Multimodal knowledge graphs often separate easily represented information (text) from that which is not (multimedia documents like images, videos, or audio). This severely limits query expressiveness, as the engines lack access to the node contents stored externally. We present MeGraS, the MediaGraph Store, a novel storage and query engine for multimodal knowledge graphs. By storing multimedia documents directly in the graph, MeGraS allows the query engine to leverage their content for enhanced capabilities, making it natively capable of performing operations such as k-NN, segmentation, or deriving non-materialized relations based on visual features. To demonstrate this, we incorporate and extend the pattern-matching query language SPARQL, resulting in a unified framework for storing and managing multimodal knowledge graphs with advanced expressiveness. MeGraS is available as open-source software: http://megras.org

Most scholarly search services only provide basic text-matching or similarity-based searches, with limited operations that require manual configuration, such as sorting and filtering by specific metadata attributes. These capabilities are insufficient for researchers who often have queries that involve complex constraints and operations, such as ''enumerating the authors of a given paper along with the venues where they have published other papers.'' In this work, we develop an interactive and intuitive scholarly question answering system called NLQxform-UI, which allows users to pose complex queries in the form of natural language questions. It is capable of automatically translating these questions into SPARQL queries that can be executed over the DBLP knowledge graph to retrieve expected answers. Furthermore, the users can interact with each step of the answering process and browse the final results in a web-based interface. A video recording of our system is available at https://youtu.be/elq8CPykiyk Additionally, the system has been completely open-sourced: https://github.com/ruijie-wang-uzh/NLQxform-UI

Current approaches to Argument Mining (AM) tend to take a holistic or black-box view of the overall pipeline.This paper, in contrast, aims to provide a solution to achieve increased performance based on current components instead of independent all-new solutions.To that end, it presents the Deployment of Recombination and Ensemble methods for Argument Miners (DREAM) framework that allows for the (automated) combination of AM components.Using ensemble methods, DREAM combines sets of AM systems to improve accuracy for the four tasks in the AM pipeline.Furthermore, it leverages recombination by using different argument miners elements throughout the pipeline.Experiments with five systems previously included in a benchmark show that the systems combined with DREAM can outperform the previous best single systems in terms of accuracy measured by an AM benchmark.