Semantator: annotating clinical narratives with semantic web ontologies.
ABSTRACT To facilitate clinical research, clinical data needs to be stored in a machine processable and understandable way. Manual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems) have been adopted to convert such data into structured formats; however, the quality of such automatically extracted data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document annotation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By representing annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using Semantator.
Semantator: Annotating Clinical Narratives with Semantic Web Ontologies
Dezhao Song1, a,b, Christopher G. Chutea, Cui Taoa
aDivision of Biomedical Statistics and Informatics, Mayo Clinic
200 First Street SW, Rochester, MN 55905
bDepartment of Computer Science and Engineering, Lehigh University
19 Memorial Drive West, Bethlehem, PA 18015
Tofacilitateclinicalresearch, clinicaldataneedsto bestoredinamachineprocessableandunderstandableway. Man-
ual annotating clinical data is time consuming. Automatic approaches (e.g., Natural Language Processing systems)
have been adopted to convert such data into structured formats; however, the quality of such automatically extracted
data may not always be satisfying. In this paper, we propose Semantator, a semi-automatic tool for document an-
notation with Semantic Web ontologies. With a loaded free text document and an ontology, Semantator supports the
creation/deletion of ontology instances for any document fragment, linking/disconnecting instances with the properties
in the ontology, and also enables automatic annotation by connecting to the NCBO annotator and cTAKES. By repre-
senting annotations in Semantic Web standards, Semantator supports reasoning based upon the underlying semantics
of the owl:disjointWith and owl:equivalentClass predicates. We present discussions based on user experiences of using
In recent years, computerized approaches have been widely adopted to conduct clinical research, particularly by using
data that is organized in a machine processable and understandable way. Louit et al.  reviewed the current methods
and problems for genomic medicine data integration. Bodenreide  described how Semantic Web ontologies  are
being used for knowledge management, data integration, etc. in the biomedical field. Tenenbaum et. al.  proposed
the Biomedical Resource Ontology (BRO) for semantic annotation and discovery of biomedical resources.
One prerequisite here is to have decent structured clinical data, i.e., converting originally free text based data into
certain structured formats. Automatic approaches that are based upon Natural Language Processing (NLP) techniques
have been well studied . According to the reported performance of the clinical Text Analysis and Knowledge
Extraction Systems (cTAKES) , one of the state-of-the-art NLP systems in the medical domain, it has been able to
achieve a F-score of 82.4% for Named Entity Recognition (NER)  which is a fundamental task for transforming free
text to structured data. However, clinical research usually requires more precise results. Fully automatic approaches
for data extraction are preferred but they do not always give satisfying results; while it may not be realistic to only
rely on manual annotation to construct structured data due to the large volume of clinical notes that are needed to
process. Therefore, semi-automatic data extraction could be one choice where we automatically extract information
from clinical narratives and then manual efforts are used to refine such automatic annotations. The results from this
semi-automatic process could potentially serve as the training sets to help automatic systems to further improve their
Another important problem is that what structured format we should use to store the data. In clinical research, a vari-
ety of data formats have been adopted, such as Comma-separated Values (CSV) , relational databases, eXtensible
Markup Language (XML), etc. Jeronimo et al.  constructed a multimedia database tool containing cervical cancer
related patient records. The data is stored in a relational database and can be exported as CSV files. The HL7 Clinical
Document Architecture (CDA)  is an XML based markup standard intended to specify the encoding, structure and
semantics of clinical documents for exchange. The advantage of these data formats is that they provide a structured
way for data storage and thus provide the possibility for more convenient data management compared to free text based
data representation. Compared to the existing formats, the Semantic Web  and its corresponding knowledge rep-
1This work was done while the first author was an intern at Mayo Clinic.
resentation standards (e.g., the Resource Description Framework (RDF)2and the Web Ontology Language (OWL)3)
adds an expressive framework for semantics-enabled data representation and knowledge inference.
In the Semantic Web, an ontology is an explicit and formal specification of a conceptualization, formally describing
a domain of discourse. It consists of a set of terms (classes) and their relationships (class hierarchies and predicates).
RDF is a graph based data model for describing resources and their relationships. Two resources are connected via one
or more predicates in the form of triple. A triple, < s,p,o >, consists of three parts: subject, predicate and object. The
subject is an identifier (e.g., a URI) and the object can either be an identifier or a literal value, such as strings, numbers,
dates, etc. One advantage of the Semantic Web is that it supports knowledge inferencing. For instance, two classes
A and B are defined to be disjoint in an ontology and if an automatic system or a human annotator annotates a piece
of free text with both classes, the system should be able to report a potential annotation error. For automatic systems,
such formal representation of semantics could be helpful to improve their performance under specific scenarios .
In this paper, we propose Semantator4, a semi-automatic annotation system for annotating clinical narratives using se-
mantic web ontologies. Here, semantic annotation is to annotate entities with ontology classes, such as instances and
their relationships creation/deletion. Although Semantator is designed for annotating clinical documents, it can also be
applied to documents in other domains. Semantator is developed as a plugin of Prot´ eg´ e5, a well known tool for build-
ing and interacting with Semantic Web ontologies. The current version of Semantator provides: 1) the basic manual
annotation functionalities, including ontology instance creation/deletion, relationship creation/deletion, linking equiv-
alent instances and exporting/reloading annotation; 2) automatic annotation by connecting to the NCBO annotator
 and cTAKES ; 3) Semantic Web based reasoning by utilizing the underlying semantics of the owl:disjointWith
and owl:equivalentClass predicates.
The rest of the paper is organized as following. Section 2 discusses related work. Section 3 and Section 4 introduce
the manual and automatic annotation functionalities respectively. We present some discussions on Semantator based
on our user studies in Section 5 and conclude in Section 6.
Existing annotation systems can be generally categorized into pattern based and machine learning based systems.
Pattern based systems, such as PANKOW  and Armadillo , find entities by discovering patterns that are either
manually identified or semi-automatically induced with a set of initial manually tagged seed patterns. SemTag 
and KIM  adopt a set of pre-defined rules to locate the information of interest. Differently, S-CREAM , MnM
, GATE  and cTAKES  explore machine learning algorithms and natural language processing techniques
to identify entities. BioNLP UIMA6provides a framework for users to plugin tools for different components for
document annotation. Although machine learning based systems do not need to have human identified rules, they
require certain amount of training data that may not always be available for each domain such systems would be
As the emergence of Semantic Web, annotation systems based on Semantic Web techniques have been proposed.
Semantic-document  and GoNTogle  support semantic annotation on documents with ontology classes. In
addition to annotation, GoNTogle also supports searching within document annotation results. Compared to these two
systems, our proposed Semantator further supports instance relationship creation and has basic reasoning capabilities.
Knowtator  is a plugin for Prot´ eg´ e. It facilitates manual creation of annotation to user’s text. However, the biggest
drawback of Knowtator is that it only works with Prot´ eg´ e-Frames7and can only export its annotations in XML;
while our system supports annotation with OWL ontologies, stores annotations in RDF, and therefore can leverage
the semantic web reasoners directly. Textpresso  is a text mining tool to extract terms from academic articles of a
given domain for ontology population and this populated ontology can then be used to enable searching.
3Manual Annotation with Semantator
In this section, we introduce the manual annotation functionalities of Semantator, including instance creation/deletion,
relationship creation/deletion, linking equivalent instances and exporting/reloading existing annotations. Accompany-
ing with our descriptions, we will also illustrate these functionalities with figures to make them more clear. A user
first can load a plain text document that contains the clinical notes to annotate by simply clicking File and then Open
in the menu to choose the file. The contents of the chosen document will be displayed in Semantator.
In Semantator, we provide two ways for a user to create instances: One-at-a-Time and Batch Creation. Figures 1(a)
and 1(b) demonstrate the two alternatives respectively.
(a) Single Creation (b) Batch Creation
Figure 1: Instance Creation
One-at-a-Time. For the first option, a user can create one instance each time. To create an instance, a user will
need to highlight a piece of text in the loaded document and select a class from the loaded ontology in Prot´ eg´ e.
The system allows the user to pick a color to be used for highlighting all instances of the selected class when this
class is first used for annotation. In Figure 1(a), we are creating an instance of the Event class with the document
fragment See the patient back; and Figure 1(b) shows that it is highlighted in green. This instance is added to the
ontology with the triple: < event1,rdf : type,Event > and is also associated with an rdfs:label8by adding the
triple: < event1,rdfs : label,See the patient back >. Note that by default, Semantator stores the highlighted text
using rdfs:label; but the user can choose to use any properties as needed.
Batch Creation. There may exist many instances that could actually be categorized of the same class in a document
and creating each instance individually could be time-consuming. Therefore, we provide the second option to create
instances, Batch Creation. A user can add different pieces of document fragments that describe instances for the same
class into a list. When finishing selecting all document fragments to annotate, the user can then choose an ontology
class and annotate all document fragments in the list to be instances of this chosen class. Note that here we assume all
selected document fragments represent instances of the same class. Document fragments in the candidate list can be
easily removed by right clicking on it and choosing to remove.
Semantator allows users to delete any previously created instances if desired. The same document fragment could
have been annotated with ontology instances of different classes. For instance, the text See the patient back could be
annotated to be an Event and an owl:Thing (the most general class in any ontology). When a user chooses to delete the
instance(s) of a document fragment, the system detects all instances for which this fragment has been created. Then,
the user can choose to delete any of these instances individually. Figure 2 demonstrates this deletion operation.
Figure 2: Instance Deletion
Generating Instance Equivalences
In a clinical document, it is possible that instances that occur at different places in the document could actually refer
to the same real world entity9. For instance, in the following document, two instances (in bold) of the Event class have
been created, and they actually represent the same event in real world.
The second cycle of chemotherapy was on June 10, 2004. Patient’s bilirubin is elevated 2 weeks after the second
cycle of chemotherapy.
medical care related applications . Therefore, in Semantator, we provide the functionality to generate equivalences
between two or more instances. In a similar fashion to instance batch creation, users will select an arbitrary number
of annotated instances and add them to a sameAs candidate list to make them pairwisely equivalent. Figures 3(a) and
3(b) demonstrate this process. Although they only show generating equivalences between two instances, an arbitrary
number of instances can be added to the sameAs candidate list to generate the linkages. Users can remove any instance
from the candidate list before making them all equivalent.
Managing Instance Relationships
Another equally important type of annotation is instance relationships annotation. As described in Section 1, each
ontology instance is a resource in the Semantic Web and different instances can be connected with one or more
properties from ontologies. For example, we have two events Event1 and Event2; and they can be connected to form
the following triple: < Event1,before,Event2 >. Semantator allows users to create a single relationship between
9This is generally referred to as entity coreference , which is out of the scope of this paper.
(a) Add Instance to owl:sameAs Candidate List (b) Generate Instance Equivalences
Figure 3: Generating Instance Equivalences
two instances at a time. Users can select two instances to be related and add them to the relationship candidate list
that holds a maximum of two instances (in the Semantic Web, all relationships are binary). Then, they can choose any
object property from the loaded ontology to connect the selected candidates. Finally, users need to decide the subject
of this new relationship. Choosing an incorrect subject will sometimes totally change the underlying semantics of a
relationship. In the triple example given above, Event1 is the subject and Event2 is the object. If we reverse their
ordering, the triple will mean that Event2 happened before Event1, which then deviates from the fact. Figures 4(a) and
4(b) give an overview of this relationship creation process. A relationship between two instances can be easily deleted
following a similar procedure as deleting an instance. Note that the instances need to be created first before they can
Exporting and Reloading Document Annotations
Till now, we have presented how to perform manual annotations. In Semantator, users can export their annotations to
an RDF file together with a XML file that contains annotation related metadata, such as the position of each annotated
instance, the color used to highlight instances of each class, etc. Next time when a user opens the same document,
Semantator will ask the users whether or not they would like to load their previous annotations on this document.
Once the correct RDF and XML files are selected, the created instances, relationships and their relevant metadata will
be reloaded to Semantator for further manipulations.
As discussed earlier, a clinical document may be long and there exists a large volume of such documents. Therefore,
manualannotatingsuchdocumentscouldbetimeconsuming. Tofacilitatetheentireannotationprocess, inthissection,
we introduce the semi-automatic annotation capability of Semantator by connecting to back end services, including
the NCBO annotator  and cTAKES .
NCBO Annotator and cTAKES Supported Automatic Annotation
BioPortal  is a Semantic Web based platform designed for the biomedical domain. It allows users to search
for specific ontologies that match certain user provided keywords. It provides an online annotation tool, the NCBO
annotator, that takes user inputs (free text), chooses relevant ontologies for annotation, recognizes relevant biomedical
(a) Choose Instances to Relate(b) Choose Subject
Figure 4: Connecting Instances with Ontological Properties
ontology terms in the text, and finally returns such annotations to the users. The NCBO annotator can also be used
as a web service, which enables us to utilize it from within Semantator. The NCBO annotator can be called from
Semantator with simply one click. Before actually starting doing any automatic annotation, we provide a list of
ontologies (by querying BioPortal) that are currently supported by BioPortal and a user can choose an arbitrary number
of ontologies from this list against which the annotator will match the words and phrases in a loaded document. All
automatically annotated entities are treated as potential ontology instances and are highlighted in Semantator. Users
can then examine such results and only retain those correctly identified instances from their perspective.
cTAKES is another tool used in Semantator that supports automatic annotation in a similar way to that supported by
NCBO annotator. Different from the NCBO annotator, cTAKES 1) is designed for clinical domain; 2) adopts NLP
techniques and supports negation and time constraints. Currently, cTAKES performs annotation with the SNOMED
CT and RxNorm  dictionaries but users can add their own dictionaries as needed. Although we currently use
cTAKES as a packaged Java library, a web service for cTAKES is under development.
With such automatic processes, a document can be firstly annotated with the available domain knowledge provided by
the chosen ontologies in BioPortal and dictionaries of cTAKES respectively to recognize candidate instances. Next,
such automatic results can be corrected and augmented by human annotators. Users could potentially benefit from
employing such automatic annotation services assuming decent performances can be achieved. We hope that using
such automatic tools could facilitate the entire process by reducing the time needed for document annotation. In the
current version of Semantator, we support automatic annotation by using BioPortal and cTAKES because they are
well-acknowledged tools in the biomedical domain; however, the key idea here is that users can plug in any annotation
tool that is suitable for a particular domain. In future work, we would like to provide APIs for our end users to support
this. As shown in Figure 5(a), a user has selected the NCBO annotator and is choosing the ontologies to use; in Figure
5(b), we see that the automatically annotated instances are highlighted when human annotators can then decide which
ones to retain.
Semantic Web based Reasoning
One advantage of putting annotation results in Semantic Web formats is that users can benefit from the reasoning
capabilities provided by Semantic Web techniques. In this section, we discuss the two types of reasoning that are
currently supported in Semantator based upon class disjointness and class equivalence.
(a) Choose Annotation Ontologies(b) Retain Correctly Identified Instances
Figure 5: Semi-automatic Annotation with BioPortal Web Services
In the Semantic Web, classes can be defined to be disjoint with each other10, i.e., Ciand Cjare two classes and
Ci⊑ ¬Cj. Disjoint classes have no instances in common. For example, two classes Male and Female are disjoint and
one person instance can only be declared to belong to either of these two classes . Classes can also be defined to
be equivalent11, i.e., Ci≡ Cj. For example, two classes Man (Ci) and the intersection of Human and Male (Cj) are
equivalent and thus any instance that is declared to be a Man should also be an instance of the other class.
In Semantator, we support reasoning based upon the two types of class relationship discussed above. Using the
automatic annotation services, the same document fragment might be annotated to be candidate instances of disjoint
classes. Take the following sentence as an example:
I was pleased to inform Mr. Smith that his PSA today is undetectable.
In this example, by calling the NCBO annotator with the SNOMED CT ontology, the word today is annotated to be an
Organic Chemical; however, a human annotator may simply annotate it to be an instance of the TimeInstant class from
the CNTRO ontology . Assuming we have the knowledge about the disjointness between the two classes: Organic
Chemical and TimeInstant, Semantator will report an inconsistency. Similarly, if we annotate this sentence with both
NCI Thesaurus and the International Classification Nursing Practice (ICNP) ontologies, BioPortal will annotate the
word today to be an instance of the Antibiotic class in both ontologies. If we assume that the two Antibiotic classes
from the two ontologies are equivalent and a user only annotated today to be an instance of one of them, Semantator
will then suggest the user to also annotate it to be an instance of the other. For future work, we will explore how to
provide a more general reasoning capable framework within Semantator.
In this section, we present discussions based upon some user experience on Semantator. Two of our annotators are
experts on ontologies. Semantator was also evaluated by one student intern at Mayo Clinic without any background
in the areas of medical informatics and the Semantic Web, and adopted by an Engineer at Boston Scientific who had
some experience with ontologies  and Knowtator , a manual annotation tool for Prot´ eg´ e-Frames.
Compared to Knowtator, the biggest advantage of Semantator is its semi-automatic annotation capability. Semantator
and Knowtator share the same objective to annotate clinical narratives to create ontology instances and their rela-
tionships with properties in an ontology. A human annotator can generally finish annotating a document faster using
Semantator because the automatic annotation capability at least helps the users to focus their attention on specific
parts rather than the entire document. Second, it was mentioned by one of our annotators that the Semantator relation-
ships are easier to follow as the system simply asks the user to identify the two instances, choose an object property,
and specify the subject. Another notable aspect of Semantator is that it works with OWL and thus can leverage the
state-of-the-art Semantic Web based reasoning techniques for inconsistency checking and automatic classification.
One drawback of Semantator is the number of files needed. Semantator requires 3 files: the text file, the annotated
OWL file, and a metadata XML file storing the information (e.g., positions and colors) of the annotations for users
to reload and visualize their previous annotations. There is therefore more file organization required, and it required
more practice to understand how to save and re-open files. Another problem mentioned by our annotators was that if an
ontology class is first used for annotating a document, Semantator will ask the user to choose a color for highlighting
its instances throughout this document. However, it would be good if the system could reuse the chosen colors for the
same classes across different documents. This might be feasible by establishing a user repository. Whenever a user
wants to use Semantator, the user could choose to log in so that all history information can be loaded; thus all the
choices about colors made by this user before can automatically apply.
6Conclusion and Future Work
In this paper, we introduce Semantator, a Semantic Web based semi-automatic annotation tool for annotating clinical
documents. Although it is designed for the clinical domain, it can also be applied to annotate documents of other
domains. Developed as a Prot´ eg´ e plugin, users can manually annotate documents with classes and properties from
a loaded ontology in Prot´ eg´ e environment. To facilitate the annotation process, automatic annotation is supported
by connecting to two back end services: NCBO annotator and cTAKES. Furthermore, the reasoning capability of
Semantator could assist users in finding inconsistencies and incompleteness in their annotations. We present some
discussion on Semantator based upon some user experiences.
For future work, it would be necessary to perform a comprehensive evaluation on the usability of Semantator and also
provide some use cases. Next, it would be useful to provide a DIFF module to visualize the differences and calculate
the inter-annotator agreement between annotations of different annotators. Furthermore, we would like to enhance
Semantator with some query capability so that users can issue queries (e.g., SPARQL) to search within the annotation
results. Also, we will explore how to provide a general framework to support reasoning within Semantator. In addition
to automatic instance creation, automatic relation extraction could be one interesting research question to explore in
This research is partially supported by the National Center for Biomedical Ontologies (NCBO) under the NIH Grant
#N01-HG04028, and the NSF under Grant #0937060 to the CRA for the CIFellows Project. We would like to thank
KimClark andIan Chute forhelping totest Semantator, andVinodKaggalandDr. HongfangLiu forhelping settingup
the cTAKES environment. We also thank Deepak Sharma and Donna Ihrke for their advice on improving Semantator.
1. Brenton Louie, Peter Mork, Fernando Mart´ ın-S´ anchez, Alon Y. Halevy, and Peter Tarczy-Hornoch. Data integra-
tion and genomic medicine. Journal of Biomedical Informatics, 40(1):5–16, 2007.
2. O Bodenreider. Biomedical ontologies in action: role in knowledge management, data integration and decision
support. Yearb Med Inform, pages 67–79, 2008.
3. Tim Berners-Lee, J Hendler, and O Lassila. The semantic web. Scientific American, 284(5):34–43, 2001.
4. Jessica D. Tenenbaum, Patricia L. Whetzel, Kent Anderson, Charles D. Borromeo, Ivo D. Dinov, Davera Gabriel,
Beth A. Kirschner, Barbara Mirel, Timothy D. Morris, Natasha Fridman Noy, Csongor Nyulas, David Rubenson,
Paul R. Saxman, Harpreet Singh, Nancy Whelan, Zach Wright, Brian D. Athey, Michael J. Becich, Geoffrey S.
Ginsburg, Mark A. Musen, and et al. The biomedical resource ontology (bro) to enable resource discovery in
clinical and translational research. Journal of Biomedical Informatics, 44(1):137–145, 2011.
5. Dina Demner-Fushman, Wendy Webber Chapman, and Clement J. McDonald. What can natural language pro-
cessing do for clinical decision support? Journal of Biomedical Informatics, 42(5):760–772, 2009.
6. Guergana K Savova, James J Masanz, Philip V Ogren, Jiaping Zheng, Sunghwan Sohn, Karin C Kipper-Schuler,
and Christopher G Chute. Mayo clinical text analysis and knowledge extraction system (ctakes): architecture,
component evaluation and applications. Journal of the American Medical Informatics Association, 17(5):507–
7. Nadeau David and Sekine Satoshi. A survey of named entity recognition and classification. Linguisticae Investi-
gationes, 30(1):3–26, January 2007.
8. Y. Shafranovich. RFC 4180: Common format and MIME type for Comma-Separated values (CSV) files, 2005.
9. Jose Jeronimo, L Rodney Long, Leif Neve, Bopf Michael, Sameer Antani, and Mark Schiffman. Digital tools for
collecting data from cervigrams for research and training in colposcopy. Journal of Lower Genital Tract Disease,
10. R. H. Dolin, L. Alschuler, C. Beebe, P. V. Biron, S. L. Boyer, D. Essin, E. Kimber, T. Lincoln, and J. E. Mattison.
The HL7 clinical document architecture. J Am Med Inform Assoc, 8(6):552–569, 2001.
11. Ulrich Sch¨ afer. OntoNERdIE - Mapping and linking ontologies to named entity recognition and information
extraction resources. In Proceedings of the 5th International Conference on Language Resources and Evaluation
(LREC), pages 1756–1761, 2006.
12. Natalya Fridman Noy, Nigam H. Shah, Patricia L. Whetzel, Benjamin Dai, Michael Dorf, Nicholas Griffith,
Clement Jonquet, Daniel L. Rubin, Margaret-Anne D. Storey, Christopher G. Chute, and Mark A. Musen. Bio-
portal: ontologies and integrated data resources at the click of a mouse. Nucleic Acids Research, 37(Web-Server-
13. Philipp Cimiano, Siegfried Handschuh, and Steffen Staab. Towards the self-annotating web. In Proceedings of
the 13th international conference on World Wide Web (WWW), pages 462–471, 2004.
14. Sam Chapman, Alexiei Dingli, and Fabio Ciravegna. Armadillo: harvesting information for the semantic web. In
Proceedings of the 27th Annual International ACM SIGIR Conference on Research and Development in Informa-
tion Retrieval, page 598, 2004.
15. Stephen Dill, Nadav Eiron, David Gibson, Daniel Gruhl, Ramanathan V. Guha, Anant Jhingran, Tapas Kanungo,
Sridhar Rajagopalan, Andrew Tomkins, John A. Tomlin, and Jason Y. Zien. Semtag and seeker: bootstrapping the
semantic web via automated semantic annotation. In Proceedings of the 12th international conference on World
Wide Web (WWW), pages 178–186, 2003.
16. Borislav Popov, Atanas Kiryakov, Damyan Ognyanoff, Dimitar Manov, and Angel Kirilov. Kim a semantic
platform for information extraction and retrieval. Nat. Lang. Eng., 10:375–392, September 2004.
17. Siegfried Handschuh and Steffen Staab. Authoring and annotation of web pages in cream. In Proceedings of the
11th international conference on World Wide Web (WWW), pages 462–473, 2002.
18. Maria Vargas-Vera, Enrico Motta, John Domingue, Mattia Lanzoni, Arthur Stutt, and Fabio Ciravegna. Mnm:
Ontology driven semi-automatic and automatic support for semantic markup. In 13th International Conference
on Knowledge Engineering and Knowledge Management (EKAW), pages 379–391, 2002.
19. Hamish Cunningham, Diana Maynard, Kalina Bontcheva, and Valentin Tablan. Gate: A framework and graphical
development environment for robust nlp tools and applications. In Proceedings of the 40th Anniversary Meeting
of the Association for Computational Linguistics (ACL), 2002.
20. Henrik Eriksson. An annotation tool for semantic documents. In The Semantic Web: Research and Applications,
4th European Semantic Web Conference (ESWC), pages 759–768, 2007.
21. Giorgos Giannopoulos, Nikos Bikakis, Theodore Dalamagas, and Timos K. Sellis. Gontogle: A tool for semantic
annotation and search. In The Semantic Web: Research and Applications, 7th Extended Semantic Web Conference
(ESWC), pages 376–380, 2010.
22. PhilipV.Ogren. Knowtator: Aprot´ eg´ eplug-inforannotatedcorpusconstruction. InHumanLanguageTechnology
Conference of the North American Chapter of the Association of Computational Linguistics (HLT-NAACL), 2006.
23. H. M. M¨ uller, E. E. Kenny, and P. W. Sternberg. Textpresso: an ontology-based information retrieval and extrac-
tion system for biological literature. PLoS Biology, 2:e309+, 2004.
24. Amit Bagga and Breck Baldwin. Entity-based cross-document coreferencing using the vector space model. In
COLING-ACL, pages 79–85, 1998.
25. Cui Tao, Harold R. Solbrig, Deepak K. Sharma, Wei-Qi Wei, Guergana K. Savova, and Christopher G. Chute.
Time-oriented question answering from clinical narratives using semantic-web techniques. In 9th International
Semantic Web Conference (ISWC), pages 241–256, 2010.
26. Stuart J. Nelson, Kelly Zeng, John Kilbourne, Tammy Powell, and Robin Moore. Normalized names for clinical
drugs: Rxnorm at 6 years. JAMIA, 18(4):441–448, 2011.
27. Benjamin N. Grosof, Ian Horrocks, Raphael Volz, and Stefan Decker. Description logic programs: combining
logic programs with description logic. In Proceedings of the Twelfth International World Wide Web Conference
(WWW), pages 48–57, 2003.
28. Cui Tao, Wei-Qi Wei, Harold R Solbrig, Guergana Savova, and Christopher G Chute. CNTRO: A semantic web
ontology for temporal relation inferencing in clinical narratives. In American Medical Informatics Assocication
Annual Symposium (AMIA), pages 787–91, 2010.
29. Clark KK, Sharma DK, Chute CG, and Tao C. Application of a temporal reasoning framework tool in analysis of
medical device. In American Medical Informatics Assocication Annual Symposium (AMIA), Accpeted, 2011.