constructs, which are not absolute when predicting a document type before retrieval. However, if our reasoning is correct, then URIs found in an OWL document will likely lead to more OWL documents, such that when the crawl ends because of reaching a maximum document limit, we will have retrieved more OWL documents than by other methods such as breadth-first or load-balanced. We conclude our research with an evaluation of our results to test the validity of our hypothesis and to see if it is worthy of future research.

ABSTRACT The availability of useful information for research strongly depends on well structured relationships between consistently defined concepts (terms) in that domain. This can be achieved through ontologies. Ontologies are models of the knowledge of specific domain such as petroleum geology, in a computer understandable format. Knowledge is a collection of facts. Facts are represented by RDF triples (subject-predicate-object). A domain ontology is therefore a collection of many RDF triples, which represent facts of that domain. The SWEET ontologies are upper or top-level ontologies (foundation ontologies) consisting of thousands of very general concepts. These concepts are obtained from of Earth System science and include other related concepts. The work in this thesis deals with scientific knowledge representation in which the SWEET ontologies are extended to include wider, more specific and specialized concepts used in Petroleum Geology. Thus Petroleum Geology knowledge modeling is presented in this thesis.

In recent years, there has been a tremendous increase in the number of data transfer (information exchange) applications on mobile devices that form the bases for new research domains. Distributed information retrieval is one of the most prominent ways for building these data transfer applications. Despite a rich list of new characteristics and advantages, these new applications particularly for mobile devices, bring a variety of important challenges. As mobile devices are limited in resources, it poses a need to deal with the data transfer cost either by minimizing it or by avoiding it as much as possible. Mobile content distribution networks, opportunistic routing for wireless networks, mobile social networks and overlay peer-to-peer networks are a few of those communication paradigms that rely on network link capacity. Therefore they usually suffer from a bottleneck at saturated links. In this current era, network capacity planning, increase in link capacity and reduction in the size of actual data transfer are a few of the proposed solutions to deal with the saturated link problem. However, not much attention has been given to domain specific solutions. The aim of this research is to fill this gap by investigating the domain specific solutions so that unnecessary network data transfer can be avoided. This thesis targets both the push and pull based distributed information retrieval and the contribution is many fold. First, in contrast to state of the art data aggregation approaches, we investigate the data transfer optimization with the help of user profile aggregation and present an architecture for content-based networks. Next, we opt for the information store discovery (also known as resource selection) so that in order to reduce the network data transfer, only a selected set of information stores transfer the data for given user queries. We extend this research for systems where data duplication across information stores can not be avoided and propose data redundancy elimination algorithms. Our data redundancy elimination algorithms are based on summary exchange and make use of Bloom filter. To show the viability of these algorithms, we present a comprehension generation scenario for the mobile search engines and evaluate it with the existing semantic search engine. In order to build a better Bloom filter, we propose two new simplistic hashing algorithms. Next we integrate these hashing algorithms with our data redundancy elimination algorithms and empirically evaluate this combination. Finally, we describe how a semantic web dataset more efficiently can be queried with the query representation as a Bloom filter.

In scientific domains, most of the measurements collected during observation periods are stored in flat files. In many cases, especially when different scientists come together from different fields to draw comprehensive conclusions, the formats of the files vary from one group to another. Integrating, querying, and retrieving data from such heterogeneous data files present a challenge. Semantic interoperability is essential in order to harmonize these datasets.In this thesis, we describe an ontology-based system that parses, summarizes, represents and integrates heterogeneous data files stored as flat files. The test bed dataset is from the Episodic Events Great Lakes Experiment (EEGLE) project which collected over 500 MB of data in more than 1,500 objects. Existing works on querying hydrological data involve the use of relational databases and do not provide ways to query within the flat files. Hence efficient ways are required to eliminate the overhead associated with relational databases and still provide the flexibility and ease of querying that relational databases offer.We develop an intuitive approach using ontologies to integrate and query the semi-structured data present in the flat files. The crawled data from the flat files is represented in XML using resource scripts to provide a structure and schema to it. We then create ontologies with rules using Protégé-OWL editor to semantically represent the data being observed. The ontologies are mapped with the XML data to generate records similar to relational database records. Finally, these mapped records can be queried from a custom-built interface to get the desired results.Currently, the system that we have developed supports simple column queries, range queries and similarity queries. There is also support for keyword-based semantic queries through the Protégé-OWL editor. Our system makes use of the right tools to integrate and represent the data semantically since we intend to provide as much semantic support as possible through the use of ontologies. Since we deal with domain-specific data, the robustness of the system can only be determined by how well we support normal and semantic querying. The ontologies can be enriched semantically to extend support for complex queries.

Serious gaming is gaining momentum as a legitimate tool in education, occupational training, physical fitness, military simulations, and many other domains. However, little work has been done to generalize the structure and development of serious gaming applications.This thesis describes the establishment of object and runtime models that can be used to enable goal-driven collaboration. This idea is explored through simple and rapid conceptualization of serious gaming applications. It begins with a background in serious gaming that includes examples and studies of their effectiveness. Then a description of GeoGame, a GIS-based multiplayer game that is used as a pilot serious gaming application, is provided. Next, the object model and runtime models are described along with the course of their development. Disparate scenarios are specified in terms of these models in order to validate them. An accompanying architecture is described that can be used to implement the models. Next the process of converting GeoGame to adhere to these models is laid out. An analysis of the model-based version of GeoGame describes how it compares to the original purpose-built application in terms of performance and other factors. The broader applications of a serious gaming framework are also explored, along with other future work.

The quest to answer fundamental questions and solve complex problems is a principal tenet of Earth science. The pursuit of scientific knowledge has generated profuse research, resulting in a plethora of information-rich resources. This phenomenon offers great potential for scientific discovery. However, a deficiency in information connectivity and processing standards has become evident. This deficiency has resulted in a demand for tools to facilitate and process this upsurge in information. This ontology project is an answer to the demand for information processing tools. The primary purpose of this domain-specific ontology and knowledge base is to organize, connect, and correlate research data related to brittle deformation microstructures. This semantically enabled ontology may be queried to return not only asserted information, but inferred knowledge that may not be evident. In addition, its standardized development in OWL-DL (Web Ontology Language-Description Logic) allows the potential for sharing and reuse among other geologic science communities.

The thesis describes a Semantic approach towards web search through a stand-alone Java application. An Ontology Web Language(OWL) model is used to build a knowledge database related to different types of Organisms. The goal is to guide the Google web search engine using this OWL model. In the rst approach towards Semantic web search, an inference engine called CLIPS is used and in the second aproach, the Protege-OWL API is used. The thesis goes in detail about the design, working and comparison of these two approaches. The thesis also deals with design approach for enhancement of the OWL model, once the Semantic web search is done through the Protege-OWL API. This is achieved using Natural Language Processing and Parsing technique. Examples and results of the search and enhancement part of the application are described in detail. Future research directions are indicated.

The Northern Saw-whet Owl (Aegolius acadicus) is a small common owl of which little is known. The saw-whet’s migration dynamics are particularly shrouded in mystery due to the secretive nature of this owl. This study examined data obtained by capturing owls with mist nets in order to better understand volume, timing, sex differences and age differences in migration in the Piedmont province of Virginia for 2002-2007. Comparative owl capture data from two other physiographic regions (Mountain and Coastal Plain) were also examined. Migration flight volume was highest in the Mountain province and lowest in the Coastal Plain, while movements were earliest in the Mountain province and latest in the Coastal Plain. Females dominated samples, suggesting differential migration between sexes and/or the existence of an audio lure bias. Samples were typically dominated by adult owls except for in 2007, a year characterized as an invasion year.

An important goal for biomedical research is to elucidate causal and modifier networks of human disease. While integrative functional genomics approaches have shown success in the identification of biological modules associated with normal and disease states, a critical bottleneck is representing knowledge capable of encompassing asserted or derivable causality mechanisms. Both single gene and more complex multifactorial diseases often exhibit several phenotypes and a variety of approaches suggest that phenotypic similarity between diseases can be a reflection of shared activities of common biological modules composed of interacting or functionally related genes. Thus, analyzing the overlaps and interrelationships of clinical manifestations of a series of related diseases may provide a window into the complex biological modules that lead to a disease phenotype. In order to evaluate our hypothesis, we are developing a systematic and formal approach to extract phenotypic information present in textual form within Online Mendelian Inheritance in Man (OMIM) and Syndrome DB databases to construct a disease - clinical phenotypic feature matrix to be used by various clustering procedures to find similarity between diseases. Our objective is to demonstrate relationships detectable across a range of disease concept types modeled in UMLS to analyze the detectable clinical overlaps of several Cardiovascular Syndromes (CVS) in OMIM in order to find the associations between phenotypic clusters and the functions of underlying genes and pathways. Most of the current biomedical knowledge is spread across different databases in different formats and mining these datasets leads to large and unmanageable results. Semantic Web principles and standards provide an ideal platform to integrate such heterogeneous information and could allow the detection of implicit relations and the formulation of interesting hypotheses. We implemented a page-ranking algorithm onto Semantic Web to prioritize biological entities for their relative contribution and relevance which can be combined with this clustering approach. In this way, disease-gene, disease-pathway or disease-process relationships could be prioritized by mining a phenome - genome framework that not only discovers but also determines the importance of the resources by making queries of higher order relationships of multi-dimensional data that reflect the feature complexity of diseases.

A protocol-oriented approach of modeling and enacting business processes and workflows has been developed recently that offers advantages in terms of supporting the autonomy and heterogeneity of business partners and the reconfigurability of their process. Importantly, protocols are described using commitments, map to the individual computation of the participating roles, and can be composed to yield more complex protocols. However, verifying that the protocols, especially composed protocols, fully satisfy appropriate correctness properties remains an open problem. This dissertation presents a novel way to model business protocols in terms of commitments involved and the constraints for protocol composition. The correct composition of a business process can be expressed via individual protocol definitions and their composition constraints,thereby enabling the verification of large processes. Importantly, as part of the verification process, protocols are translated into the language Promela, which makes them amenable to analysis and verification using the model checker Spin. As a result many important properties of business protocols and their compositions into partial and full workflows can be verified, and improved protocols can be produced. The contribution of this dissertation is in providing a generalized mechanism for modeling commitments, formulating and verifying properties related to commitments. In fact, the results are applicable to a wide range of processes and related protocols, such as scientific discovery processes and workflows.

Application of ontologies in the environmental science will allow experts in this field to model their domain knowledge for more efficient exchange and reuse. This thesis presents a modular approach in reengineering existing upper-level ontologies to conceptualize specific domain knowledge. The aim of extending these upper-level ontologies is to tailor and transform the existing conceptual models into new ones designed for the use in a specific domain in the earth sciences. This thesis extends the upper-level Semantic Web for Earth and Environmental Terminology (SWEET)ontologies to develop ontologies for part of the hydrogeology domain. The existing SWEET ontologies are developed by NASA’s Jet Propulsion lab for Earth system science (http://sweet.jpl.nasa.gov/ontology/). In the new model, presented in this thesis, the architecture and orthogonal design of the SWEET ontologies is not disturbed but restructured at certain levels. New concepts are added to the old structure and the consistency is maintained for use by other domains. This thesis discusses the useful steps,necessary tools and other procedures involved in ontological reengineering of existing upper-level ontologies. The hydrogeology domain modeled in this thesis by means of reengineering, exemplifies the reusability methodology for the Earth system science knowledge base.

Today, worldwide business communities are in the era of the Supply Chains. A Supply Chain is a collection of several independent enterprises that partner together to achieve specific goals. These enterprises may plan, source, produce, deliver, or transport materials to satisfy an immediate or projected market demand, and may provide the after sales support, warranty services, and returns. Each enterprise in the Supply Chain has roles and elements. The roles include supplier, customer, or carrier and the elements include functional units, processes, information, information resources, materials, objects, decisions, practices, and performance measures. Each enterprise, individually, manages these elements in addition to their flows, their interdependencies, and their complex interactions. Since a Supply Chain brings several enterprises together to complement each other to achieve a unified goal, the elements in each enterprise have to complement each other and have to be managed together as one unit to achieve the unified goal efficiently. Moreover, since there are a large number of elements to be defined and managed in a single enterprise, then the number of elements to be defined and managed when considering the whole Supply Chain is massive. The supply chain community is using the Supply Chain Operations Reference model (SCOR model) to define their supply chains. However, the SCOR model methodology is limited in defining the supply chain. The SCOR model defines the supply chain in terms of processes, performance metrics, and best practices. In fact, the supply chain community, SCOR users in particular, exerts massive effort to render an adequate supply chain definition that includes the other elements besides the elements covered in the SCOR model. Also, the SCOR model is delivered to the user in a document, which puts a tremendous burden on the user to use the model and makes it difficult to share the definition within the enterprise or across the supply chain. This research is directed towards overcoming the limitations and shortcomings of the current supply chain definition methodology. This research proposes a methodology and a tool that will enable an automated and comprehensive definition of the Supply Chain at any level of details. The proposed comprehensive definition methodology captures all the constituent parts of the Supply Chain at four different levels which are, the supply chain level, the enterprise level, the elements level, and the interaction level. At the Supply Chain level, the various enterprises that constitute the supply chain are defined. At the enterprise level, the enterprise elements are identified. At the enterprises' elements level, each element in the enterprise is explicitly defined. At the interaction level, the flows, interdependence, and interactions that exist between and within the other three levels are identified and defined. The methodology utilized several modeling techniques to generate generic explicit views and models that represents the four levels. The developed views and models were transformed to a series of questions and answers, where the questions correspond to what a view provides and the answers are the knowledge captured and generated from the view. The questions and answers were integrated to render a generic multi-view of the supply chain. The methodology and the multi-view were implemented in an ontology-based tool. The ontology includes sets of generic supply chain ontological components that represent the supply chain elements and a set of automated procedures that can be utilized to define a specific supply chain. A specific supply chain can be defined by re-using the generic components and customizing them to the supply chain specifics. The ontology-based tool was developed to function in the supply chain dynamic, information intensive, geographically dispersed, and heterogeneous environment. To that end, the tool was developed to be generic, sharable, automated, customizable, extensible, and scalable.