Constructing Medical Image Domain Ontology with Anatomical Knowledge Extracting information from the medical imaging reports with domain ontology has attracted much attention in medical natural language processing field. Based on the unstructured characteristics of medical image report text, the existing image report domain ontology is constructed by ontology learning method, including ontology learning method based on language and ontology learning method based on machine learning. However, these existing methods ignore the anatomical knowledge embedded in medical imaging reports, which is very useful for extracting entity relationships from reports. In order to solve the above problems, this paper proposes a domain ontology construction method for medical image reporting based on anatomy knowledge, which combines the prior knowledge of pathology and anatomy to obtain the basic framework of the domain ontology as the knowledge driver. In particular, our proposed approach consists of two tasks. The first task is to convert each text report into a semantic tree through an anatomic knowledge based semantic subtree generation algorithm. Semantic subtree generation algorithm mainly consists of three parts: framework positioning, relation extraction and adding relation. Firstly, the text is located based on anatomical knowledge, and the relational extraction mainly adopts the method of dependency syntactic analysis to extract the three semantic relations contained in the text. Finally, add the relationship to the corresponding branch. The second task is to obtain the domain ontology by merging semantic subtree. This step is mainly to obtain the domain ontology by merging the nodes of the XML structure semantic tree. The experimental results show that this method can be used for relational extraction and domain ontology construction, laying a good foundation for the followup research.

NLP for the Generation of Training Data Sets for OntologyGuided WeaklySupervised Machine Learning in Digital Pathology The combination of ontologies with machine learning (ML) approaches is a hot topic and not yet extensively investigated but having great future potential. This is due to the general fact that both, ontologies and ML, constitute two indispensable technologies for domainspecific knowledge extraction, actively used in knowledgebased systems. Whilst the primary goal of both these approaches are the same, knowledge discovery, little is yet known about how the two sources of knowledge can be successfully integrated. The main data source in digital pathology are whole slide images. For the effective generation of sufficiently large and highquality training data we need to extract in addition information from medical reports, containing nonstandardized text. Since full annotation on pixel level would be impracticably expensive, a practical solution is in weaklysupervised ML. In the project described in this paper we used ontologyguided natural language processing (NLP) for term extraction and a decision tree built with an expertcurated classification system. This demonstrates the practical value of our solution to analyze and structure training data sets for ML and as a tool for the generation of biobank catalogues.