An Ontology-Driven Search Module for Accessing Chronic Pathology Literature.
ABSTRACT This paper presents an advanced search module for bibliography retrieval developed within the CHRONIOUS European IP project. The developed search module is specifically targeted to clinicians and healthcare practitioners searching for documents related to Chronic Obstructive Pulmonary Disease (COPD) and Chronic Kidney Disease (CKD). To this aim, the presented tool exploits two pathology-specific ontologies that allow focused document indexing and retrieval. Besides the search module, an enrichment tool is provided to maintain and to keep up-to date such as ontologies. In addition link with the terms of the MeSH (Medical Subject Heading) thesaurus has been provided to guarantee the coverage with the general certified medical terms and multilingual capabilities.
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ABSTRACT: Ontologies have proven to be useful in the area of Information Retrieval and the biomedical informatics community has acknowledged, in recent years, their utility. However, building and updating manually ontologies is a long and tedious task. This paper proposes a system that allows any search engine to develop its semantic layer by applying ontology learning techniques on Web snippets and applies it to a well-known medical digital library, PubMed. The new system (SemPubMed) automatically builds new ontology fragments related to the user’s query and then it reformulates queries using the new concepts in order to improve information retrieval. Our system has endured a twofold evaluations. On the one hand, we have evaluated the quality of the modular ontologies built by the system. On the other hand, we have studied how the semantic reformulation of the queries has led to an improvement of the quality of the results given by PubMed, both in terms of precision and recall. Obtained results show that adding semantic layer to PubMed enables an improvement of query reformulation and predicted ranking score.Multimedia Tools and Applications 10/2014; · 1.06 Impact Factor
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ABSTRACT: CHRONIOUS is an Open, Ubiquitous and Adaptive Chronic Disease Management Platform for Chronic Obstructive Pulmonary Disease(COPD) Chronic Kidney Disease (CKD) and Renal Insufﬁciency. It consists of several modules: an ontology based literature search engine, a rule based decision support system, remote sensors interacting with lifestyle interfaces (PDA, monitor touch screen) and a machine learning module. All these modules interact each other to allow the monitoring of two types of chronic diseases and to help clinician in taking decision for cure purpose. This paper illustrates how some machine learning algorithms and a rule based decision support system are used in the CHRONIOUS project, to monitor chronic patient. We will analyse how a set of machine learning algorithms can be used in smart devices to alert the clinician in case of a patient health condition worsening trendInternational Journal of Advance in Life sciences. 07/2012; 4(1&2):11-20.
An ontology-driven search module for accessing chronic
Stephan Kiefer1, Jochen Rauch1, Riccardo Albertoni2, Marco Attene2, Franca
Giannini2, Simone Marini2, Luc Schneider3, Carlos Mesquita4, Xin Xing5
1Home Care / Telemedicine- Fraunhofer Institute for Biomedical Engineering, St.Ingbert
2Institute for Applied Mathematics and Information Technology – C.N.R.
3Institute for Formal Ontology and Medical Information Science, Saarland University, P.O.
Box151150, Saarbrücken, 66041, Germany
4Link Consulting, Lisbon, 1000-138, Portugal,
5TZI - Center for Computing and Communication Technologies, Bremen, 28359, Germany
Abstract. This paper presents an advanced search module for bibliography retrieval developed
within the CHRONIOUS European IP project. The developed search module is specifically
targeted to clinicians and healthcare practitioners searching for documents related to Chronic
Obstructive Pulmonary Disease (COPD) and Chronic Kidney Disease (CKD). To this aim, the
presented tool exploits two pathology-specific ontologies that allow focused document
indexing and retrieval. Besides the search module, an enrichment tool is provided to maintain
and to keep up-to date such as ontologies. In addition link with the terms of the MeSH (Medical
Subject Heading) thesaurus has been provided to guarantee the coverage with the general
certified medical terms and multilingual capabilities.
Keywords: Ontology driven search, Health care literature, Chronic diseases.
Chronic diseases are mostly characterized by complex causality, multiple risk fac-
tors, long latency periods, a prolonged course of illness and functional impairment or
disability. Most chronic diseases do not resolve spontaneously, and are generally not
cured completely. Chronic diseases may get worse, lead to death, be cured, remain
dormant or require continual monitoring. It is then clear that reducing the severity of
both the symptoms and the impact would mean significant benefit for both the indi-
vidual and the society. This is possible in many conditions. Many physicians are very
optimistic about the benefits that a proper disease management can bring to provide a
full and active life and recognize that the long-term health outlook for chronic disease
has improved in the past decade, and most of them credit the improvement to better
management and monitoring.
To support the management of persons at risk or with chronic health conditions,
the project CHRONIOUS (EU Contract N. FP7-ICT-2007–1–216461) is developing
an open framework for their remote monitoring and treatment. Chronic obstructive
pulmonary disease (COPD) and Chronic kidney disease (CKD) are two case studies
to set up a test bed in the project, but the CHRONIOUS architecture is suitable for
any chronic disease.
Within this project an intelligent literature Search Module has been developed. The
CHRONIOUS Search Module enables healthcare professionals to access well focused
and up to date healthcare information in the form of scientific literature, guidelines,
hospital-specific documentation and grey literature.
The CHRONIOUS search mechanism overcome Google Scholar, PubMed and
GoPubMed by combining the MeSH potentiality with domain ontologies as topic-
neutral representations of the domain of entities (objects, processes, qualities, disposi-
tions, functions, etc.) related to the COPD and CKD on ontologies. Indeed, while
MeSH encodes terminological knowledge, the ontologies encode expert domain
knowledge. In this respect, MeSH and domain ontologies do not compete, but fulfill
complementary tasks and the domain ontologies provide additional structure and
depth as far as knowledge items are concerned; this additional representational power
can be harnessed to improve annotation of and hence search over medical literature.
The Search Module combines two domain ontologies (one for each chronic disease
treated) built on top of the Middle Layer Ontology for Clinical Care (MLOCC) 
with the linguistic capabilities provided by the Medical Subject Heading thesaurus
The use of the ontology for Clinical Care (MLOCC) guarantees the exploitation of
well-funded and formalized medical concepts and the integration with other medical
domain ontologies (e.g., ACGT Master Ontology , Open Biological and Biomedi-
cal Ontologies (OBO) foundry [4, 5]). Based on medical guidelines, the two domain-
specific ontologies have been defined and properly validated by clinical experts coor-
dinated by an international medical board. Within CHRONIOUS, ontologies and
MeSH provide complementary benefits: for each specific disease, indeed, an ontology
provides a fine-grained knowledge that MeSH is not supposed to reach; on the other
hand, MeSH provides well-established generic medical terminology, multilingual
representations, synonyms, narrower/broader/related concepts which can be exploited
by the clinicians for the composition of expressive queries in a simple and effective
way. The combination of the domain specific ontologies and the MeSH thesaurus
allows CHRONIOUS to provide more focused results when compared to traditional
search engines for the medical literature. Besides the two domain-specific ontologies,
the literature Search Module consists of components for processing and indexing the
scientific literature, as well as the components that allow the user to formulate appro-
priate queries. Moreover, to deal with the problem of medical knowledge evolution,
the developed system provides tools for supporting the experts in upgrading the pro-
vided ontologies to new emerging concepts in the concerned documentation. This is
even more important when considering remote patient monitoring, with wearable
sensors, as in CHRONIOUS, since the availability of new signals and information is
expected to introduce new practices and new insight for patient care which turns in
more evolving concepts.
The architecture of the CHRONIOUS Search Module, depicted in Fig. 1, is based
on different sub-modules including:
Upload Tools to load documents related to CKD and COPD in the CHONIOUS
System for their indexing. It provides two different capabilities: one supporting the
clinicians to upload their own documents, the second offering capabilities to auto-
matically treat resources directly coming from publishers upon specific agreements;
Transformation Module converts the imported documents, which might be in dif-
ferent formats, to normalized plain text format;
Natural Language Processing (NLP) Tool is based on the GATE Framework ,
allows to specify processing pipelines (e.g. consisting of Sentence Splitter, Tokeniser,
Part-of-speech Tagger and Morphological Analyser) as well as filter parameters (e.g.
for word kinds and categories). The NLP module is also exploited by the Ontology
Enrichment tool for the extraction of candidate concepts from the documents;
Fig. 1.The CHRONIOUS Search Module components.
Knowledge Cache maintains a graph database to store the generated indices and
Search Module offers search methods as well as several tools that facilitate the
user’s interaction with the system;
Ontology/Thesaurus module incorporates the domain knowledge. It includes the
CHRONIOUS Ontologies, MeSH and its translations in different languages, as well
as the mapping between MeSH and Ontologies;
Ontology Enrichment module is responsible for the maintenance of the ontology
in a semi-automatic way. It uses NLP tool to pre-process the imported documents and
to find word groups and linguistic patterns that are related to the domain.
In the following paragraphs, the most important modules are detailed.
Natural Language Processing tool
Within the CHRONIOUS Search Module, the NLP tool is applied to the text
documents provided by the transformation component to automatically extract struc-
tured information. The specific annotations provided by the NLP tool are used by the
Document Indexer and Concept Associator as well as by the Ontology Enrichment
Tool for proposing new concepts. The NLP Tool runs on a server and can be accessed
through a web service. Transformation tool provides methods for receiving docu-
ments in different formats and responds with processed data. These operations also
allow defining filter parameter (for annotation types and features) in order to focus on
the actual required data and to reduce the number of delivered annotations.
For implementing the NLP algorithms the GATE framework  has been used that
allows developing and deploying software components that process human language.
The NLP Tool extracts the headwords of a text using different modules: the Token-
izer splits the sentences of the text obtained by the Sentence Splitter into very simple
tokens such as numbers, punctuation and words of different types; the Part-of-speech
(POS) Tagger produces part-of-speech tags (e.g. noun, proper noun, preposition) as an
annotation on each word or symbol. Finally the Morphological analyser identifies the
lemma (headword) and affix (e.g. “runs”, “ran” and “running” has all “run” as
lemma) by considering one token and its part-of-speech tag, one at a time.
In addition to the above standard processing sequence, in order to satisfy the require-
ments of the Ontology Enrichment Tool and the Indexer, in the NLP Tool some addi-
tional resources of the GATE framework have been integrated:
OntoRoot Gazetteer : A GATE plugin that produces ontology-aware annota-
tions for extracted terms (ontology matching using names and labels of the ontol-
Shallow Parser: analyzes the sentences to identify word groups by linguistic pat-
terns (e.g. “chronic disease”, “lung function”). For the prototype implementation,
which is focused on the English language, we extracted and utilized a JAPE
transducer, executes a linguistic pre-processing in terms of ontology learning,
from the Text2Onto project .
RegEx-Pattern Matcher: matches the lemma of a token with word patterns de-
fined as regular expressions. It has been implemented as JAPE resource, which
expects an input list with entries coded as regular expressions.
Dictionary Matcher: matches the lemma of a token to a (common) dictionary. In
our implementation it uses the WordNet dictionary  and Java API for search-
Thesaurus Matcher: matches the lemma of a token to a (domain) thesaurus. For
the target medical domain we have implemented a JAPE resource, which uses the
MeSH-Mapping API to access MeSH terms and the mapped ontology concepts.
To better support users various search methods have been provided together with
tools that facilitate the interaction with the system and with functionalities for the
ontology/thesaurus browsing and the query building. Search is then possible as:
• Metadata Search, which is a keyword-based search looking into the metadata in-
formation associated with a document and indexed in the Knowledge Cache.
• Conceptual Search, which allows the user to submit queries at a higher level of
abstraction as compared to keyword based search by retrieving content that does
not contain keywords from the query. The conceptual search consist of three
components the user can exploit to build and refine their queries:
o An Ontology Browser panel displaying a clinical view, which is a simplified
view of the ontology structure enabling the user to exploit relations with
o A Concept Finder tool that provides on-type suggestions of concepts in-
cluded in the Ontology and/or MeSH and that exploits the multilingual capa-
bilities of MeSH.
o A Query Building tool where queries are build combining Ontology and
MeSH concepts through boolean expressions.
• Free-text search: this is also a conceptual-based search allowing the user to sub-
mit queries in natural language by exploiting the NLP capabilities. The NLP
Tool processes the sentence, keeps the valuable information and the search in-
vestigates the associations stored in the Knowledge Cache. This search exploits
the multilingual capability of MeSH allowing the user to use concept terms in
their languages (currently in Italian, Spanish and Portuguese).
To avoid copyright infringement, while guaranteeing a sufficient coverage of the
search capabilities, full papers are used only for indexing purpose. The search is per-
formed on the metadata and indexed concepts, and displays the results’ metadata
which also include for published document the related DOI. Therefore access to the
document is performed by dereferencing the HTTP URI of the paper's DOI respecting
the paper access constraints and users’ subscriptions. The solution has been verified to
be acceptable for getting access to the bibliography provided by the main publishers.
This module provides the access to all the domain and linguistic resources used in
the annotation and retrieval of medical reference documents enhancing the search
Through an ad hoc developed API, the Ontology and thesaurus module provides
access to: COPD , CKD  and MLOCC (middle-layer)  ontologies; MeSH
(English)  thesaurus version 2010; MeSH multilingual lexicographic representa-
tions (currently the Italian, the Spanish and the Portuguese versions have been pro-
vided); mapping between MeSH concepts and correspondent Ontologies classes.
The CHRONIOUS ontologies encode expert knowledge relevant to the envisaged
domain (COPD and CKD) such as to provide a rich science-driven repository of con-
cepts for annotating and searching medical literature in the scope of the project, as
well as a wealth of relations to refine domain-specific literature search. The COPD
and CKD ontologies are built on top of a Middle Layer Ontology for Clinical Care
(MLOCC) whose purpose is to provide a link between the leaves of Basic Formal
Ontology , a foundational ontology used in the Open Biological and Biomedical
Ontologies (OBO) Foundry , and the top-nodes of the domain ontologies. MLOCC
represents, as it were, the common core of biomedical and clinical knowledge shared
by the COPD and CKD Ontologies. Furthermore, we have expanded the ontology
providing reference for selected terms from MeSH. MeSH is a certified and very rich
thesaurus, that covers a large part of the concepts pertaining to the medical domains.
It is already used to index medical articles, but
• it does not deepen specifically the two diseases considered in the project, so it
lacks of the required specificity to support in complex search pertaining the con-
• it is not compliant with the newest web standard suggested by the W3C, so it can-
not be easily combined with the OWL  domain ontologies.
In the project, the former limitations are overcome by ontologies about COPD and
CKD. However, searching for scientific papers may require to move from concepts
strictly related to the aforementioned diseases to concepts less specific and vice versa,
thus some connections between the two diseases-specific ontologies and the MeSH
concepts are required. For this reason, a MeSH-Ontologies mapping linking the con-
cepts provided by MeSH to the ontologies classes has been provided.
To ensure that the thesauri and COPD/CKD ontologies can be fruitfully connected,
we have SKOSified MeSH and their Multilingual versions: we have mapped\encoded
the MeSH content into Simple Knowledge Organization System (SKOS) model fol-
lowing the rules discussed in  and serialized the SKOS in RDF. SKOSyfied
MeSH thesaurus, its translations and its mapping to concept described in the Diseases
Ontologies are accessible by API developed in CHONIOUS which relies on the
JENA framework . Ontologies are accessible through standard OWL 2 API .
Ontology enrichment tool
As mentioned, since in the project we aim at supporting care givers in remote
monitoring and managing chronic patients, for which knowledge and literature are
still at their infancy, it is crucial to have tools for helping the upgrade of the ontology
when new concepts arise. Therefore, we decided to exploit the possibility of deriving
new concepts from pertinent literature in a semi-automatic way. Thus, the so-called
ontology enrichment tool has been developed allowing the identification of gaps in
the ontologies with respect to newly inserted documents to support the ontology cura-
tor. In particular, the tool combines ontology learning techniques and submission
functionalities. Methods for ontology learning are based on linguistic, statistical and
machine learning approaches. Thus the Ontology Enrichment Tool uses the NLP Tool
described in section 2.1 for a linguistic processing of the transformed document data
and annotate the text with several features. Based on these annotations candidate con-
cepts are extracted in a second step. After the pre-linguistic processing the extracted
candidate concepts are rated concerning their relevance by points based on several
• Corpus relevance: the relevance of each candidate concept is determined by com-
puting its average Term Frequency Inverted Document Frequency (TF.IDF) value
with respect to the whole document corpus.
• Domain relevance: matchings with a common dictionary, a domain thesaurus and
with regular expression patterns is used to determinate the domain relevance of a
candidate concept. The assignment of synonyms results in a higher relevance. This
means that the candidate concept has been identified as a synonym of an existing
concept (by dictionary synonyms or ontology mapped MeSH thesaurus terms).
• Subclass-of relations: the relevance of the candidate concept is raised, if it is possi-
ble to assign a subclass-of relation to it. Such an assignment can be determined by
the extraction of vertical relations, linguistic patterns or dictionary hypernyms.
• Concept co-occurrences: the co-occurrences of the candidate concept with con-
cepts in sentences can be considered as an indicator of a possible relation extrac-
tion. The average distance in the text between the candidate concept and a concept
within the corpus is calculated as a benchmark.
Candidate extensions need to be validated by clinicians which are supported by the
submission facility of the Ontology Enrichment Tool, i.e. a GUI that allows the user
review the extracted data. Furthermore the user has the possibility to administrate the
candidate concepts in terms of a workflow state (“new”, “to validate”, “postponed”,
“accepted” or “rejected”). In this way the submission function of the Enrichment Tool
provides suggestions for new concepts (possibly with subclass-of relation assignment)
or new labels of existing concepts, but modifications of the ontology must still be
done by the ontology expert with an external tool (e.g. Protégé).
Building domain-specific ontology requires large involvement of experts and close
cooperation with technical partners. Both the CHRONIOUS ontologies developed by
computer engineers and the mapping relations between MeSH concepts and ontology
classes have been evaluated by medical experts. Questionnaires checking the agree-
ment among medical experts on the correctness of the developed ontologies and of the
MeSH mapping relations have been created by test engineers and filled in by COPD
and CKD clinicians. The evaluation result shows that most of the ontology classes
have been defined correctly, a few ontology classes needed to be modified (eight of
964 COPD classes and nine of 972 CKD classes), and together 150 new classes
needed to be added (80 new classes for COPD ontology and 70 for CKD ontology).
That is less than 7.8% of the total number of defined ontology classes. Furthermore,
42 of 120 mapping relations between MeSH concepts and COPD ontology classes, as
well as 43 of 138 mapping relations between MeSH concepts and CKD ontology
classes have been modified. The CHRONIOUS search mechanism overcome Google
Scholar, PubMed and GoPubMed by combining the MeSH potentiality with the two
specific domain ontologies. Indeed Google Scholar supports the search of documents
by providing a simple search by keyword mechanism. Such a mechanism is based on
a pure syntactical search of the documents, without taking into account any semantic
interpretation of the proposed query. As a consequence, two queries having different
syntax but equivalent semantics yield different search results. PubMed improves
Google Scholar by exploiting the synonymous provided by MeSH, where the thesau-
rus is used to produce a semantic interpretation of the query keyword mechanism. The
GoPubMed search engine, improves PubMed by considering the concepts of Gene
ontology (GO) during the query refinement step. In this case, the clinicians are facili-
tated during the query composition by exploiting both the terms of MeSH and the
terms carried by the Gene ontology. Neither PubMed nor GoPubMed are supported
by the specific knowledge related to the specific domain of the COPD and CKD on-
tology that provide additional structure and depth as far as knowledge items are con-
cerned; this additional representational power is exploited to improve annotation of
and hence search over medical literature.
In order to complete the previous qualitative comparison of the CHRONIOUS
Conceptual Search, a quantitative evaluation of the performance of the system, i.e.,
the exactness and completeness of its search result, has been evaluated. Exactness (or
Precision) measures the number of correctly found documents – documents which are
relevant to the search query – as a percentage of the number of documents found,
whereas completeness (or Recall) measures the number of correctly found documents
as a percentage of the total number of existing relevant documents. Usually, Precision
and Recall scores are not discussed in isolation. Instead, both are combined into a
single measure – the F-measure , which is the weighted harmonic mean value of
Precision and Recall. To quantitatively evaluate the search performance of the
CHRONIOUS Conceptual Search, seven commonly used COPD terms and five CKD
terms have been defined by medical experts as search queries. The F-measure of
search results of each search query with the CHRONIOUS Conceptual Search has
been calculated and compared with one of the most popular text-based search engines
– PubMed Central. To have meaningful comparison of the two systems we executed
the test with same document database. Thus, documents meeting with certain limita-
tions (e.g., published in a specific year, contain specific terms in their text body, open
access, etc.) have been downloaded from PubMed Central and uploaded into the
CHRONIOUS document repository. The evaluation result shows that the overall
search performance of CHRONIOUS Conceptual Search with defined search queries
is in most cases better than PubMed Central. Fig 2 shows the F-measure comparison
results with two search terms: “Inhaler Device” and “PostBronchodilator Spirome-
try”.1 Furthermore, the overall end user satisfaction of the CHRONIOUS Search
Module has been investigated with the medical experts by a questionnaire. The
evaluation has shown that 90% of them generally accept the Conceptual Search de-
velopment and 60% of them gave positive evaluation (“Somewhat satisfied”) to the
Free-text Search option.
Fig. 2. F-measure comparison between CHRONIOUS Conceptual Search and PubMed Central
with search query (a) “Inhaler Device” and (b) “PostBronchodilator Spirometry”. The X-axis
presents the estimated total number of existing relevant documents in the repository for the
corresponding search query (increased incrementally).
In this paper, we presented a literature search module developed within the FP7 IP
Project CHRONIOUS to complement out of shell search modules such as Google
Scholar, PubMed and GoPubmed by improving the search capabilities within the
COPD and CKD pathologies, and by providing the possibility to index and retrieve
hospital-specific internal documentation. The adopted ontology-thesaurus approach
aims at overcoming limitations of pure syntactical document search, in which two
queries having different syntax but equivalent semantics yield different search results.
The search mechanism combines MeSH terminological power with formal knowledge
1 The search results of COPD terms have been retrieved from 930 documents that were published from
January 1st to December 31st, 2008 and contained the term “COPD” in their text (incl. title, abstract, text
body, figure/table caption, etc.).
of the domains specified by the ontologies. MeSH and domain ontologies do not
compete, but fulfill complementary tasks. MeSH provides well-established generic
medical terminology, multilingual lexical representations and synonyms while do-
main ontologies provide additional representational power can be harnessed to im-
prove medical literature annotation and retrieval.
Acknowledgements. This research has been supported by the IP project
CHRONIOUS (EU Contract N. FP7-ICT-2007–1–216461). The authors want to
thanks all the partners for their support. This paper was written as a joint contribution;
authors are listed in alphabetical order grouped by their affiliations, besides they have
equally contributed to this paper.
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