Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects

The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts

Crowd-annotation and LoD-based semantic indexing of content in multi-disciplinary web repositories to improve search results

Searching for relevant information in multi-disciplinary web repositories is becoming a topic of increasing interest among the computer science research community. To date, methods and techniques to extract useful and relevant information from online repositories of research data have largely been based on static full text indexing which entails a ‘produce once and use forever’ kind of strategy. That strategy is fast becoming insufficient due to increasing data volume, concept obsolescence, and complexity and heterogeneity of content types in web repositories. We propose that by automatic semantic annotation of content in web repositories (using Linked Open Data or LoD sources) without using domain-specific ontologies, we can sustain the performance of searching by retrieving highly relevant search results. Secondly, we claim that by expert crowd-annotation of content on top of automatic semantic annotation, we can enrich the semantic index over time to augment the contextual value of content in web repositories so that they remain findable despite changes in language, terminology and scientific concepts. We deployed a custom- built annotation, indexing and searching environment in a web repository website that has been used by expert annotators to annotate webpages using free text and vocabulary terms. We present our findings based on the annotation and tagging data on top of LoD-based annotations and the overall modus operandi. We also analyze and demonstrate that by adding expert annotations to the existing semantic index, we can improve the relationship between query and documents using Cosine Similarity Measures (CSM)

Requirements for Data Quality Metrics

Data quality and especially the assessment of data quality have been intensively discussed in research and practice alike. To support an economically oriented management of data quality and decision making under uncertainty, it is essential to assess the data quality level by means of well-founded metrics. However, if not adequately defined, these metrics can lead to wrong decisions and economic losses. Therefore, based on a decision-oriented framework, we present a set of five requirements for data quality metrics. These requirements are relevant for a metric that aims to support an economically oriented management of data quality and decision making under uncertainty. We further demonstrate the applicability and efficacy of these requirements by evaluating five data quality metrics for different data quality dimensions. Moreover, we discuss practical implications when applying the presented requirements

Concepts and Methods from Artificial Intelligence in Modern Information Systems – Contributions to Data-driven Decision-making and Business Processes

Today, organizations are facing a variety of challenging, technology-driven developments, three of the most notable ones being the surge in uncertain data, the emergence of unstructured data and a complex, dynamically changing environment. These developments require organizations to transform in order to stay competitive. Artificial Intelligence with its fields decision-making under uncertainty, natural language processing and planning offers valuable concepts and methods to address the developments. The dissertation at hand utilizes and furthers these contributions in three focal points to address research gaps in existing literature and to provide concrete concepts and methods for the support of organizations in the transformation and improvement of data-driven decision-making, business processes and business process management. In particular, the focal points are the assessment of data quality, the analysis of textual data and the automated planning of process models. In regard to data quality assessment, probability-based approaches for measuring consistency and identifying duplicates as well as requirements for data quality metrics are suggested. With respect to analysis of textual data, the dissertation proposes a topic modeling procedure to gain knowledge from CVs as well as a model based on sentiment analysis to explain ratings from customer reviews. Regarding automated planning of process models, concepts and algorithms for an automated construction of parallelizations in process models, an automated adaptation of process models and an automated construction of multi-actor process models are provided