UMLS for information extraction

The enormous growth of the world wide flood of information makes it more and more impor-tant to use effective tools to extract and condense key information. There are ongoing re-searches in the branch of Natural Language Processing (NLP). Information Extraction (IE) is a section of NLP and is used to extract information from text to fill a database. However, there are limitations in the use of IE. The IE systems need to be specialised on a specific domain and therefore they are only able to handle text from an indicated domain. IE systems are con-sisting of several components, one of the important components may be composed of termi-nologies, ontologies, and vocabularies.

The UMLS combines a huge variety of source vocabularies, terminologies, and ontologies to the SPECIALIST lexicon, the Metathesaurus, and the Semantic Network. The UMLS is a gi-gantic knowledge base, which covers numerous themes in medicine. Due the large size of umls, it is difficult to extract information. Also matching concepts to phrases is not an easy task. With the help of MMTx the matching problem can be outsourced. To break down the complex data structure of UMLS and MMTx, a more simple and easy ac-cessible data structure was introduced, which is part of the UMLSint package. The UMLSint package was developed to simplify the access to the UMLS data, to extract the attributes, which are of interest, and to analyse the input data to find the referring concepts in the knowl-edge base. The UMLSint package gets as an input a sentence of medical text and returns at-tributes of interest from the UMLS in accordance to questioned phrase. The information con-sists of factual knowledge from the Metathesaurus and information generated by the MetaMap Transfer (MMTx) tool. The MMTx tool is used to create logical elements and gather informa-tion about the lexical and morphological structure. For each logical element various information is now accessible, such as semantic type, term type, Part-Of-Speech tag, Metathesaurus concept ID, and many more. This information can be used for both NLP and IE systems for further analysis of the text. The subject of this thesis is to enable IE systems, which process medical text, an easier access to the knowledge base named Unified Medical Language System (UMLS).