Vocabulary Evolution on the Semantic Web: From Changes to Evolution of Vocabularies and its Impact on the Data

The main objective of the Semantic Web is to provide data on the web well-defined meaning. Vocabularies are used for modeling data in the web, provide a shared understanding of a domain and consist of a collection of types and properties. These types and properties are so-called terms. A vocabulary can import terms from other vocabularies, and data publishers use vocabulary terms for modeling data. Importing terms via vocabularies results in a Network of Linked vOcabularies (NeLO). Vocabularies are subject to change during their lifetime. When vocabularies change, the published data become a problem if they are not updated based on these changes. So far, there has been no study that analyzes vocabulary changes over time. Furthermore, it is unknown how data publishers reflect on such vocabulary changes. Ontology engineers and data publishers may not be aware of the changes in the vocabulary terms that have already happened since they occur rather rarely. This work addresses the problem of vocabulary changes and their impact on other vocabularies and the published data. We analyzed the changes of vocabularies and their reuse. We selected the most dominant vocabularies, based on their use by data publishers. Additionally, we analyzed the changes of 994 vocabularies. Furthermore, we analyzed various vocabularies to better understand by whom and how they are used in the modeled data, and how these changes are adopted in the Linked Open Data cloud. We computed the state of the NeLO from the available versions of vocabularies for over 17 years. We analyzed the static parameters of the NeLO such as its size, density, average degree, and the most important vocabularies at certain points in time. We further investigated how NeLO changes over time, specifically measuring the impact of a change in one vocabulary on others, how the reuse of terms changes, and the importance of vocabulary changes. Our results show that the vocabularies are highly static, and many of the changes occurred in annotation properties. Additionally, 16% of the existing terms are reused by other vocabularies, and some of the deprecated and deleted terms are still reused. Furthermore, most of the newly coined terms are adopted immediately. Our results show that even if the change frequency of terms is rather low, it can have a high impact on the data due to a large amount of data on the web. Moreover, due to a large number of vocabularies in the NeLO, and therefore the increase of available terms, the percentage of imported terms compared with the available ones has decreased over time. Additionally, based on the scores of the average number of exports for the vocabularies in the NeLO, some vocabularies have become more popular over time. Overall, understanding the evolution of vocabulary terms is important for ontology engineers and data publishers to avoid wrong assumptions about the data published on the web. Furthermore, it may foster a better understanding of the impact of the changes in vocabularies and how they are adopted to possibly learn from previous experience. Our results provide for the first time in-depth insights into the structure and evolution of the NeLO. Supported by proper tools exploiting the analysis of this thesis, it may help ontology engineers to identify data modeling shortcomings and assess the dependencies implied by the reusing of a specific vocabulary.Das Hauptziel des Semantic Web ist es, den Daten im Web eine klar definierte Bedeutung zu geben. Vokabulare werden zum Modellieren von Daten im Web verwendet. Vokabulare vermitteln ein gemeinsames Verständnis einer Domäne und bestehen aus einer Sammlung von Typen und Eigenschaften. Diese Typen und Eigenschaften sind sogenannte Begriffe. Ein Vokabular kann Begriffe aus anderen Vokabularen importieren, und Datenverleger verwenden die Begriffe der Vokabulare zum Modellieren von Daten. Durch das Importieren von Begriffen entsteht ein Netzwerk verknüpfter Vokabulare (NeLO). Vokabulare können sich im Laufe der Zeit ändern. Wenn sich Vokabulare ändern, kann dies zu Problemen mit bereits veröffentlichten Daten führen, falls diese nicht entsprechend angepasst werden. Bisher gibt es keine Studie, die die Veränderung der Vokabulare im Laufe der Zeit analysiert. Darüber hinaus ist nicht bekannt, inwiefern bereits veröffentlichte Daten an diese Veränderungen angepasst werden. Verantwortliche für Ontologien und Daten sind sich möglicherweise der Änderungen in den Vokabularen nicht bewusst, da solche Änderungen eher selten vorkommen. Diese Arbeit befasst sich mit dem Problem der Änderung von Vokabularen und deren Auswirkung auf andere Vokabulare sowie die Daten. Wir analysieren die Änderung von Vokabularen und deren Wiederverwendung. Für unsere Analyse haben wir diejenigen Vokabulare ausgewählt, die am häufigsten verwendet werden. Zusätzlich analysieren wir die Änderungen von 994 Vokabularen aus dem Verzeichnis "Linked Open Vocabulary". Wir analysieren die Vokabulare, um zu verstehen, von wem und wie sie in den modellierten Daten verwendet werden und inwiefern Änderungen in die Linked Open Data Cloud übernommen werden. Wir beobachten den Status von NeLO aus den verfügbaren Versionen der Vokabulare über einen Zeitraum von 17 Jahren. Wir analysieren statische Parameter von NeLO wie Größe, Dichte, Durchschnittsgrad und die wichtigsten Vokabulare zu bestimmten Zeitpunkten. Wir untersuchen weiter, wie sich NeLO mit der Zeit ändert. Insbesondere messen wir die Auswirkung einer Änderung in einem Vokabular auf andere, wie sich die Wiederverwendung von Begriffen ändert und wie wichtig Änderungen im Vokabular sind. Unsere Ergebnisse zeigen, dass die Vokabulare sehr statisch sind und viele Änderungen an sogenannten Annotations-Properties vorgenommen wurden. Darüber hinaus werden 16% der vorhandenen Begriffen von anderen Vokabularen wiederverwendet, und einige der veralteten und gelöschten Begriffe werden weiterhin wiederverwendet. Darüber hinaus werden die meisten neu erstellten Begriffe unmittelbar verwendet. Unsere Ergebnisse zeigen, dass selbst wenn die Häufigkeit von Änderungen an Vokabularen eher gering ist, so kann dies aufgrund der großen Datenmenge im Web erhebliche Auswirkungen haben. Darüber hinaus hat sich aufgrund einer großen Anzahl von Vokabularen in NeLO und damit der Zunahme der verfügbaren Begriffe der Prozentsatz der importierten Begriffe im Vergleich zu den verfügbaren Begriffen im Laufe der Zeit verringert. Basierend auf den Ergebnissen der durchschnittlichen Anzahl von Exporten für die Vokabulare in NeLO sind einige Vokabulare im Laufe der Zeit immer beliebter geworden. Insgesamt ist es für Verantwortliche für Ontologien und Daten wichtig, die Entwicklung der Vokabulare zu verstehen, um falsche Annahmen über die im Web veröffentlichten Daten zu vermeiden. Darüber hinaus ermöglichen unsere Ergebnisse ein besseres Verständnis der Auswirkungen von Änderungen in Vokabularen, sowie deren Nachnutzung, um möglicherweise aus früheren Erfahrungen zu lernen. Unsere Ergebnisse bieten erstmals detaillierte Einblicke in die Struktur und Entwicklung des Netzwerks der verknüpften Vokabularen. Unterstützt von geeigneten Tools für die Analyse in dieser Arbeit, kann es Verantwortlichen für Ontologien helfen, Mängel in der Datenmodellierung zu identifizieren und Abhängigkeiten zu bewerten, die durch die Wiederverwendung eines bestimmten Vokabulars entstehenden

Completeness and Consistency Analysis for Evolving Knowledge Bases

Assessing the quality of an evolving knowledge base is a challenging task as it often requires to identify correct quality assessment procedures. Since data is often derived from autonomous, and increasingly large data sources, it is impractical to manually curate the data, and challenging to continuously and automatically assess their quality. In this paper, we explore two main areas of quality assessment related to evolving knowledge bases: (i) identification of completeness issues using knowledge base evolution analysis, and (ii) identification of consistency issues based on integrity constraints, such as minimum and maximum cardinality, and range constraints. For completeness analysis, we use data profiling information from consecutive knowledge base releases to estimate completeness measures that allow predicting quality issues. Then, we perform consistency checks to validate the results of the completeness analysis using integrity constraints and learning models. The approach has been tested both quantitatively and qualitatively by using a subset of datasets from both DBpedia and 3cixty knowledge bases. The performance of the approach is evaluated using precision, recall, and F1 score. From completeness analysis, we observe a 94% precision for the English DBpedia KB and 95% precision for the 3cixty Nice KB. We also assessed the performance of our consistency analysis by using five learning models over three sub-tasks, namely minimum cardinality, maximum cardinality, and range constraint. We observed that the best performing model in our experimental setup is the Random Forest, reaching an F1 score greater than 90% for minimum and maximum cardinality and 84% for range constraints.Comment: Accepted for Journal of Web Semantic

A Personal Research Agent for Semantic Knowledge Management of Scientific Literature

The unprecedented rate of scientific publications is a major threat to the productivity of knowledge workers, who rely on scrutinizing the latest scientific discoveries for their daily tasks. Online digital libraries, academic publishing databases and open access repositories grant access to a plethora of information that can overwhelm a researcher, who is looking to obtain fine-grained knowledge relevant for her task at hand. This overload of information has encouraged researchers from various disciplines to look for new approaches in extracting, organizing, and managing knowledge from the immense amount of available literature in ever-growing repositories. In this dissertation, we introduce a Personal Research Agent that can help scientists in discovering, reading and learning from scientific documents, primarily in the computer science domain. We demonstrate how a confluence of techniques from the Natural Language Processing and Semantic Web domains can construct a semantically-rich knowledge base, based on an inter-connected graph of scholarly artifacts – effectively transforming scientific literature from written content in isolation, into a queryable web of knowledge, suitable for machine interpretation. The challenges of creating an intelligent research agent are manifold: The agent's knowledge base, analogous to his 'brain', must contain accurate information about the knowledge `stored' in documents. It also needs to know about its end-users' tasks and background knowledge. In our work, we present a methodology to extract the rhetorical structure (e.g., claims and contributions) of scholarly documents. We enhance our approach with entity linking techniques that allow us to connect the documents with the Linked Open Data (LOD) cloud, in order to enrich them with additional information from the web of open data. Furthermore, we devise a novel approach for automatic profiling of scholarly users, thereby, enabling the agent to personalize its services, based on a user's background knowledge and interests. We demonstrate how we can automatically create a semantic vector-based representation of the documents and user profiles and utilize them to efficiently detect similar entities in the knowledge base. Finally, as part of our contributions, we present a complete architecture providing an end-to-end workflow for the agent to exploit the opportunities of linking a formal model of scholarly users and scientific publications

Federated knowledge base debugging in DL-Lite A

Due to the continuously growing amount of data the federation of different and distributed data sources gained increasing attention. In order to tackle the challenge of federating heterogeneous sources a variety of approaches has been proposed. Especially in the context of the Semantic Web the application of Description Logics is one of the preferred methods to model federated knowledge based on a well-defined syntax and semantics. However, the more data are available from heterogeneous sources, the higher the risk is of inconsistency – a serious obstacle for performing reasoning tasks and query answering over a federated knowledge base. Given a single knowledge base the process of knowledge base debugging comprising the identification and resolution of conflicting statements have been widely studied while the consideration of federated settings integrating a network of loosely coupled data sources (such as LOD sources) has mostly been neglected. In this thesis we tackle the challenging problem of debugging federated knowledge bases and focus on a lightweight Description Logic language, called DL-LiteA, that is aimed at applications requiring efficient and scalable reasoning. After introducing formal foundations such as Description Logics and Semantic Web technologies we clarify the motivating context of this work and discuss the general problem of information integration based on Description Logics. The main part of this thesis is subdivided into three subjects. First, we discuss the specific characteristics of federated knowledge bases and provide an appropriate approach for detecting and explaining contradictive statements in a federated DL-LiteA knowledge base. Second, we study the representation of the identified conflicts and their relationships as a conflict graph and propose an approach for repair generation based on majority voting and statistical evidences. Third, in order to provide an alternative way for handling inconsistency in federated DL-LiteA knowledge bases we propose an automated approach for assessing adequate trust values (i.e., probabilities) at different levels of granularity by leveraging probabilistic inference over a graphical model. In the last part of this thesis, we evaluate the previously developed algorithms against a set of large distributed LOD sources. In the course of discussing the experimental results, it turns out that the proposed approaches are sufficient, efficient and scalable with respect to real-world scenarios. Moreover, due to the exploitation of the federated structure in our algorithms it further becomes apparent that the number of identified wrong statements, the quality of the generated repair as well as the fineness of the assessed trust values profit from an increasing number of integrated sources

Scalable Quality Assessment of Linked Data

In a world where the information economy is booming, poor data quality can lead to adverse consequences, including social and economical problems such as decrease in revenue. Furthermore, data-driven indus- tries are not just relying on their own (proprietary) data silos, but are also continuously aggregating data from different sources. This aggregation could then be re-distributed back to “data lakes”. However, this data (including Linked Data) is not necessarily checked for its quality prior to its use. Large volumes of data are being exchanged in a standard and interoperable format between organisations and published as Linked Data to facilitate their re-use. Some organisations, such as government institutions, take a step further and open their data. The Linked Open Data Cloud is a witness to this. However, similar to data in data lakes, it is challenging to determine the quality of this heterogeneous data, and subsequently to make this information explicit to data consumers. Despite the availability of a number of tools and frameworks to assess Linked Data quality, the current solutions do not aggregate a holistic approach that enables both the assessment of datasets and also provides consumers with quality results that can then be used to find, compare and rank datasets’ fitness for use. In this thesis we investigate methods to assess the quality of (possibly large) linked datasets with the intent that data consumers can then use the assessment results to find datasets that are fit for use, that is; finding the right dataset for the task at hand. Moreover, the benefits of quality assessment are two-fold: (1) data consumers do not need to blindly rely on subjective measures to choose a dataset, but base their choice on multiple factors such as the intrinsic structure of the dataset, therefore fostering trust and reputation between the publishers and consumers on more objective foundations; and (2) data publishers can be encouraged to improve their datasets so that they can be re-used more. Furthermore, our approach scales for large datasets. In this regard, we also look into improving the efficiency of quality metrics using various approximation techniques. However the trade-off is that consumers will not get the exact quality value, but a very close estimate which anyway provides the required guidance towards fitness for use. The central point of this thesis is not on data quality improvement, nonetheless, we still need to understand what data quality means to the consumers who are searching for potential datasets. This thesis looks into the challenges faced to detect quality problems in linked datasets presenting quality results in a standardised machine-readable and interoperable format for which agents can make sense out of to help human consumers identifying the fitness for use dataset. Our proposed approach is more consumer-centric where it looks into (1) making the assessment of quality as easy as possible, that is, allowing stakeholders, possibly non-experts, to identify and easily define quality metrics and to initiate the assessment; and (2) making results (quality metadata and quality reports) easy for stakeholders to understand, or at least interoperable with other systems to facilitate a possible data quality pipeline. Finally, our framework is used to assess the quality of a number of heterogeneous (large) linked datasets, where each assessment returns a quality metadata graph that can be consumed by agents as Linked Data. In turn, these agents can intelligently interpret a dataset’s quality with regard to multiple dimensions and observations, and thus provide further insight to consumers regarding its fitness for use