As a multilingual system,Wikipedia provides many challenges for academics and engineers alike. One such challenge is cultural contextualisation of Wikipedia content, and the lack of approaches to effectively quantify it. Additionally, what seems to lack is the intent of establishing sound computational practices and frameworks for measuring cultural variations in the data. Current approaches seem to mostly be dictated by the data availability, which makes it difficult to apply them in other contexts. Another common drawback is that they rarely scale due to a significant qualitative or translation effort. To address these limitations, this thesis develops and tests two modular quantitative approaches. They are aimed at quantifying culture-related phenomena in systems which rely on multilingual user-generated content. In particular, they allow to: (1) operationalise a custom concept of culture in a system; (2) quantify and compare culture-specific content- or coverage biases in such a system; and (3) map a large scale landscape of shared cultural interests and focal points. Empirical validation of these approaches is split into two parts. First, an approach to mapping Wikipedia communities of shared co-editing interests is validated on two large Wikipedia datasets comprising multilateral geopolitical and linguistic editor communities. Both datasets reveal measurable clusters of consistent co-editing interest, and computationally confirm that these clusters correspond to existing colonial, religious, socio economic, and geographical ties. Second, an approach to quantifying content differences is validated on a multilingual Wikipedia dataset, and a multi-platform (Wikipedia and Encyclopedia Britannica) dataset. Both are limited to a selected knowledge domain of national history. This analysis allows, for the first time on the large scale, to quantify and visualise the distribution of historical focal points in the articles on national histories. All results are cross-validated either by domain experts, or external datasets. Main thesis contributions. This thesis: (1) presents an effort to formalise the process of measuring cultural variations in user-generated data; (2) introduces and tests two novel approaches to quantifying cultural contextualisation in multilingual data; (3) synthesises a valuable overview of literature on defining and quantifying culture; (4) provides important empirical insights on the effect of culture on Wikipedia content and coverage; demonstrates that Wikipedia is not contextfree, and these differences should not be treated as noise, but rather, as an important feature of the data. (5) makes practical service contributions through sharing data and visualisations.

Graph-based data formats are flexible in representing data. In particular semantic data models, where the schema is part of the data, gained traction and commercial success in recent years. Semantic data models are also the basis for the Semantic Web - a Web of data governed by open standards in which computer programs can freely access the provided data. This thesis is concerned with the correctness of programs that access semantic data. While the flexibility of semantic data models is one of their biggest strengths, it can easily lead to programmers accidentally not accounting for unintuitive edge cases. Often, such exceptions surface during program execution as run-time errors or unintended side-effects. Depending on the exact condition, a program may run for a long time before the error occurs and the program crashes. This thesis defines type systems that can detect and avoid such run-time errors based on schema languages available for the Semantic Web. In particular, this thesis uses the Web Ontology Language (OWL) and its theoretic underpinnings, i.e., description logics, as well as the Shapes Constraint Language (SHACL) to define type systems that provide type-safe data access to semantic data graphs. Providing a safe type system is an established methodology for proving the absence of run-time errors in programs without requiring execution. Both schema languages are based on possible world semantics but differ in the treatment of incomplete knowledge. While OWL allows for modelling incomplete knowledge through an open-world semantics, SHACL relies on a fixed domain and closed-world semantics. We provide the formal underpinnings for type systems based on each of the two schema languages. In particular, we base our notion of types on sets of values which allows us to specify a subtype relation based on subset semantics. In case of description logics, subsumption is a routine problem. For the type system based on SHACL, we are able to translate it into a description logic subsumption problem.

The distributed setting of RDF stores in the cloud poses many challenges. One such challenge is how the data placement on the compute nodes can be optimized to improve the query performance. To address this challenge, several evaluations in the literature have investigated the effects of existing data placement strategies on the query performance. A common drawback in theses evaluations is that it is unclear whether the observed behaviors were caused by the data placement strategies (if different RDF stores were evaluated as a whole) or reflect the behavior in distributed RDF stores (if cloud processing frameworks like Hadoop MapReduce are used for the evaluation). To overcome these limitations, this thesis develops a novel benchmarking methodology for data placement strategies that uses a data-placement-strategy-independent distributed RDF store to analyze the effect of the data placement strategies on query performance. With this evaluation methodology the frequently used data placement strategies have been evaluated. This evaluation challenged the commonly held belief that data placement strategies that emphasize local computation, such as minimal edge-cut cover, lead to faster query executions. The results indicate that queries with a high workload may be executed faster on hash-based data placement strategies than on, e.g., minimal edge-cut covers. The analysis of the additional measurements indicates that vertical parallelization (i.e., a well-distributed workload) may be more important than horizontal containment (i.e., minimal data transport) for efficient query processing. Moreover, to find a data placement strategy with a high vertical parallelization, the thesis tests the hypothesis that collocating small connected triple sets on the same compute node while balancing the amount of triples stored on the different compute nodes leads to a high vertical parallelization. Specifically, the thesis proposes two such data placement strategies. The first strategy called overpartitioned minimal edge-cut cover was found in the literature and the second strategy is the newly developed molecule hash cover. The evaluation revealed a balanced query workload and a high horizontal containment, which lead to a high vertical parallelization. As a result these strategies showed a better query performance than the frequently used data placement strategies.

Current political issues are often reflected in social media discussions, gathering politicians and voters on common platforms. As these can affect the public perception of politics, the inner dynamics and backgrounds of such debates are of great scientific interest. This thesis takes user generated messages from an up-to-date dataset of considerable relevance as Time Series, and applies a topic-based analysis of inspiration and agenda setting to it. The Institute for Web Science and Technologies of the University Koblenz-Landau has collected Twitter data generated beforehand by candidates of the European Parliament Election 2019. This work processes and analyzes the dataset for various properties, while focusing on the influence of politicians and media on online debates. An algorithm to cluster tweets into topical threads is introduced. Subsequently, Sequential Association Rules are mined, yielding wide array of potential influence relations between both actors and topics. The elaborated methodology can be configured with different parameters and is extensible in functionality and scope of application.

Commonsense reasoning can be seen as a process of identifying dependencies amongst events and actions. Understanding the circumstances surrounding these events requires background knowledge with sufficient breadth to cover a wide variety of domains. In the recent decades, there has been a lot of work in extracting commonsense knowledge, a number of these projects provide their collected data as semantic networks such as ConceptNet and CausalNet. In this thesis, we attempt to undertake the Choice Of Plausible Alternatives (COPA) challenge, a problem set with 1000 questions written in multiple-choice format with a premise and two alternative choices for each question. Our approach differs from previous work by using shortest paths between concepts in a causal graph with the edge weight as causality metric. We use CausalNet as primary network and implement a few design choices to explore the strengths and drawbacks of this approach, and propose an extension using ConceptNet by leveraging its commonsense knowledge base.

Most social media platforms allow users to freely express their opinions, feelings, and beliefs. However, in recent years the growing propagation of hate speech, offensive language, racism and sexism on the social media outlets have drawn attention from individuals, companies, and researchers. Today, sexism both online and offline with different forms, including blatant, covert, and subtle lan- guage, is a common phenomenon in society. A notable amount of work has been done over identifying sexist content and computationally detecting sexism which exists online. Although previous efforts have mostly used peoples’ activities on social media platforms such as Twitter as a public and helpful source for collecting data, they neglect the fact that the method of gathering sexist tweets could be biased towards the initial search terms. Moreover, some forms of sexism could be missed since some tweets which contain offensive language could be misclassified as hate speech. Further, in existing hate speech corpora, sexist tweets mostly express hostile sexism, and to some degree, the other forms of sexism which also appear online was disregarded. Besides, the creation of labeled datasets with manual exertion, relying on users to report offensive comments with a tremendous effort by human annotators is not only a costly and time-consuming process, but it also raises the risk of involving discrimination under biased judgment. This thesis generates a novel sexist and non-sexist dataset which is constructed via "UnSexistifyIt", an online web-based game that incentivizes the players to make minimal modifications to a sexist statement with the goal of turning it into a non-sexist statement and convincing other players that the modified statement is non-sexist. The game applies the methodology of "Game With A Purpose" to generate data as a side-effect of playing the game and also employs the gamification and crowdsourcing techniques to enhance non-game contexts. When voluntary participants play the game, they help to produce non-sexist statements which can reduce the cost of generating new corpus. This work explores how diverse individual beliefs concerning sexism are. Further, the result of this work highlights the impact of various linguistic features and content attributes regarding sexist language detection. Finally, this thesis could help to expand our understanding regarding the syntactic and semantic structure of sexist and non-sexist content and also provides insights to build a probabilistic classifier for single sentences into sexist or non-sexist classes and lastly find a potential ground truth for such a classifier.

The purpose of this thesis is to explore the sentiment distributions of Wikipedia concepts. We analyse the sentiment of the entire English Wikipedia corpus, which includes 5,669,867 articles and 1,906,375 talks, by using a lexicon-based method with four different lexicons. Also, we explore the sentiment distributions from a time perspective using the sentiment scores obtained from our selected corpus. The results obtained have been compared not only between articles and talks but also among four lexicons: OL, MPQA, LIWC, and ANEW. Our findings show that among the four lexicons, MPQA has the highest sensitivity and ANEW has the lowest sensitivity to emotional expressions. Wikipedia articles show more sentiments than talks according to OL, MPQA, and LIWC, whereas Wikipedia talks show more sentiments than articles according to ANEW. Besides, the sentiment has a trend regarding time series, and each lexicon has its own bias regarding text describing different things. Moreover, our research provides three interactive widgets for visualising sentiment distributions for Wikipedia concepts regarding the time and geolocation attributes of concepts.

Wikipedia is the biggest, free online encyclopaedia that can be expanded by any-one. For the users, who create content on a specific Wikipedia language edition, a social network exists. In this social network users are categorised into different roles. These are normal users, administrators and functional bots. Within the networks, a user can post reviews, suggestions or send simple messages to the "talk page" of another user. Each language in the Wikipedia domain has this type of social network. In this thesis characteristics of the three different roles are analysed in order to learn how they function in one language network of Wikipedia and apply them to another Wikipedia network to identify bots. Timestamps from created posts are analysed to reveal noticeable characteristics referring to continuous messages, message rates and irregular behaviour of a user are discovered. Through this process we show that there exist differences between the roles for the mentioned characteristics.

Ontologies are valuable tools for knowledge representation and important building blocks of the Semantic Web. They are not static and can change over time. Changing an ontology can be necessary for various reasons: the domain that is represented by an ontology can change or an ontology is reused and must be adapted to the new context. In addition, modeling errors could have been introduced into the ontology which must be found and removed. The non-triviality of the change process has led to the emerge of ontology change as an own field of research. The removal of knowledge from ontologies is an important aspect of this change process, because even the addition of new knowledge to an ontology potentially requires the removal of older, conflicting knowledge. Such a removal must be performed in a thought-out way. A naïve change of concepts within the ontology can easily remove other, unrelated knowledge or alter the semantics of concepts in an unintended way [2]. For these reasons, this thesis introduces a formal operator for the fine-grained retraction of knowledge from EL concepts which is partially based on the postulates for belief set contraction and belief base contraction [3, 4, 5] and the work of Suchanek et al. [6]. For this, a short introduction to ontologies and OWL 2 is given and the problem of ontology change is explained. It is then argued why a formal operator can support this process and why the Description Logic EL provides a good starting point for the development of such an operator. After this, a general introduction to Description Logic is given. This includes its history, an overview of its applications and common reasoning tasks in this logic. Following this, the logic EL is defined. In a next step, related work is examined and it is shown why the recovery postulate and the relevance postulate cannot be naïvely employed in the development of an operator that removes knowledge from EL concepts. Following this, the requirements to the operator are formulated and properties are given which are mainly based on the postulates for belief set and belief base contraction. Additional properties are developed which make up for the non-applicability of the recovery and relevance postulates. After this, a formal definition of the operator is given and it is shown that the operator is applicable to the task of a fine-grained removal of knowledge from EL concepts. In a next step, it is proven that the operator fulfills all the previously defined properties. It is then demonstrated how the operator can be combined with laconic justifications [7] to assist a human ontology editor by automatically removing unwanted consequences from an ontology. Building on this, a plugin for the ontology editor Protégé is introduced that is based on algorithms that were derived from the formal definition of the operator. The content of this work is then summarized and a final conclusion is drawn. The thesis closes with an outlook into possible future work.

This Master Thesis is an exploratory research to determine whether it is feasible to construct a subjectivity lexicon using Wikipedia. The key hypothesis is that that all quotes in Wikipedia are subjective and all regular text are objective. The degree of subjectivity of a word, also known as ''Quote Score'' is determined based on the ratio of word frequency in quotations to its frequency outside quotations. The proportion of words in the English Wikipedia which are within quotations is found to be much smaller as compared to those which are not in quotes, resulting in a right-skewed distribution and low mean value of Quote Scores. The methodology used to generate the subjectivity lexicon from text corpus in English Wikipedia is designed in such a way that it can be scaled and reused to produce similar subjectivity lexica of other languages. This is achieved by abstaining from domain and language-specific methods, apart from using only readily-available English dictionary packages to detect and exclude stopwords and non-English words in the Wikipedia text corpus. The subjectivity lexicon generated from English Wikipedia is compared against other lexica; namely MPQA and SentiWordNet. It is found that words which are strongly subjective tend to have high Quote Scores in the subjectivity lexicon generated from English Wikipedia. There is a large observable difference between distribution of Quote Scores for words classified as strongly subjective versus distribution of Quote Scores for words classified as weakly subjective and objective. However, weakly subjective and objective words cannot be differentiated clearly based on Quote Score. In addition to that, a questionnaire is commissioned as an exploratory approach to investigate whether subjectivity lexicon generated from Wikipedia could be used to extend the coverage of words of existing lexica.

Topic models are a popular tool to extract concepts of large text corpora. These text corpora tend to contain hidden meta groups. The size relation of these groups is frequently imbalanced. Their presence is often ignored when applying a topic model. Therefore, this thesis explores the influence of such imbalanced corpora on topic models. The influence is tested by training LDA on samples with varying size relations. The samples are generated from data sets containing a large group differences i.e language difference and small group differences i.e. political orientation. The predictive performance on those imbalanced corpora is judged using perplexity. The experiments show that the presence of groups in training corpora can influence the prediction performance of LDA. The impact varies due to various factors, including language-specific perplexity scores. The group-related prediction performance changes for groups when varying the relative group sizes. The actual change varies between data sets. LDA is able to distinguish between different latent groups in document corpora if differences between groups are large enough, e.g. for groups with different languages. The proportion of group-specific topics is under-proportional to the share of the group in the corpus and relatively smaller for minorities.

The Web contains some extremely valuable information; however, often poor quality, inaccurate, irrelevant or fraudulent information can also be found. With the increasing amount of data available, it is becoming more and more difficult to distinguish truth from speculation on the Web. One of the most, if not the most, important criterion used to evaluate data credibility is the information source, i.e., the data origin. Trust in the information source is a valuable currency users have to evaluate such data. Data popularity, recency (or the time of validity), reliability, or vagueness ascribed to the data may also help users to judge the validity and appropriateness of information sources. We call this knowledge derived from the data the provenance of the data. Provenance is an important aspect of the Web. It is essential in identifying the suitability, veracity, and reliability of information, and in deciding whether information is to be trusted, reused, or even integrated with other information sources. Therefore, models and frameworks for representing, managing, and using provenance in the realm of Semantic Web technologies and applications are critically required. This thesis highlights the benefits of the use of provenance in different Web applications and scenarios. In particular, it presents management frameworks for querying and reasoning in the Semantic Web with provenance, and presents a collection of Semantic Web tools that explore provenance information when ranking and updating caches of Web data. To begin, this thesis discusses a highly exible and generic approach to the treatment of provenance when querying RDF datasets. The approach re-uses existing RDF modeling possibilities in order to represent provenance. It extends SPARQL query processing in such a way that given a SPARQL query for data, one may request provenance without modifying it. The use of provenance within SPARQL queries helps users to understand how RDF facts arederived, i.e., it describes the data and the operations used to produce the derived facts. Turning to more expressive Semantic Web data models, an optimized algorithm for reasoning and debugging OWL ontologies with provenance is presented. Typical reasoning tasks over an expressive Description Logic (e.g., using tableau methods to perform consistency checking, instance checking, satisfiability checking, and so on) are in the worst case doubly exponential, and in practice are often likewise very expensive. With the algorithm described in this thesis, however, one can efficiently reason in OWL ontologies with provenance, i.e., provenance is efficiently combined and propagated within the reasoning process. Users can use the derived provenance information to judge the reliability of inferences and to find errors in the ontology. Next, this thesis tackles the problem of providing to Web users the right content at the right time. The challenge is to efficiently rank a stream of messages based on user preferences. Provenance is used to represent preferences, i.e., the user defines his preferences over the messages' popularity, recency, etc. This information is then aggregated to obtain a joint ranking. The aggregation problem is related to the problem of preference aggregation in Social Choice Theory. The traditional problem formulation of preference aggregation assumes a I fixed set of preference orders and a fixed set of domain elements (e.g. messages). This work, however, investigates how an aggregated preference order has to be updated when the domain is dynamic, i.e., the aggregation approach ranks messages 'on the y' as the message passes through the system. Consequently, this thesis presents computational approaches for online preference aggregation that handle the dynamic setting more efficiently than standard ones. Lastly, this thesis addresses the scenario of caching data from the Linked Open Data (LOD) cloud. Data on the LOD cloud changes frequently and applications relying on that data - by pre-fetching data from the Web and storing local copies of it in a cache - need to continually update their caches. In order to make best use of the resources (e.g., network bandwidth for fetching data, and computation time) available, it is vital to choose a good strategy to know when to fetch data from which data source. A strategy to cope with data changes is to check for provenance. Provenance information delivered by LOD sources can denote when the resource on the Web has been changed last. Linked Data applications can benefit from this piece of information since simply checking on it may help users decide which sources need to be updated. For this purpose, this work describes an investigation of the availability and reliability of provenance information in the Linked Data sources. Another strategy for capturing data changes is to exploit provenance in a time-dependent function. Such a function should measure the frequency of the changes of LOD sources. This work describes, therefore, an approach to the analysis of data dynamics, i.e., the analysis of the change behavior of Linked Data sources over time, followed by the investigation of different scheduling update strategies to keep local LOD caches up-to-date. This thesis aims to prove the importance and benefits of the use of provenance in different Web applications and scenarios. The exibility of the approaches presented, combined with their high scalability, make this thesis a possible building block for the Semantic Web proof layer cake - the layer of provenance knowledge.

In this article we analyze the privacy aspects of a mobile sensor application used for recording urban travel patterns as part of a travel-survey service. This service has been developed and field-tested within the Live+Gov EU Project. The privacy analysis follows a structured approach established in. Eight privacy recommendations are derived, and have already led to corresponding enhancements of the travel-survey service.

This habilitation thesis collects works addressing several challenges on handling uncertainty and inconsistency in knowledge representation. In particular, this thesis contains works which introduce quantitative uncertainty based on probability theory into abstract argumentation frameworks. The formal semantics of this extension is investigated and its application for strategic argumentation in agent dialogues is discussed. Moreover, both the computational as well as the meaningfulness of approaches to analyze inconsistencies, both in classical logics as well as logics for uncertain reasoning is investigated. Finally, this thesis addresses the implementation challenges for various kinds of knowledge representation formalisms employing any notion of inconsistency tolerance or uncertainty.

Through the increasing availability of access to the web, more and more interactions between people take place in online social networks, such as Twitter or Facebook, or sites where opinions can be exchanged. At the same time, knowledge is made openly available for many people, such as by the biggest collaborative encyclopedia Wikipedia and diverse information in Internet forums and on websites. These two kinds of networks - social networks and knowledge networks - are highly dynamic in the sense that the links that contain the important information about the relationships between people or the relations between knowledge items are frequently updated or changed. These changes follow particular structural patterns and characteristics that are far less random than expected. The goal of this thesis is to predict three characteristic link patterns for the two network types of interest: the addition of new links, the removal of existing links and the presence of latent negative links. First, we show that the prediction of link removal is indeed a new and challenging problem. Even if the sociological literature suggests that reasons for the formation and resolution of ties are often complementary, we show that the two respective prediction problems are not. In particular, we show that the dynamics of new links and unlinks lead to the four link states of growth, decay, stability and instability. For knowledge networks we show that the prediction of link changes greatly benefits from the usage of temporal information; the timestamp of link creation and deletion events improves the prediction of future link changes. For that, we present and evaluate four temporal models that resemble different exploitation strategies. Focusing on directed social networks, we conceptualize and evaluate sociological constructs that explain the formation and dissolution of relationships between users. Measures based on information about past relationships are extremely valuable for predicting the dissolution of social ties. Hence, consistent for knowledge networks and social networks, temporal information in a network greatly improves the prediction quality. Turning again to social networks, we show that negative relationship information such as distrust or enmity can be predicted from positive known relationships in the network. This is particularly interesting in networks where users cannot label their relationships to other users as negative. For this scenario we show how latent negative relationships can be predicted.

The way information is presented to users in online community platforms has an influence on the way the users create new information. This is the case, for instance, in question-answering fora, crowdsourcing platforms or other social computation settings. To better understand the effects of presentation policies on user activity, we introduce a generative model of user behaviour in this paper. Running simulations based on this user behaviour we demonstrate the ability of the model to evoke macro phenomena comparable to the ones observed on real world data.

Next word prediction is the task of suggesting the most probable word a user will type next. Current approaches are based on the empirical analysis of corpora (large text files) resulting in probability distributions over the different sequences that occur in the corpus. The resulting language models are then used for predicting the most likely next word. State-of-the-art language models are based on n-grams and use smoothing algorithms like modified Kneser-Ney smoothing in order to reduce the data sparsity by adjusting the probability distribution of unseen sequences. Previous research has shown that building word pairs with different distances by inserting wildcard words into the sequences can result in better predictions by further reducing data sparsity. The aim of this thesis is to formalize this novel approach and implement it by also including modified Kneser-Ney smoothing.

Tagging systems are intriguing dynamic systems, in which users collaboratively index resources with the so-called tags. In order to leverage the full potential of tagging systems, it is important to understand the relationship between the micro-level behavior of the individual users and the macro-level properties of the whole tagging system. In this thesis, we present the Epistemic Dynamic Model, which tries to bridge this gap between the micro-level behavior and the macro-level properties by developing a theory of tagging systems. The model is based on the assumption that the combined influence of the shared background knowledge of the users and the imitation of tag recommendations are sufficient for explaining the emergence of the tag frequency distribution and the vocabulary growth in tagging systems. Both macro-level properties of tagging systems are closely related to the emergence of the shared community vocabulary. rnrnWith the help of the Epistemic Dynamic Model, we show that the general shape of the tag frequency distribution and of the vocabulary growth have their origin in the shared background knowledge of the users. Tag recommendations can then be used for selectively influencing this general shape. In this thesis, we especially concentrate on studying the influence of recommending a set of popular tags. Recommending popular tags adds a feedback mechanism between the vocabularies of individual users that increases the inter-indexer consistency of the tag assignments. How does this influence the indexing quality in a tagging system? For this purpose, we investigate a methodology for measuring the inter-resource consistency of tag assignments. The inter-resource consistency is an indicator of the indexing quality, which positively correlates with the precision and recall of query results. It measures the degree to which the tag vectors of indexed resources reflect how the users perceive the similarity between resources. We argue with our model, and show it with a user experiment, that recommending popular tags decreases the inter-resource consistency in a tagging system. Furthermore, we show that recommending the user his/her previously used tags helps to increase the inter-resource consistency. Our measure of the inter-resource consistency complements existing measures for the evaluation and comparison of tag recommendation algorithms, moving the focus to evaluating their influence on the indexing quality.

The Multimedia Metadata Ontology (M3O) provides a generic modeling framework for representing multimedia metadata. It has been designed based on an analysis of existing metadata standards and metadata formats. The M3O abstracts from the existing metadata standards and formats and provides generic modeling solutions for annotations, decompositions, and provenance of metadata. Being a generic modeling framework, the M3O aims at integrating the existing metadata standards and metadata formats rather than replacing them. This is in particular useful as today's multimedia applications often need to combine and use more than one existing metadata standard or metadata format at the same time. However, applying and specializing the abstract and powerful M3O modeling framework in concrete application domains and integrating it with existing metadata formats and metadata standards is not always straightforward. Thus, we have developed a step-by-step alignment method that describes how to integrate existing multimedia metadata standards and metadata formats with the M3O in order to use them in a concrete application. We demonstrate our alignment method by integrating seven different existing metadata standards and metadata formats with the M3O and describe the experiences made during the integration process.

In this thesis, I study the spectral characteristics of large dynamic networks and formulate the spectral evolution model. The spectral evolution model applies to networks that evolve over time, and describes their spectral decompositions such as the eigenvalue and singular value decomposition. The spectral evolution model states that over time, the eigenvalues of a network change while its eigenvectors stay approximately constant. I validate the spectral evolution model empirically on over a hundred network datasets, and theoretically by showing that it generalizes arncertain number of known link prediction functions, including graph kernels, path counting methods, rank reduction and triangle closing. The collection of datasets I use contains 118 distinct network datasets. One dataset, the signed social network of the Slashdot Zoo, was specifically extracted during work on this thesis. I also show that the spectral evolution model can be understood as a generalization of the preferential attachment model, if we consider growth in latent dimensions of a network individually. As applications of the spectral evolution model, I introduce two new link prediction algorithms that can be used for recommender systems, search engines, collaborative filtering, rating prediction, link sign prediction and more. The first link prediction algorithm reduces to a one-dimensional curve fitting problem from which a spectral transformation is learned. The second method uses extrapolation of eigenvalues to predict future eigenvalues. As special cases, I show that the spectral evolution model applies to directed, undirected, weighted, unweighted, signed and bipartite networks. For signed graphs, I introduce new applications of the Laplacian matrix for graph drawing, spectral clustering, and describe new Laplacian graph kernels. I also define the algebraic conflict, a measure of the conflict present in a signed graph based on the signed graph Laplacian. I describe the problem of link sign prediction spectrally, and introduce the signed resistance distance. For bipartite and directed graphs, I introduce the hyperbolic sine and odd Neumann kernels, which generalize the exponential and Neumann kernels for undirected unipartite graphs. I show that the problem of directed and bipartite link prediction are related by the fact that both can be solved by considering spectral evolution in the singular value decomposition.

Die nächste Generation des World Wide Web, das Semantic Web, erlaubt Benutzern, Unmengen an Informationen über die Grenzen von Webseiten und Anwendungen hinaus zu veröffentlichen und auszutauschen. Die Prinzipien von Linked Data beschreiben Konventionen, um diese Informationen maschinenlesbar zu veröffentlichen. Obwohl es sich aktuell meist um Linked Open Data handelt, deren Verbreitung nicht beschränkt, sondern explizit erwünscht ist, existieren viele Anwendungsfälle, in denen der Zugriff auf Linked Data in Resource Description Framework (RDF) Repositories regelbar sein soll. Bisher existieren lediglich Ansätze für die Lösung dieser Problemstellung, weshalb die Veröffentlichung von vertraulichen Inhalten mittels Linked Data bisher nicht möglich war. Aktuell können schützenswerte Informationen nur mit Hilfe eines externen Betreibers kontrolliert veröffentlicht werden. Dabei werden alle Daten auf dessen System abgelegt und verwaltet. Für einen wirksamen Schutz sind weitere Zugriffsrichtlinien, Authentifizierung von Nutzern sowie eine sichere Datenablage notwendig. Beispiele für ein solches Szenario finden sich bei den sozialen Netzwerken wie Facebook oder StudiVZ. Die Authentifizierung aller Nutzer findet über eine zentrale Webseite statt. Anschließend kann beispielsweise über eine Administrationsseite der Zugriff auf Informationen für bestimmte Nutzergruppen definiert werden. Trotz der aufgezeigten Schutzmechanismen hat der Betreiber selbst immer Zugriff auf die Daten und Inhalte aller Nutzer. Dieser Zustand ist nicht zufriedenstellend. Die Idee des Semantic Webs stellt einen alternativen Ansatz zur Verfügung. Der Nutzer legt seine Daten an einer von ihm kontrollierten Stelle ab, beispielsweise auf seinem privaten Server. Im Gegensatz zum zuvor vorgestellten Szenario ist somit jeder Nutzer selbst für Kontrollmechanismen wie Authentifizierung und Zugriffsrichtlinien verantwortlich. Innerhalb der vorliegenden Arbeit wird ein Framework konzeptioniert und entworfen, welches es mit Hilfe von Regeln erlaubt, den Zugriff auf RDF-Repositories zu beschränken. In Kapitel 2 werden zunächst die bereits existierenden Ansätze für die Zugriffssteuerung vertraulicher Daten im Sematic Web vorgestellt. Des Weiteren werden in Kapitel 3 grundlegende Mechanismen und Techniken erläutert, welche in dieser Arbeit Verwendung finden. In Kapitel 4 wird die Problemstellung konkretisiert und anhand eines Beispielszenarios analysiert. Nachdem Anforderungen und Ansprüche erhoben sind, werden in Kapitel 6 verschiedene Lösungsansätze, eine erste Implementierung und ein Prototyp vorgestellt. Abschließend werden die Ergebnisse der Arbeit und die resultierenden Ausblicke in Kapitel 7 zusammengefasst.

We present the user-centered, iterative design of Mobile Facets, a mobile application for the faceted search and exploration of a large, multi-dimensional data set of social media on a touchscreen mobile phone. Mobile Facets provides retrieval of resources such as places, persons, organizations, and events from an integration of different open social media sources and professional content sources, namely Wikipedia, Eventful, Upcoming, geo-located Flickr photos, and GeoNames. The data is queried live from the data sources. Thus, in contrast to other approaches we do not know in advance the number and type of facets and data items the Mobile Facets application receives in a specific contextual situation. While developingrnMobile Facets, we have continuously evaluated it with a small group of fifive users. We have conducted a task-based, formative evaluation of the fifinal prototype with 12 subjects to show the applicability and usability of our approach for faceted search and exploration on a touchscreen mobile phone.

In dieser Arbeit wird das MobileFacets System präsentiert, dass ein bequemes facettiertes Browsen und Suchen von semantischen Daten auf einem mobilen Endgerät ermöglicht. Anwender bekommen in Abhängigkeit ihres lokalen Ortskontextes, weitreichende Informationen wie Orte, Personen, Organisationen oder Events dargeboten. Basierend auf der Theorie von Facetten, wird das facettierte Browsen zur Erkundung von strukturierten Datensätzen anhand einer Client Anwendung realisiert. Die Anwendung bedient sich dabei eines lokalen Servers, der für Anfragen der Clients, die Anbindung an externe Datenquellen und die Aufbereitung der strukturierten Daten zuständig ist.

In recent years ontologies have become common on the WWW to provide high-level descriptions of specific domains. These descriptions could be effectively used to build applications with the ability to find implicit consequences of their represented knowledge. The W3C developed the Resource Description Framework RDF, a language to describe the semantics of the data on the web, and the Ontology Web Language OWL, a family of knowledge representation languages for authoring ontologies. In this thesis we propose an ontology API engineering framework that makes use of the state-of-the-art ontology modeling technologies as well as of software engineering technologies. This system simplifies the design and implementation process of developing dedicated APIs for ontologies. Developers of semantic web applications usually face the problem of mapping entities or complex relations described in the ontology to object-oriented representations. Mapping complex relationship structures that come with complex ontologies to a useful API requires more complicated API representations than does the mere mapping of concepts to classes. The implementation of correct object persistence functions in such class representations also becomes quite complex.

Existing tools for generating application programming interfaces (APIs) for ontologies lack sophisticated support for mapping the logics-based concepts of the ontology to an appropriate object-oriented implementation of the API. Such a mapping has to overcome the fundamental differences between the semantics described in the ontology and the pragmatics, i.e., structure, functionalities, and behavior implemented in the API. Typically, concepts from the ontology are mapped one-to-one to classes in the targeted programming language. Such a mapping only produces concept representations but not an API at the desired level of granularity expected by an application developer. We present a Model-Driven Engineering (MDE) process to generate customized APIs for ontologies. This API generation is based on the semantics defined in the ontology but also leverages additional information the ontology provides. This can be the inheritance structure of the ontology concepts, the scope of relevance of an ontology concept, or design patterns defined in the ontology.