# An Integrated Approach to Biomedical Term Identification Systems
## 1 Introduction  
In the biomedical domain we can find an impressive number of information sources. Traditionally, this information was difficult to access for different reasons, such as that the documents were not publicly available or the content was so complex that only medical specialists could understand the terminology used in the documents. However, with advent of the Internet and open access to all types of data, every day more and more ordinary people like for example students, researchers, patients and relatives try to access this information, as well as medical professionals. Systems for combining, fusing and retrieving medical information from heterogeneous sources are a great help, not only for professionals who must continuously handle a large number of reports, articles, documents and data but also for ordinary people who want to be empowered with easy and fast access to all kinds of information, including medical information. In this paper we describe an approach to interactive information retrieval. This approach combines and integrates different semantic resources such as: UMLS (Unified Medical Language System), Google Scholar, SciELO and MedLine. Our main goal is to provide a tool for a better understanding of specialized medical terminology. In addition, our approach implements a semantic search focusedon improving the accuracy of the user's query by providing support to understand the contextual meaning of the terms. On the one hand, tools for understanding specialized terminology help the normal user to have a better readability of the texts and, on the other hand, help experts to have a more exhaustive analysis using tools that provide additional references on the terms detected. Medical terminology detection systems have always attracted great interest [1] given the high number of related resources and their good quality, such as the specialized ontologies UMLS [2] and MeSH [3]. The identification of terms is not only interesting in specialized texts, it can also be very useful over texts written by patients [4]. In both cases, systems for identifying terms become the key to accessing bibliographic documentation and related literature, since these terms and their relationships, when identified, allow the connection of knowledge between different sources. The proposed system is flexible enough so that both medical professionals and non-specialized users (for example, patients) can take advantage of it. Our approach takes a textual document as input and different techniques of Natural Language Processing (NLP) are applied in order to recognize the biomedical entities in the text. It should be noted that our system works on texts written in Spanish, which adds an additional difficulty to the task of term identification. Most of the tools developed to date are oriented toward textual information in English. In fact, there are some good examples of resources that have acceptable accuracy in Named Entity Recognition (NER) in English such as the cTakes tool [5]. Therefore, our system implements its own NER for recognizing biomedical entities in Spanish including the UMLS codes associated to each recognized term. Then, it connects entities found in the document to a Linked Open Data (LOD) resource, called Linked Life Data (Linked Life Data--A Semantic Data Integration Platform for the Biomedical Domain: [[http://linkedlifedata.com/](http://linkedlifedata.com/)]([http://linkedlifedata.com/](http://linkedlifedata.com/))), by using the first UMLS code. Once we have the text marked with all the biomedical entities, the system integrates three different knowledge bases and external sources (MedLine, SciELO and Google Scholar) to perform information retrieval using the entities as query keywords. Finally, the system generates a semantic network relating all the identified entities by taking as central node the most recurrent entity in the original text.