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Politics and Linguistics have an inextricable affinity. A wide array of evidence suggests that latent ideological nuances are ingrained within the language of political discourse. Over the last decade, uncovering and leveraging patterns in language data has become one of the most outstanding achievements of modern Data Science, which raises some noteworthy questions regarding its prospects within the political landscape.

This paper will examine how the relationship between Politics and Linguistics can be approached in Data Science. I will explore the abilities and limitations of contemporary concepts and state-of-the-art instruments in Natural Language Processing, Machine Learning, and Information Retrieval to address questions inspired by political linguistics, and, more specifically, to classify political claims in terms of their ideology with the help of political party programs in the context of an election process. The connections between Linguistics, Ideology and Data Science are interesting in their own right, but may also be of paramount importance for practical applications. Leveraging political linguistics could have profound implications for research on political behavior, and enable a more accessible way of understanding political agendas, revealing antagonistic lexical structures that arise from a set of political parties competing for attention and support in the context of an election.

Abstract: Data integration of RDF knowledge bases is an important task that plays an increasingly important role. By using many different data sources, it is possible to expand the data stock of a knowledge base or, if necessary, to correct erroneous information in the knowledge base. For this purpose, alignment systems are increasingly used, which relate the schema of one data source to that of another data source in such a way that the data can then be transferred between the data sources. One such system is FiLiPo (Finding Linkage Points). It automatically finds mappings between the schema of a local RDF knowledge base and the schema of a web API. One of the current challenges with such systems is to integrate users more into the process.  Especially when it comes to explaining to the users how and why the system made certain decisions. This bachelor thesis therefore presents a user interface for the FiLiPo alignment system that graphically presents FiLiPo's data to users. The user interface should enable users to understand, analyse and, if necessary, change or remove the alignments generated by FiLiPo.

Abstract: Within the framework of the Semantic Web, information (knowledge) can be recorded in so-called knowledge graphs. However, these can quickly grow to an unmanageable size, so that both the content and the structure of the graph are difficult for people to comprehend. Therefore, it is necessary to find ways to create a basic understanding of the properties of knowledge graphs.

The aim of this work is to determine "knowledge about knowledge graphs" automatically by means of the mathematical model of Formal Concept Analysis (FCA) and to present it to the user. Therefore, an interactive tool was developed with which a user can perform and control the exploration of knowledge graphs.

To confirm the effectiveness of the tool, it was tested by a number of people and then evaluated. The test persons assessed the user experience and usability of the tool as predominantly positive. The aspects rated as less good offer clues for future improvements and optimisations to make the use of the tool even more attractive.

Abstract: In this final thesis the user interface for the FiLiPo system is presented. The development of such user interface requires a further study of problems and risks, the drafting of a concept and its implementation. One of the main goals was to develop an intuitive user interface that allows to use all the functionalities of the FiLiPo system. The thesis provides with the short introduction into schema alignment of RDF based knowledge bases and Web APIs. It also gives short information about the Angular framework that was used for the implementation. After describing the main requirements that have to be taken into consideration and giving answers on how to implement an intuitive user interface, the main concept is presented. It is based on already known solutions and examples, but still requires some creativity for the visualization of the alignment results. Then the implementation is documented. Using the Angular allows a quick integration of different components and their easy manipulation. The results of the user evaluation are presented that show if the concept and implementation were successful or not. In the end, we discuss on the further possible improvements.

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Abstract: Databases are used to store information and it is therefore essential that they are complete. In reality, however, databases have gaps and therefore methods must be used to supplement this missing information. Existing Linked Data systems use interfaces (SPARQL endpoints) for this purpose, which are not provided by all data providers. The common solution in practice is to provide a web API to still be able to request information. In order to be able to supplement missing information via Web APIs, a programme is implemented in this thesis that enables the connection of Linked Data systems and Web APIs. Thus, the programme ExtendedSPARQL developed in this thesis can completely answer a query to the local knowledge base by filling in missing information on-the-fly with the help of external web APIs. In doing so, the programme decides which external Web APIs are relevant for missing information and how to request the external Web APIs. It also decides how to extract the information it is looking for from Web API responses and how to add it to the results of the query. Furthermore, ExtendedSPARQL executes as few Web API requests as possible so that missing information is added with the least effort and redundant information is avoided. It is also easy to use, so that even users with only basic SPARQL knowledge can successfully perform ExtendedSPARQL queries. ExtendedSPARQL also provides a graphical user interface, which makes it even easier to use. In a subsequent evaluation, the programme proved that missing information can be successfully added using external web APIs and that redundant results rarely occur.

Abstract: Researchers are normally not familiar with the thematic orientation of all journals and conferences in their field of research. As soon as researchers want to publish their work, they face the problem of finding a suitable journal or conference where they want to submit the paper. The aim of this thesis is the development of a recommender system, which can find suitable ones in respect of a given title of a publication. The system is based on data from dblp and Semantic Scholar, which contain titles of publications as well as their abstracts and keywords. Different methods for determining the similarity and relevance of papers were investigated. These include Tf/idf, BM25 and cosine similarity in conjunction with Doc2Vec. Various techniques were analysed in order to find and rank the journals and conferences associated with the corresponding papers. In addition, methods were developed to improve the results of the recommender system, such as looking at the number of citations from journals and conferences. The methods were evaluated automatically and manually. It turned out that cosine similarity with Doc2Vec did not achieve good results in contrast to the other two methods. To improve the usability of the recommender system, a visualisation in form of a web service was implemented.

Since the development of the Semantic Web by Tim Berners-Lee, more and more information is being published on the internet as Linked Open Data. These are specially designed to be analysed by machines. All elements are given unique identifiers. The elements can then be linked to each other via relations and form ever larger networks. The result is a "Giant Global Graph" in which all things of interest can be referenced. 

But while the amount of data in the SemanticWeb is constantly growing, only a few can use it. Searching for information is difficult because the user needs some prior knowledge. On the one hand, he needs to know how the data in the graph are connected and how they are labelled. On the other hand, they need knowledge about the query language SPARQL, which can be used to make queries to data sources in the Semantic Web. The visual query language developed in this work makes it easier for the user to get started and thus enables even non-experts to search the Semantic Web for information. Instead of a written query, the user graphically constructs a query from prefabricated elements. For this purpose, the Visual Query Builder programme was developed in this work, which implements such a visual query language. By specifying a schema for the respective data endpoint, the user is given the elements he can use. Thus, the user can see which elements exist at all and which attributes they have. The programme developed in this work and the underlying visual query language were then evaluated by a group of test persons. Visual Query Builder was able to prove that it enables both beginners and advanced users to successfully search a data source in the Semantic Web for desired information. In the evaluation, particular attention was paid to the usability of the application. The evaluation showed that the application achieved good results in both test procedures used and was able to prove its effectiveness.

Digital libraries, such as dblp or the German National Library (DNB), aim to bring knowledge together online and make it available via the internet. Unfortunately, incomplete data sets are part of the everyday life of a digital library. Missing information, such as titles or author names, could be added using external web APIs. The main problem here is the integration of the external data into the local database, since a common schema, which serves to describe the structure of the data, must first be found. This is the main task of schema integration, which is a subfield of information integration and data migration. The ActiveSPARQL programme designed in this thesis exploits schema integration to use data from Web APIs to answer queries on-the-fly. When a user makes a query to the application, both the data from the local database and the externalWeb-APIs should be used to answer it satisfactorily. Using both sources is called a hybrid request. The design is based on the already existing framework ANGIE. In contrast to this, no wrapper is generated to answer the query, but an extended SPARQL query. In addition, ANGIE requires that the access methods of the web APIs must be declared manually. This step can be automated by the AID4SPARQL programme. This is able to find linkage points between the local and external data and thus ensure that external information is compatible with the local data. The results from AID4SPARQL are prepared in such a way that they can be used as a configuration for communication with web APIs. In addition to ActiveSPARQL, aWeb interface was designed to enable non-experts to create and execute hybrid queries without prior knowledge. Finally, a concept for evaluating the framework is presented, which can be used to compare ANGIE and ActiveSPARQL.

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Abstract: This thesis introduces improvements to current approaches of classifying scientific work by observing the semantic similarity of publications in the same citation neighborhood. Available patterns in the neighborhood structures are used to generate an initial set of features. Different text representations, similarity measures and feature modes are implemented and studied to explore new approaches of generating meaningful features that improve classification procedures. Features are evaluated in terms of their predictive power when learning a model that distinguishes between seminal and survey publications. Learning patterns from features to better distinguish between the publications will be a proxy of the effectiveness of these features in evaluating research impact. The state-of-the-art research in this area achieved a result of 68.97% prediction accuracy whereas the approaches presented in this thesis achieved a prediction accuracy of up to 86.98% and therefore beat the latest results by a large margin. Thorough evaluation of the feature sets reveals which relationships in a neighborhood structure provide information that can help improve current research evaluation metrics by identifying high impact scientific work.

Keywords: Semantometrics - Feature Engineering - Natural Language Processing

All kinds of information can be retrieved from web APIs, for example metadata of publications. However, it is not always obvious what kind of data must be sent to the web API in order to receive a meaningful response. For this problem, a programme was developed that learns the appropriate transfer parameters of web APIs with the help of a source database. For this purpose, each type of data from the source database is sent to the web API and it is checked whether the response of the API is related to the sent data. Various parameters can be used to configure how closely the responses of the web API must match the data of the source database in order to be considered meaningful. For this purpose, several metrics for calculating string similarities were used to find the matches of both data sets. Through a suitable evaluation, it could be shown that with good configuration parameters all matches are found. In the presented system, a user also has the possibility to choose different metrics to compare the similarity of two values. For example, it is possible to specify that there must be an exact match between some data, such as ISBNs or other IDs. With the right configuration parameters, as well as knowing and specifying which metric is best for which type of data, almost any data can be recognised as a match that a human would also consider a match.

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Politics and Linguistics have an inextricable affinity. A wide array of evidence suggests that latent ideological nuances are ingrained within the language of political discourse. Over the last decade, uncovering and leveraging patterns in language data has become one of the most outstanding achievements of modern Data Science, which raises some noteworthy questions regarding its prospects within the political landscape.

This paper will examine how the relationship between Politics and Linguistics can be approached in Data Science. I will explore the abilities and limitations of contemporary concepts and state-of-the-art instruments in Natural Language Processing, Machine Learning, and Information Retrieval to address questions inspired by political linguistics, and, more specifically, to classify political claims in terms of their ideology with the help of political party programs in the context of an election process. The connections between Linguistics, Ideology and Data Science are interesting in their own right, but may also be of paramount importance for practical applications. Leveraging political linguistics could have profound implications for research on political behavior, and enable a more accessible way of understanding political agendas, revealing antagonistic lexical structures that arise from a set of political parties competing for attention and support in the context of an election.

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Releasing harmful pollutants like ozone and nitrogen dioxide gas into the atmosphere has been a serious concern in today’s era. Such gases are harmful to the health of humans as well as other species and cause damage to the environment. Therefore, it has become necessary to find ways to monitor the air quality around us. With technological advancement, chemical gas sensors equipped with machine or deep learning algorithms can be employed to detect these gases and their concentrations. However, with the use of such algorithms, there comes a challenge to understand why they made certain predictions in human terms. This thesis aims to address this concern by adopting different explainable artificial intelligence (XAI) approaches for the gas sensors that can help understand the reasons behind the predictions made by the models, allowing not only to understand these models but also improve understanding of sensor behaviour.

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Hard disk drive failures are a rare event, nonetheless the occurrence of such failures especially in modern data storage centres can result in catastrophic data losses and large monetary costs. To tackle such problems, companies rely on SMART (Self-Monitoring, Analysis and Report Technology) attributes which monitor the state of drives and report upcoming failures. Therefore, this thesis uses the Backblaze public dataset and aims at forecasting hard drive failures that would happen in 1, 10, 20 and 30 days. Also, SMART features were modeled as lag windows to include past values, with one of the window sizes: current day, last five days or last ten days. For forecasting, machine learning algorithms Random Forest, Linear Support Vector Machine and Multi-layer Perceptron were used. These models were also compared and evaluated using raw, normalized and standardized SMART features in order to observe their forecasting abilities. The training and testing of models was set in Azure machine learning, using various Jupyter Notebooks and blob storage for storing the data. Thus, the obtained results showed that models were able to forecast failures that would happen in a further future than in a nearer one. On the other hand, including past feature values by creating lag windows had no significant impact on forecasting performances. Best results were obtained by Linear Support Vector Machine while looking at 30 days into the future and at a lag window including SMART features of the current day only, with an F1-score of 51%. The other two models, namely Random Forest and Linear Support Vector Machine also reported performance increases while looking at 10 and 20 days into the future. Thus, all models performed the worst while looking 1 day into the future. In relation to including past SMART feature values, visible positive impact was not reported considering overall performance of the models. Moreover, in relation to data normalization and standardization, Linear Support Vector Machine reported only a slight increase in its performance, whereas Random Forest did not visibly increase in its performance at all. In regard to Multi-layer Perceptron, while training on raw SMART features resulted in a F1-Score of 0%, using standardized data, brought increased outcomes in the forecasting performance. Overall, Linear Support Vector Machine reported the best hard drive failure forecasting results in comparison to Random Forest and Multi-layer Perceptron and therefore is considered as the best forecasting model in this thesis.

Argumentation can be understood as the activity of using arguments to convince, agree, or disagree people with people about a point of view. In our daily lives, argumentation is one of the most common behaviors in applying natural language. For example, social media users would respond to controversial topics using their stances and opinions. The collection and analysis of user ideas are critical to studying social phenomena and trends. However, it is hard to analyze all collected arguments since processing enormous data size needs much time and human costs, which is undesirable. This requires more efficient methods. A possible solution might be the research in computational argumentation because computers can handle numerous data efficiently. Besides social phenomenon analysis, other areas such as business and linguistics also benefit from studying argumentation.

Computational argumentation is a growing research field that yield many new methods in this area. This work is inspired by a study investigating in transforming natural language texts to argument graphs. In this thesis, we base on the previous studies and explore deep into the steps of each part, including classifying major claims, inferring relations between statements, and constructing argument graphs, and investigate in approaches for improvement. We propose a new method in major claim classification, which is to find the statement describing the core idea of the discussion, and obtain an excellent enhancement. Moreover, we introduce state-of-the-art methods to estimate the relations between arguments. We suggest six methods in the step of argument graph construction, which also give satisfactory results. There are some limitations to our research. We discuss them and explore some possible further improvements for achieving a better result in the future studies.

An information retrieval system’s purpose is to return results that are relevant to the user’s query. Information relevant to the user’s request may not exist in the user’s native language in some instances. It’s also possible that the user can read papers in languages other than his or her native tongue but has trouble forming inquiries in them. The primary goal of Multilingual Information Extraction is to locate the most relevant information accessible, regardless of the query language.

Artificial intelligence (AI) has become an increasingly popular research field in recent years. Similarly, Natural Language Processing (NLP) has become an important point of discussion. Neural networks, do exceptionally well in this field. The speed and performance of neural networks dealing with diverse NLP tasks have been greatly enhanced due to a variety of effective learning methods and technologies.

The recent advances in NLP transfer learning have resulted in powerful models, mostly from the tech giants like Google, Facebook, Microsoft, etc. which perform well on NLP tasks in the general domain. In this thesis, we are going to fine-tune multilingual transformer models for the domain of engineering data both in English and German Languages. Hence, we need a language independent model - which can able to learn it’s parameters (weights and bias) of any language-specific features. First, we will describe how multilingual transfer is implemented, with the focus on state-of-the-art transformer models. Then, in the methodology part, we leverage our engineering domain data of English-German languages to fine-tune multilingual transformer models.

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