Maleshkova, Maria

The field of virtual reality (VR) is no longer limited to the entertainment sector, and the use also increased in the sector of healthcare and therapy. This paper attempts to compare the effectiveness of a Behavioral Avoidance Test for spider phobia in virtual reality to in vivo. To do this, two different BAT approaches are designed for both in vivo and in virtuo to be compared by a group of phobic participants. In addition, physiological data of the participants were recorded to investigate this comparison on a physiological level.

This paper addresses the task of conversational question answering (ConvQA) over knowledge graphs (KGs). The majority of existing ConvQA methods rely on full supervision signals with a strict assumption of the availability of gold logical forms of queries to extract answers from the KG. However, creating such a gold logical form is not viable for each potential question in a real-world scenario. Hence, in the case of missing gold logical forms, the existing information retrieval-based approaches use weak supervision via heuristics or reinforcement learning, formulating ConvQA as a KG path ranking problem. Despite missing gold logical forms, an abundance of conversational contexts, such as entire dialog history with fluent responses and domain information, can be incorporated to effectively reach the correct KG path. This work proposes a contrastive representation learning-based approach to rank KG paths effectively. Our approach solves two key challenges. Firstly, it allows weak supervision-based learning that omits the necessity of gold annotations. Second, it incorporates the conversational context (entire dialog history and domain information) to jointly learn its homogeneous representation with KG paths to improve contrastive representations for effective path ranking. We evaluate our approach on standard datasets for ConvQA, on which it significantly outperforms existing baselines on all domains and overall. Specifically, in some cases, the Mean Reciprocal Rank (MRR) and Hit@5 ranking metrics improve by absolute 10 and 18 points, respectively, compared to the state-of-the-art performance. © 2022 ACM.

In recent years, there have been significant developments in Question Answering over Knowledge Graphs (KGQA). Despite all the notable advancements, current KGQA systems only focus on answer generation techniques and not on answer verbalization. However, in real-world scenarios (e.g., voice assistants such as Alexa, Siri, etc.), users prefer verbalized answers instead of a generated response. This paper addresses the task of answer verbalization for (complex) question answering over knowledge graphs. In this context, we propose a multi-task-based answer verbalization framework: VOGUE (Verbalization thrOuGh mUlti-task lEarning). The VOGUE framework attempts to generate a verbalized answer using a hybrid approach through a multi-task learning paradigm. Our framework can generate results based on using questions and queries as inputs concurrently. VOGUE comprises four modules that are trained simultaneously through multi-task learning. We evaluate our framework on existing datasets for answer verbalization, and it outperforms all current baselines on both BLEU and METEOR scores. © 2021, Springer Nature Switzerland AG.

Medical knowledge graphs (KGs) constructed from Electronic Medical Records (EMR) contain abundant information about patients and medical entities. The utilization of KG embedding models on these data has proven to be efficient for different medical tasks. However, existing models do not properly incorporate patient demographics and most of them ignore the probabilistic features of the medical KG. In this paper, we propose DARLING (Demographic Aware pRobabiListic medIcal kNowledge embeddinG), a demographic-aware medical KG embedding framework that explicitly incorporates demographics in the medical entities space by associating patient demographics with a corresponding hyperplane. Our framework leverages the probabilistic features within the medical entities for learning their representations through demographic guidance. We evaluate DARLING through link prediction for treatments and medicines, on a medical KG constructed from EMR data, and illustrate its superior performance compared to existing KG embedding models.

This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.

In this paper, we introduce the MLM (Multiple Languages and Modalities) dataset - a new resource to train and evaluate multitask systems on samples in multiple modalities and three languages. The generation process and inclusion of semantic data provide a resource that further tests the ability for multitask systems to learn relationships between entities. The dataset is designed for researchers and developers who build applications that perform multiple tasks on data encountered on the web and in digital archives. A second version of MLM provides a geo-representative subset of the data with weighted samples for countries of the European Union. We demonstrate the value of the resource in developing novel applications in the digital humanities with a motivating use case and specify a benchmark set of tasks to retrieve modalities and locate entities in the dataset. Evaluation of baseline multitask and single task systems on the full and geo-representative versions of MLM demonstrate the challenges of generalising on diverse data. In addition to the digital humanities, we expect the resource to contribute to research in multimodal representation learning, location estimation, and scene understanding.

The Digital Twin is currently a widely discussed topic for presenting and exchanging information on physical assets through vir- tual networks. Especially the digitisation of the manufacturing industry, Industry 4.0, drives the implementation of Digital Twins as part of inte- grated production processes. Hence, the protection of sensitive data be- comes an evident challenge. We contribute by determining the require- ments of current scenarios, the descriptive expressiveness of necessary vocabularies, and by formalising the involved operations. The Asset Ad- ministration Shell is used as a concrete metamodel for Digital Twins and is combined with the data sovereignty and enforcement concepts of the International Data Spaces to illustrate how these concepts can be implemented into a comprehensive Industry 4.0 scenario. Copyright © 2020 for this paper by its authors.

The digital revolution affects every aspect of society and economy. In particular, the manufacturing industry faces a new age of production processes and connected collaboration. The underlying ideas and concepts, often also framed as a new "Internet of Things", transfer IT technologies to the shop floor, entailing major challenges regarding the heterogeneity of the domain. On the other hand, web technologies have already proven their value in distributed settings. SOLID (derived from "social linked data") is a recent approach to decentralize data control and standardize interactions for social applications in the web. Extending this approach towards industrial applications has the potential to bridge the gap between theWorldWideWeb and local manufacturing environments. This paper proposes SOLIOT-a combination of lightweight industrial protocols with the integration and data control provided by SOLID. An in-depth requirement analysis examines the potential but also current limitations of the approach. The conceptual capabilities are outlined, compared and extended for the IoT protocols CoAP and MQTT. The feasibility of the approach is illustrated through an open-source implementation, which is evaluated in a virtual test bed and a detailed analysis of the proposed components.

Question Answering (QA) systems over Knowledge Graphs (KGs) aim to provide a concise answer to a given natural language question. Despite the significant evolution of QA methods over the past years, there are still some core lines of work, which are lagging behind. This is especially true for methods and datasets that support the verbalization of answers in natural language. Specifically, to the best of our knowledge, none of the existing Question Answering datasets provide any verbalization data for the question-query pairs. Hence, we aim to fill this gap by providing the first QA dataset VQuAnDa that includes the verbalization of each answer. We base VQuAnDa on a commonly used large-scale QA dataset – LC-QuAD, in order to support compatibility and continuity of previous work. We complement the dataset with baseline scores for measuring future training and evaluation work, by using a set of standard sequence to sequence models and sharing the results of the experiments. This resource empowers researchers to train and evaluate a variety of models to generate answer verbalizations.

One of the most crucial tasks for today’s knowledge workers is to get and retain a thorough overview on the latest state of the art. Especially in dynamic and evolving domains, the amount of relevant sources is constantly increasing, updating and overruling previous methods and approaches. For instance, the digital transformation of manufacturing systems, called Industry 4.0, currently faces an overwhelming amount of standardization efforts and reference initiatives, resulting in a sophisticated information environment. We propose a structured dataset in the form of a semantically annotated knowledge graph for Industry 4.0 related standards, norms and reference frameworks. The graph provides a Linked Data-conform collection of annotated, classified reference guidelines supporting newcomers and experts alike in understanding how to implement Industry 4.0 systems. We illustrate the suitability of the graph for various use cases, its already existing applications, present the maintenance process and evaluate its quality. © Springer Nature Switzerland AG 2020.