As recent surveys indicated, more than 80% of patients seek health information on the Internet [3]; more than 70% of physicians regularly search online for medical or professional updates [19]. Approximately 80% of health care data, as well as the ever-growing data online, however, consist of unstructured narratives [14, 18]. Effciently querying and browsing data embedded in these biomedical documents is an important and challenging task. The unstructured nature of these text-based documents brings to light an inherent problem: locked within these documents lies an extraordinary amount of key biomedical knowledge and clinical data, which can hardly be leveraged without intensive manual work. Traditional search engines such as Google can return users the potential documents of interest based on keywords. Users still have to, however, read through the returned documents until the information of interest is located. In addition, search engines usually return hundreds of thousands of links, many of which are not relevant to users’ search.#R##N##R##N#One approach to facilitate browsing and querying biomedical text is to convert the plain text into an annotated web of data, i.e., to convert data originally in free text into structured formats with defined meta-level semantics. Manual annotation may not be realistic due to the large volume of text that needs to be processed. Fully automatic approaches for semantic annotation do not always give satisfying results. Semi-automatic data annotation is, therefore, an attractive alternative. Semi-automatic annotation supports information from biomedical text to be automatically extracted and annotated with manual on refining the annotations.#R##N##R##N#To support semi-automatic annotation, we developed Semantator. Semantator is a user-friendly, semantic-web-oriented environment for annotating data of interest in biomedical documents with respect to domain ontologies. Domain ontologies have been used in information technology to provide semantic definitions of a particular domain, which enable automated agents to perform queries intelligently and infer new knowledge. An ontology includes a set of classes and their relationships (e.g., class hierarchies and predicates). Semantator provides an environment to link data embedded in text to ontology concepts by using semantic annotation. Information of interest from a document can be annotated as an instance of an ontology class to obtain all the semantic definition of that class. In addition, relations between instances can be created using the predicates (properties) defined in the ontology. The annotation results are saved in the Resource Description Framework (RDF) [21] format, which provides a standard way for data sharing and exchange and enables querying and browsing the data using the SPARQL query language [24]. In addition, Semantator also provides an interface where users can compare annotations done by different curators or annotation tools, leverage semantic web technologies for inferences, and detect conflicts in annotations.#R##N##R##N#More specifically, Semantator is implemented as a Protege [2] plug-in, which allows users to view the original documents, the ontology used for an notation, and the annotation results in the same environment. Semantator provides two modes: 1) manual annotation, and 2) semi-automatic annotation. In the manual annotation mode, an expert can choose an annotation schema (a domain ontology), open a document to be annotated, highlight different pieces of information to be annotated, and then mark which ontology concepts the information belongs to. For each highlighted piece of data, the system will generate class instances according to the annotation and display different class instances in different colors. Relationships between instances can also be created using the properties defined in the domain ontology. For the semi-automatic annotation mode, Semantator provides an Application Programming Interface (API), which provides the option to connect the Semantator annotation environment to state-of-the-art or customized information extraction or semantic annotation tools. Human curators can review the automatic annotation results in the Semantator environment and modify them as needed.#R##N##R##N#The Semantator has been released through our web site: http://informatics.mayo.edu/CNTRO/index.php/Download_Semantator. In our previous publication [23], we reported the basic functionalities of Semantator: preliminary implementation of the manual annotation mode; and semi-automatic annotation using the clinical Text Analysis and Knowledge Extraction Systems (cTAKES) [22] and the NCBO annotator [16] (Section 3). This manuscript extends our previous work by introducing two new major functionalities: 1) rule-based extraction capacity (Section 4) and 2) the annotation result comparison function (Section 5). We analyze and illustrate the benefits of using semantic web technologies on the Semantator annotated data (Section 6). We have also conducted a functionality evaluation (Section 7.1) and applied Semantator in a real clinical research application as a case evaluation (Section 7.2). The evaluation results indicate that Semantator can successfully conduct the annotation tasks as designed. We have received much positive feedback and suggestions from the community, based on what we have already improved and will continually improve the functionalities of the tool (Section 8).
['Cui Tao', 'Dezhao Song', 'Deepak Sharma', 'Christopher G. Chute']
Semantator: Semantic Annotator for Converting Biomedical Text to Linked Data