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A Review on Recent Advances in Video-based Learning Research: Video Features, Interaction, Tools, and Technologies
Human learning shifts stronger than ever towards online settings, and especially towards video platforms. There is an abundance of tutorials and lectures covering diverse topics, from fixing a bike to particle physics. While it is advantageous that learning resources are freely available on the Web, the quality of the resources varies a lot. Given the number of available videos, users need algorithmic support in finding helpful and entertaining learning resources. In this paper, we present a review of the recent research literature (2020-2021) on video-based learning. We focus on publications that examine the characteristics of video content, analyze frequently used features and technologies, and, finally, derive conclusions on trends and possible future research directions.
Understanding Class Representations: An Intrinsic Evaluation of Zero-Shot Text Classification
Frequently, Text Classification is limited by insufficient training data. This problem is addressed by Zero-Shot Classification through the inclusion of external class definitions and then exploiting the relations between classes seen during training and unseen classes (Zero-shot). However, it requires a class embedding space capable of accurately representing the semantic relatedness between classes. This work defines an intrinsic evaluation based on greater-than constraints to provide a better understanding of this relatedness. The results imply that textual embeddings are able to capture more semantics than Knowledge Graph embeddings, but combining both modalities yields the best performance.
Check square at CheckThat! 2020: Claim Detection in Social Media via Fusion of Transformer and Syntactic Features
In this digital age of news consumption, a news reader has the ability to react, express and share opinions with others in a highly interactive and fast manner. As a consequence, fake news has made its way into our daily life because of very limited capacity to verify news on the Internet by large companies as well as individuals. In this paper, we focus on solving two problems which are part of the fact-checking ecosystem that can help to automate fact-checking of claims in an ever increasing stream of content on social media. For the first prob-lem, claim check-worthiness prediction, we explore the fusion of syntac-tic features and deep transformer Bidirectional Encoder Representations from Transformers (BERT) embeddings, to classify check-worthiness of a tweet, i.e. whether it includes a claim or not. We conduct a detailed feature analysis and present our best performing models for English and Arabic tweets. For the second problem, claim retrieval, we explore the pre-trained embeddings from a Siamese network transformer model (sentence-transformers) specifically trained for semantic textual similar-ity, and perform KD-search to retrieve verified claims with respect to a query tweet.
Improving Zero-Shot Text Classification with Graph-based Knowledge Representations
Insufficient training data is a key challenge for text classification. In particular, long-tail class distributions and emerging, new classes do not provide any training data for specific classes. Therefore, such a zeroshot setting must incorporate additional, external knowledge to enable transfer learning by connecting the external knowledge of previously unseen classes to texts. Recent zero-shot text classifier utilize only distributional semantics defined by large language models and based on class names or natural language descriptions. This implicit knowledge contains ambiguities, is not able to capture logical relations nor is it an efficient representation of factual knowledge. These drawbacks can be avoided by introducing explicit, external knowledge. Especially, knowledge graphs provide such explicit, unambiguous, and complementary, domain specific knowledge. Hence, this thesis explores graph-based knowledge as additional modality for zero-shot text classification. Besides a general investigation of this modality, the influence on the capabilities of dealing with domain shifts by including domain-specific knowledge is explored.
Formalizing Gremlin pattern matching traversals in an integrated graph Algebra
Graph data management (also called NoSQL) has revealed beneficial characteristics in terms of flexibility and scalability by differ-ently balancing between query expressivity and schema flexibility. This peculiar advantage has resulted into an unforeseen race of developing new task-specific graph systems, query languages and data models, such as property graphs, key-value, wide column, resource description framework (RDF), etc. Present-day graph query languages are focused towards flex-ible graph pattern matching (aka sub-graph matching), whereas graph computing frameworks aim towards providing fast parallel (distributed) execution of instructions. The consequence of this rapid growth in the variety of graph-based data management systems has resulted in a lack of standardization. Gremlin, a graph traversal language, and machine provide a common platform for supporting any graph computing sys-tem (such as an OLTP graph database or OLAP graph processors). In this extended report, we present a formalization of graph pattern match-ing for Gremlin queries. We also study, discuss and consolidate various existing graph algebra operators into an integrated graph algebra.
Steps towards a Dislocation Ontology for Crystalline Materials
The field of Materials Science is concerned with, e.g., properties and performance of materials. An important class of materials are crystalline materials that usually contain “dislocations" - a line-like defect type. Dislocation decisively determine many important materials properties. Over the past decades, significant effort was put into understanding dislocation behavior across different length scales both with experimental characterization techniques as well as with simulations. However, for describing such dislocation structures there is still a lack of a common standard to represent and to connect dislocation domain knowledge across different but related communities. An ontology offers a common foundation to enable knowledge representation and data interoperability, which are important components to establish a “digital twin". This paper outlines the first steps towards the design of an ontology in the dislocation domain and shows a connection with the already existing ontologies in the materials science and engineering domain.
Demonstration of a customizable knowledge graph visualization framework
In the context of the Semantic Web, various visualization methods and tools exist. However, suitable visualizations are highly de-pendent on individual use cases and targeted user groups. Therefore, existing solutions require modifications and adjustments to meet the de-mands of other use cases and user groups. In this demo, we present an approach for a unified framework addressing customizable visual rep-resentations of knowledge graphs. Our approach refines the commonly used steps in the visualization generation process (i.e., data access, map-ping to visual primitives, and rendering) for Semantic Web contexts. Separation of concerns for individual steps and a modular and customiz-able architecture build the foundation for a pipeline-based visualization framework. The framework enables the creation and selection of the right components for the right tasks, realizing a variety of use cases and visual representations in Semantic Web contexts.
On the Role of Images for Analyzing Claims in Social Media
Fake news is a severe problem in social media. In this paper, we present an empirical study on visual, textual, and multimodal models for the tasks of claim, claim check-worthiness, and conspiracy detection, all of which are related to fake news detection. Recent work suggests that images are more influential than text and often appear alongside fake text. To this end, several multimodal models have been proposed in recent years that use images along with text to detect fake news on social media sites like Twitter. However, the role of images is not well understood for claim detection, specifically using transformer-based textual and multimodal models. We investigate state-of-the-art models for images, text (Transformer-based), and multimodal information for four different datasets across two languages to understand the role of images in the task of claim and conspiracy detection.
zbMATH Open: API Solutions and Research Challenges
We present zbMATH Open, the most comprehensive collection of reviews and bibliographic metadata of scholarly literature in mathematics. Besides our website zbMATH.org which is openly accessible since the beginning of this year, we provide API endpoints to offer our data. APIs improve interoperability with others, i.e., digital libraries, and allow using our data for research purposes. In this article, we (1) illustrate the current and future overview of the services offered by zbMATH; (2) present the initial version of the zbMATH links API; (3) analyze potentials and limitations of the links API based on the example of the NIST Digital Library of Mathematical Functions; (4) and finally, present thezbMATHOpen dataset as a research resource and discuss connected open research problems.
SHACL Constraint Validation during SPARQL Query Processing
The importance of knowledge graphs is increasing. Due to their application in more and more real-world use-cases the data quality issue has to be addressed. The Shapes Constraint Language (SHACL) is the W3C recommendation language for defining integrity constraints over knowledge graphs expressed in the Resource Description Framework (RDF). Annotating SPARQL query results with metadata from the SHACL validation provides a better understanding of the knowledge graph and its data quality. We propose a query engine that is able to efficiently evaluate which instances in the knowledge graph fulfill the requirements from the SHACL shape schema and annotate the SPARQL query result with this metadata. Hence, adding the dimension of explainability to SPARQL query processing. Our preliminary analysis shows that the proposed optimizations performed for SHACL validation during SPARQL query processing increase the performance compared to a naive approach. However, in some queries the naive approach outperforms the optimizations. This shows that more work needs to be done in this topic to fully comprehend all impacting factors and to identify the amount of overhead added to the query execution.