INFRAWEBS semantic web service development on the base of knowledge management layer

The paper gives an overview about the ongoing FP6-IST INFRAWEBS project and describes the main layers and software components embedded in an application oriented realisation framework. An important part of INFRAWEBS is a Semantic Web Unit (SWU) – a collaboration platform and interoperable middleware for ontology-based handling and maintaining of SWS. The framework provides knowledge about a specific domain and relies on ontologies to structure and exchange this knowledge to semantic service development modules. INFRAWEBS Designer and Composer are sub-modules of SWU responsible for creating Semantic Web Services using Case-Based Reasoning approach. The Service Access Middleware (SAM) is responsible for building up the communication channels between users and various other modules. It serves as a generic middleware for deployment of Semantic Web Services. This software toolset provides a development framework for creating and maintaining the full-life-cycle of Semantic Web Services with specific application support

POS Tagging and its Applications for Mathematics

Content analysis of scientific publications is a nontrivial task, but a useful and important one for scientific information services. In the Gutenberg era it was a domain of human experts; in the digital age many machine-based methods, e.g., graph analysis tools and machine-learning techniques, have been developed for it. Natural Language Processing (NLP) is a powerful machine-learning approach to semiautomatic speech and language processing, which is also applicable to mathematics. The well established methods of NLP have to be adjusted for the special needs of mathematics, in particular for handling mathematical formulae. We demonstrate a mathematics-aware part of speech tagger and give a short overview about our adaptation of NLP methods for mathematical publications. We show the use of the tools developed for key phrase extraction and classification in the database zbMATH

AATCT-IDS: A Benchmark Abdominal Adipose Tissue CT Image Dataset for Image Denoising, Semantic Segmentation, and Radiomics Evaluation

Methods: In this study, a benchmark \emph{Abdominal Adipose Tissue CT Image Dataset} (AATTCT-IDS) containing 300 subjects is prepared and published. AATTCT-IDS publics 13,732 raw CT slices, and the researchers individually annotate the subcutaneous and visceral adipose tissue regions of 3,213 of those slices that have the same slice distance to validate denoising methods, train semantic segmentation models, and study radiomics. For different tasks, this paper compares and analyzes the performance of various methods on AATTCT-IDS by combining the visualization results and evaluation data. Thus, verify the research potential of this data set in the above three types of tasks. Results: In the comparative study of image denoising, algorithms using a smoothing strategy suppress mixed noise at the expense of image details and obtain better evaluation data. Methods such as BM3D preserve the original image structure better, although the evaluation data are slightly lower. The results show significant differences among them. In the comparative study of semantic segmentation of abdominal adipose tissue, the segmentation results of adipose tissue by each model show different structural characteristics. Among them, BiSeNet obtains segmentation results only slightly inferior to U-Net with the shortest training time and effectively separates small and isolated adipose tissue. In addition, the radiomics study based on AATTCT-IDS reveals three adipose distributions in the subject population. Conclusion: AATTCT-IDS contains the ground truth of adipose tissue regions in abdominal CT slices. This open-source dataset can attract researchers to explore the multi-dimensional characteristics of abdominal adipose tissue and thus help physicians and patients in clinical practice. AATCT-IDS is freely published for non-commercial purpose at: \url{https://figshare.com/articles/dataset/AATTCT-IDS/23807256}.Comment: 17 pages, 7 figure

SEMIAUTOMATIC DETECTION OF STENOSIS AND OCCLUSION OF PULMONARY ARTERIES FOR PATIENTS WITH CHRONIC THROMBOEMBOLIC PULMONARY HYPERTENSION

Chronic thromboembolic pulmonary hypertension (CTEPH) is a severe lung disease defined by the presence of chronic blood clots in the pulmonary arteries accompanied by severe health complications. It is necessary to go through a large set of axial sections from Computed tomography pulmonary angiogram (CTPA) for diagnosing the disease, which is difficult and time consuming for the radiologist. The radiologist's experience plays a significant role, same as subjective factors such as attention and fatigue. In this work we pursued the design and development of the algorithm for semiautomatic detection of pulmonary artery stenoses and clots for diagnosing CTEPH, which is based on the implementation of semantic segmentation using deep convolutional neural networks. Specifically, it is about the use of the DeepLab V3 + model embedded in the Xception architecture. Within this work we focused on stenoses and clots located in larger pulmonary arteries. Anonymized data of patients diagnosed with CTEPH and one healthy patient in the term of the presence of the disease were used for realization of this work. Statistical analysis of the results is divided into two parts: analysis of the created algorithm based on comparison of outputs with ground truth data (manually marked references) and analysis of pathology detection on new data based on comparison of predictions with reference images from the radiologist. The proposed algorithm correctly detects present vascular pathology in 83% of cases (sensitivity) and precisely selects cases where the investigated pathology does not occur in 72% of cases (specificity). The calculated Matthews correlation coefficient is 0.53. This means that the predictive ability of the algorithm is moderate positive. The designed and developed image analysis algorithm offers the radiologist a "second opinion" and it also could enable to increase the sensitivity of CTEPH diagnostics in cooperation with a radiologist.

Are all the frames equally important?

In this work, we address the problem of measuring and predicting temporal video saliency - a metric which defines the importance of a video frame for human attention. Unlike the conventional spatial saliency which defines the location of the salient regions within a frame (as it is done for still images), temporal saliency considers importance of a frame as a whole and may not exist apart from context. The proposed interface is an interactive cursor-based algorithm for collecting experimental data about temporal saliency. We collect the first human responses and perform their analysis. As a result, we show that qualitatively, the produced scores have very explicit meaning of the semantic changes in a frame, while quantitatively being highly correlated between all the observers. Apart from that, we show that the proposed tool can simultaneously collect fixations similar to the ones produced by eye-tracker in a more affordable way. Further, this approach may be used for creation of first temporal saliency datasets which will allow training computational predictive algorithms. The proposed interface does not rely on any special equipment, which allows to run it remotely and cover a wide audience.Comment: CHI'20 Late Breaking Work

SEAN: multi-ontology semantic annotation for highly accurate closed domains

Semantic annotation has gained momentum with the emergence of the current user-generated content paradigm on the Web. The ever-growing quantity of collaborative data sources has resulted in the requirement for efficient approaches to create, integrate and retrieve information more efficiently in an environment where the users ask for accurate information. The main research challenge of the current work is using manual semantic annotation in a highly accurate closed domain, a conceptual domain with a minimal set of concepts where the benefits of adding semantics, search efficiency, optimization and the cost estimations are viable. This paper presents a semantic annotation approach for highly accurate closed domain based on multi-ontology annotation (domain and application ontologies).Publicad

An Automated Method to Enrich and Expand Consumer Health Vocabularies Using GloVe Word Embeddings

Clear language makes communication easier between any two parties. However, a layman may have difficulty communicating with a professional due to not understanding the specialized terms common to the domain. In healthcare, it is rare to find a layman knowledgeable in medical jargon, which can lead to poor understanding of their condition and/or treatment. To bridge this gap, several professional vocabularies and ontologies have been created to map laymen medical terms to professional medical terms and vice versa. Many of the presented vocabularies are built manually or semi-automatically requiring large investments of time and human effort and consequently the slow growth of these vocabularies. In this dissertation, we present an automatic method to enrich existing concepts in a medical ontology with additional laymen terms and also to expand the number of concepts in the ontology that do not have associated laymen terms. Our work has the benefit of being applicable to vocabularies in any domain. Our entirely automatic approach uses machine learning, specifically Global Vectors for Word Embeddings (GloVe), on a corpus collected from a social media healthcare platform to extend and enhance consumer health vocabularies. We improve these vocabularies by incorporating synonyms and hyponyms from the WordNet ontology. By performing iterative feedback using GloVe’s candidate terms, we can boost the number of word occurrences in the co-occurrence matrix allowing our approach to work with a smaller training corpus. Our novel algorithms and GloVe were evaluated using two laymen datasets from the National Library of Medicine (NLM), the Open-Access and Collaborative Consumer Health Vocabulary (OAC CHV) and the MedlinePlus Healthcare Vocabulary. For our first goal, enriching concepts, the results show that GloVe was able to find new laymen terms with an F-score of 48.44%. Our best algorithm enhanced the corpus with synonyms from WordNet, outperformed GloVe with an F-score relative improvement of 25%. For our second goal, expanding the number of concepts with related laymen’s terms, our synonym-enhanced GloVe outperformed GloVe with a relative F-score relative improvement of 63%. The results of the system were in general promising and can be applied not only to enrich and expand laymen vocabularies for medicine but any ontology for a domain, given an appropriate corpus for the domain. Our approach is applicable to narrow domains that may not have the huge training corpora typically used with word embedding approaches. In essence, by incorporating an external source of linguistic information, WordNet, and expanding the training corpus, we are getting more out of our training corpus. Our system can help building an application for patients where they can read their physician\u27s letters more understandably and clearly. Moreover, the output of this system can be used to improve the results of healthcare search engines, entity recognition systems, and many others

WIKINGER – Wiki Next Generation Enhanced Repositories

The regular indexing of text documents is based on the textual representation and does not evaluate the actual document content. In the semantic web approach, human-authored text documents are transformed into machine-readable content data which can be used to create semantic relations among documents. In this paper, we present on-going work in the WIKINGER project which aims to build a web-based system for semantic indexing of text documents by evaluating manual and semi-automatic annotations. A particular feature is the continuous refinement of the automatically generated semantic network by considering community feedback. The feasibility of the approach will be validated in a pilot application

Towards an intelligent and supportive environment for people with physical or cognitive restrictions

AmbienNet environment has been developed with the aim of demonstrating the feasibility of accessible intelligent environments designed to support people with disabilities and older persons living independently. Its main purpose is to examine in depth the advantages and disadvantages of pervasive supporting systems based on the paradigm of Ambient Intelligence for people with sensory, physical or cognitive limitations. Hence diverse supporting technologies and applications have been designed in order to test their accessibility, ease of use and validity. This paper presents the architecture of AmbienNet intelligent environment and an intelligent application to support indoors navigation for smart wheelchairs designed for validation purposes.Ministerio de Educación y Ciencia TIN2006-15617-C[01,02,03