Aerospace Information System based on Semantic Technonogies
and Ontology Management
A Web Portal for Semantic Search and Document Categorization
F. Gargiulo
, G. Zazzaro
, G. Romano
, G. Gigante
, A. Raggioli
and R. Fusco
Soft Computing Lab, Italian Aerospace Research Centre – Via Maiorise, Capua (CE), Italy
GruppoMeta, Via G. Porzio, 4 – Centro Direzionale, Napoli, Italy
Keywords: Aerospace Lexical Domain Ontology, Word Sense Disambiguation, Aerospace Taxonomy, Semantic
Search, Document Categorization, Aerospace Information System.
Abstract: This paper describes a semantic search tool based on our experience in using a new lexical domain ontology
for aerospace integrated with an open source general purpose ontology to support aerospace engineers in the
timely semantic retrieval of the knowledge. The semantic search module represents an integrated tool
dedicated to the semantic search, extraction and classification of information and knowledge in aerospace
domain. It describes the implementation of a disambiguation algorithm based upon these ontologies and a
new interesting graphical user interface for semantic searches is presented. Furthermore, next to the domain
ontology, a taxonomy for classifying aerospace documents is also proposed. The document classification
algorithm that leverages the deep integration between the proposed lexical domain ontology and taxonomy
is also described. Finally, some considerations about the usage of the semantic search module by the side of
domain experts, semantic experts or common users are reported.
The growing demands of developing complex
information systems saving costs and guaranteeing
reliability leads to the adoption of different
paradigms facilitating knowledge sharing,
interoperability, completeness, and reuse. This paper
presents a new integrated tool for semantic search,
extraction and classification of information and
knowledge in aerospace domain. It is based on:
a) The proposal of a new lexical ontology and a
new taxonomy for aerospace domain;
b) The integration of both of them with open
source general purpose ontologies;
c) The implementation of a disambiguation
algorithm based on these ontologies;
d) The implementation of a classification
algorithm that leverages the deep integration
between the new ontology and the new
e) A graphical user interface that allows natural
language queries and “by meaning” queries on
a very large number of documents (books,
papers, news, websites, etc.) both for experts
and common user.
The tool represents a subsystem of a wider
architecture that was implemented in SIA portal. As
described below, SIA – Sistema Informativo
Aerospaziale, Aerospace Information System
( – is software infrastructure for access,
retrieval and exploitation of technical, scientific
information for aerospace and high-tech user
community and related domains and added value
user services.
It comprises the following major subsystems:
I. Web Portal subsystem;
II. Semantic Search subsystem;
III. Linked Open Data subsystem;
IV. Document Warehouse subsystem.
This paper mainly focuses on the Semantic
Search subsystem and provides a only brief
description of the other subsystems.
Gargiulo F., Zazzaro G., Romano G., Gigante G., Raggioli A. and Fusco R..
Aerospace Information System based on Semantic Technonogies and Ontology Management - A Web Portal for Semantic Search and Document
DOI: 10.5220/0004994703410348
In Proceedings of 3rd International Conference on Data Management Technologies and Applications (DATA-2014), pages 341-348
ISBN: 978-989-758-035-2
2014 SCITEPRESS (Science and Technology Publications, Lda.)
The ontology term is borrowed from philosophy,
where an Ontology is a systematic account of
Existence. In Artificial Intelligence context we can
describe the ontology of a program by defining a set
of representational terms. In particular, an ontology
is an explicit specification of a conceptualization of
a domain of interest (Gruber, 1993).
An ontology can be written for different tasks
since many domains may need a specific and formal
representation of knowledge:
Data Integration: the purpose is to integrate
heterogeneous information systems. Often different
databases retain the same type of information in
different patterns of data modeling. An ontology can
be used as mediator between database schemas,
allowing you to integrate information in different
patterns and to realize an interpreter between data
from two different sources.
Information Retrieval (IR): IR is the set of
techniques used for the recovery information in
electronic format. IR is the largest field of
application of ontologies because they improve the
accuracy of online searches by adding semantic
information which is useful to reduce the search
Semantic Web: ontologies can be used to solve
various problems of heterogeneity of the Web.
Ontologies can enrich internal representation
(metadata) of meaningful semantic labels, can build
representations to model users with respect to their
information needs and build mechanisms of
mediation between metadata and information needs
of the user (to build custom interfaces).
There are different types of ontologies depending
on abstraction level (Guarino, 1998):
Top-level: ontologies with very general or
abstract concepts such as space, time, behavior,
action, etc. which are independent from specific
domains, so as to be useful for their reusability in
other ontologies. For this reason they are also called
Meta-Ontology. Such ontologies alone may have
little use but they are great for building knowledge
Domain and Task Ontology: this type describes
the vocabulary related to a generic domain (
aerospace, medicine, geography) or a generic
problem (e.g. diagnosis, configuration) and it can
specify concepts of a top-level ontology.
Application Ontology: this kind of ontology
describe concepts in a specific domain and the
problems derived from it.
The use of ontologies to provide a single and
shared representation of knowledge for all system
components has been largely motivated in literature
in the last decade: an interesting review of the state
of art of ontology-based software engineering can be
found in (Calero, 2005; Castañeda , 2010; Gasevic,
2009; Farfeleder, 2011), and the last recent
proceedings of international forums like SWESE,
W3C, SEKE discussing synergies between ontology
engineering and software engineering. Synergies are
discussed focusing on different key concerns.
The first concern is related to the development of
life cycle integrating the adoption of ontologies.
The second one proposes methods to develop
ontologies. Literature recognizes mainly two
approaches: the experience-based (Gómez-Pérez,
2004) and the “engineered” based which defines a
set of life cycle activities aiming at prototype
refinement (Uschold, 1996; Noy, 2001).
The third concern is related to the development
of ontologies. Literature proposes ontologies with
different richness of expressivity and to different
purposes. Lightweight ontologies are principally
taxonomies, they include concepts, relationships
between concepts, and properties describing
concepts. Heavyweight ontologies are those which
model knowledge and define restrictions on
domain semantics, by means of axioms and
constraints. Ontologies are developed to support the
development process, to support the knowledge
sharing of general information about “the real
world” and the application domain (medicine,
automotive, railway, aerospace) (Calero, 2005).
The fourth concern aims to develop a complete
framework proposing both new methodologies and
tools to guide the use of ontologies and to apply it to
each phase of software life cycle (Gasevic, 2009). In
the aerospace industry domain ontologies are a
constant in each approach but are rarely defined. An
important work describing a basic ontology for
aerospace is presented in (Malin, 2006) where the
basic concepts of functions, entities and problems
are defined. Specific ontologies are proposed to
support the justification of design, RaDEX (Kuofie,
2010), and the aerospace composite manufacturing
domain (Verhagen, 2011), to define UAV missions
(Schumann, 2012) and to support the intelligence,
surveillance, and reconnaissance (ISR) mission.
NASA addresses the use of ontologies in
different contexts. The CDXA program aims to
integrate knowledge in complex programs proposing
a constellation of ontologies (SWEET, 2011).
Recent European projects adopt sematic
techniques in aerospace domain. The EU CESAR
project bring innovations in the two most
improvable engineering disciplines: Requirements
engineering and Component-based design of
automotive, aerospace and railway (Bogusch, 2011).
SIA project activity has been conceived as a
strategic information tool in order to facilitate the
growth of aerospace knowledge in the Regione
Campania as it is oriented to the main aerospace
actor such as SME, Universities, Research Centers,
etc. This work has been carried on within the
research project SIA, funded by the Campania
Region and EU within the framework of POR
Campania FESR 2007 – 2013.
SIA is SW infrastructure for access, retrieval and
exploitation of technical, scientific information for
aerospace and high-tech user community and related
domains and for providing them added value user
The objective of SIA project is the development
of a SW infrastructure able:
to guarantee access to the most important
source of information related to the aerospace
domain (paper, technical report, e-books, e-
to facilitate the exchange among users of
knowledge related to the research and
development activities in the aerospace
to support user in the optimization of complex
activities such as certification task, e-learning,
SIA services are accessible through a vertical
web portal based on semantic features with
aerospace ontology and taxonomies with which SIA
make documents content more meaningful for
an efficient search and access of information;
provide to users a relevant information as
result of a search avoiding the negative
experience of information overload or out of
enables users to activate strategies for a more
efficient sharing and spread of domain
Furthermore, SIA web portal offer to end-users
the following functionalities:
1. user profile management for adaptive access
to SIA services and content;
2. advanced information retrieval able to more
exploit semantic information representative
both of documents contents managed by SIA
and user preferences;
3. semantic enterprise wiki in order to facilitate
information exchange among users and to
improve collaborative work;
4. Press and automatic News generation and
aggregation for a more wide information
In regard to 2 and 4 above, the user can define an
alert (i.e. a set of semantic queries) and she will be
notified when the system will index any document
that satisfies the defined alert.
SIA operational context is built upon the
following software subsystems conceived to satisfy
project requirements:
Web Portal Subsystem: SIA portal through
which services are made available to users (Search,
Browsing, Blog, Wiki, News, News Alert, e-Press,
Reference). Access to SIA web contents and
services depends on user profile regardless of user
device (PC, PDA, Tablet, etc.).
Semantic Search Subsystem: this component
guarantees features in terms of automatic retrieval of
predefined information sources, content filtering,
parsing, word disambiguation, data extraction and
correlation, data classification, indexing, data
Linked Open Data Subsystem: a triple store
based on Virtuoso and a SPARQ endpoint for
sharing information about the document indexed by
Semantic Search subsystem in RDF format
according to W3C best practice regarding semantic
Document Warehouse Subsystem: assures
loading and storage of structured information
generated in the Semantic Search Subsystem in a
document warehouse for further user analysis tasks
based on OLAP features.
The Semantic Search subsystem, as mentioned
earlier, is characterized by the integration between
the proposed lexical ontology and open source
general purpose ontologies joined to the
disambiguation and classification algorithms and the
graphical user interface. The following paragraphs
describe the features of each of these aspects.
4.1 Aerospace Lexical Domain
The ontology development was inspired by the steps
identified by Pinto & Martins (Pinto and
2004), which is roughly reflected in this section.
Specification: the aerospace domain ontology has
a twofold objective. From a broad perspective, the
purpose of this ontology is to fill a gap by
introducing a new domain ontology. In a more strict
sense, the purpose of the ontology is to support
knowledge management, allowing the indexing,
disambiguation, classification and search for
contents in aerospace domain. The scope of the
ontology is limited to its domain, and within this
scope, the emphasis lies on the concepts and
relationships of meaning among them.
Conceptualization: the applicable concepts and
relationships come from an amalgam of various
sources. The domain experts involved into the SIA
project taken very carefully into account the existing
ontologies (SWEET, 2010; Hannessen, 2003; CIRA,
2012). These ontologies have been studied in order
to capture the current state of the art.
Formalization: the lexical domain ontology is
available in two languages: Italian and English
language. The following semantic relations in both
languages are handled:
Synonymy: it indicates the relationship
between two terms that have the same or
nearly the same meaning in aerospace domain,
such as “space” and “cosmo”;
Hypernymy: the relation of being
superordinate or belonging to a higher (more
abstract) rank or class. Inverse of hyponym.
For instance, “tree” is hypernym of “oak” and
Hyponymy: used to designate a member of a
class. For instance, “Boeing 747” and “Airbus
A380” are hyponym of “Aircraft”;
Meronym: a word that denotes a constituent
part or a member of something. For example,
“wings” and “engine” are a meronyms of
Holonym: the opposite of a meronym is a
holonym, the name of the whole of which the
meronym is a part.
Implementation: the domain ontology contains
7,497 Italian words, 5,962 Italian synsets, 5,750
English words and 5,127 English synsets. The Italian
and English version of the ontology share 4,344
multilingual relations. In addition, the Italian version
contains other 1,405 relations. The general purpose
multilingual ontologies used are actually: the
WordNet developed at the Princeton University
(Fellbaum, 1999) for English language and the
MultiWordNet for Italian language. For this reason,
a natural choice was store the domain lexical
ontology in the same database schema of
MultiWordNet and therefore a custom simple
ontology editor was developed. This editor allows to
manage concepts, synsets and relations in the
MySQL database schema. Also, the editor allows the
editing of the domain taxonomy, described below,
contained in the same database. A simpler version of
the editor has been included in the Web Portal
subsystem and it is available for administrative
4.2 The Ontologies Integration
The lexical ontologies in the Semantic Search
subsystem are three: WordNet for English language,
MultiWordNet for Italian language and the
aerospace domain lexical ontology for both
languages. The WordNet contains about 117,000
synsets and the currently available release of
MultiWordNet includes information about 58,000
Italian word meanings and 32,700 synsets.
During the morphological analysis, the
disambiguation and indexing phase of an Italian
document, the tool can rely on the MultiWordNet
and the domain ontology to detect the concepts and
assign the meaning to the words in the document; it
uses the WordNet and the domain ontology for
English language instead. Before the disambiguation
phase, concepts that are compound words,
abbreviations or acronyms are detected. These
concepts are usually relevant in domain terminology
and their identification permits a more accurate
tokenization of the sentences. If a token is related to
a concepts belonging both general purpose and
domain ontology, the system performs a sort of
context analysis to determine if a general or a
domain specific meaning would to be assigned to the
word. The system tries to guess if the context is
strictly related to the domain analyzing the previous
and the following sentences and counting the
number of tokens related to the domain terminology.
If this number exceeds a fixed threshold then the
system chooses the domain meaning otherwise the
general meaning.
Even if the domain meaning was chosen, the
system also memorize the general meaning; this
information can be used during the disambiguation
of the adjacent token that do not belong to the
domain ontology.
At the end, the disambiguation algorithm assigns
an identifier to the token. Note that some token does
not have an identifier, in particular: the terms that
were not recognized, articles, conjunctions,
prepositions, etc. The identifiers follow the
MultiWordNet pattern and are formed by part of
speech followed by (#) character and a numeric
string consisting of 8 characters, e.g. n#00001234
(“n” means “noun”). Identifiers of domain
ontology to be unique in the overall system are
prefixed by “d” (domain) and a number. For
example d1n#00001234 represents a domain
concept. The number allows the simultaneous
presence of more domain ontologies and
distinguishes which of domain ontologies the
concept belongs (in this work there is only one
domain ontology).
At this point, the elaboration of a natural
language queries can be briefly explained. In fact
when the user inserts a sentence and executes a
natural language query, the sentence will be
processed in the same manner described above and
at the end of the elaboration the system knows which
concepts – and their ontologies - must search for. In
other words, the system tries to determine the correct
meaning of each word of the sentence from the
sentence itself and therefore the sentence represents
the context wherein the disambiguation is
performed. On the other hand, if the user inserts a
word and executes a “by meaning” query the system
prompts the user to select one or more meanings
among those present in ontologies, as described in
more detail later.
4.3 The Disambiguation Algorithm
The disambiguation algorithm adopted is an
implementation of a variant of the JIGSAW
algorithm for word sense disambiguation proposed
by University of Bari (Basile, 2007). For reasons of
space the algorithm will not be described here
(please, for the details refer to the original paper and
to the documentation of the University of Bari). In
this paper only the main changes occurred during the
implementation will be described. The changes were
aimed at improving integration with the other
components of the system and an improvement of
the performance.
The first adjustment involved the modules
assigned to morpho-syntactical analysis and
tokenization. The original system had its own
morpho-syntactic analysis module, this module has
been removed and replaced by two modules,
respectively: Gate for English language and
TreeTagger (Schmid, 1995) for Italian language.
With regard to the performance, it was noted that
the introduction of a caching mechanism runs the
algorithm about 5 times faster. This mechanism
helps to avoid re-running the analysis of a token if a
syntactic constructs (with equivalent terminology)
was analyzed above. Therefore, could be formulated
a conjecture about the high frequency of re-use of
terminology in very specialized domains documents
(such as aerospace).
4.4 The Classification Algorithm
A Bayesian model is trained in order to classify the
indexed documents in taxonomy categories, Table 1.
It is based on the Weka (Hall, 2009) implementation
of the Bayesian multinomial classifier named
NaiveBayesMultinomial (McCallum , 1998).
Table 1: The domain taxonomy.
Level 1 Level 2 Level 3
Design and Validation
Traffic Management
& Airports
Design and Validation
Ground Support &
Launch Operations
During the training phase of the classification
model a standard training set based on an association
between documents and classification taxonomy
categories was not used. In fact such a kind of
training set requires a huge number of documents
manually tagged with the category. A different
approach instead was proposed; it is based on the
presence of domain ontology and semantic
disambiguation system. As mentioned, the
disambiguation algorithm determines if a word can
be associate to a concept belonging to the domain
and this information can provide a significant
contribution to the attribution of a document to a
specific category. In fact, in the domain terminology
are often present terms closely related to some
categories, such as the name of the specific missile
propellant, on-board equipment, etc. The presence of
such terms often accurately directs the document to
a category and, at the same time, filters out
potentially noise resulting from the generic
Then, the domain ontology concepts were
associated with the taxonomy categories with a
weight that represents the degree of membership. In
a number of cases it was not possible to create this
association because the concept is too general (i.e.,
"Flight") or the domain experts did not found the
association. About 2,880 domain ontology concepts
are associated to one or more taxonomy categories
and with these concepts the training sets – one for
each category – are built. In particular, the training
set of a category contains the concepts associated to
that category and the weight of the association
represents the label. When a document has to be
classified, the system detects all domain concepts
associated to one or more categories and submits
them to the classification model. Finally, the
classifier evaluates the degree of membership of the
document to every taxonomy category.
4.5 The Semantic Search
Semantic Search function is the core of SIA. User
can search documents through input query written in
natural language or keyword-based. Until now,
about 800,000 documents in both languages were
indexed and it has been designed in order to aid user
in the search of useful information and documents.
At this end, in the SIA system three search features
have been implemented:
Natural Language Search: an user can search
documents through input query written in natural
language. This search activates semantic
disambiguation of the user input text and the system
founds documents containing semantically
disambiguated terms consistent to the context
analysis performed on input text.
Lemma Search: SIA identifies the different
meanings of each user input text through a querying
into a general ontology (WordNet and
MultiWordNet) and a domain ontology (SIA
aerospace ontology). SIA shows an interactive
window with all possible meaning and relations
related to the search term. User can select a specific
meaning (lemma) and its semantic relationship with
other lemma in order to refine search. In SIA, this
kind of query is also called “by meaning”.
Keywords Search: traditional full-text search
performed on the basis of user query terms.
Whatever is the search feature selected by the
user, SIA returns search results grouped by
predefined facet: data source, format (html, pdf,
word, etc.), category (main aeronautical and space
taxonomy class), type (magazine, journal, etc.),
authors, keywords, domain entity. Furthermore,
search results are ordered according to a score
function evaluated on the basis of the Virtuoso
scoring algorithm. After the user selects a document,
the system redirects her to a detail page. In this page
there are also a list of similar document to the
selected one. As described previously, the Natural
Language Search lead back to Lemma Search
therefore the latter will be described in more details.
The user executes the following steps: selects
this kind of search, types a word and chooses the
language. At this point, the system will guide her in
the choice of the meaning or meanings of the word
that should be looking for. It displays a tree like the
one shown in the figure 1.
Figure 1: The GUI of lemma search in SIA.
All nodes of this tree, but the root, are concepts
contained in the general or domain ontologies.
On mouse move the system shows a tooltip for
each node with the exact definition of the concept
and the lemmas it contains, figure 2.
Figure 2: The tooltip with definition of the general
meaning of the term (the pink top node) “fuselage”.
The red node is the root of the tree and it
represents the term T that the user entered
(“fuselage” in figure 1). The children of the root
represents all the broad meanings of the term T. In
practice, for each child C of the root the term T is a
lemma of C (i.e., T belongs to the synset of C).
Moreover, if D is a child of C it means that there is a
relationship between C and D (in one of the two
ontologies) and the color of D represents the type of
relationship. As mentioned, the semantic relations
handled are: synonymy, hypernymy, hyponymy,
holonymy, meronymy; it is possible to think the
concept D as a "specialization" of the concept C (for
example, D is "part of" C or D is an hyponym of C).
Note that the term T can belong to both concepts
C and D. In this case, the root is connected only to
the node C because C is a more general concept of
Each node in the tree, but the root, can be
selected and the search will return documents in
which the term T is used in the meanings that
correspond to nodes/concepts selected.
The interpretation of the edge between the root
and its children is different with respect to the
interpretation of the edges the links a child of the
root to its children. In the first case, the children of
the root are the broad meanings of the term T. At
this level the user manually disambiguate the term
and the color of the child indicates the respective
ontology, whereby the user can decide whether to
continue the search on only one of the ontologies, or
on both.
The other edges always represent semantic
relations and the color of the children of a child of
the root specifies the kind of relationship. At this
level, the user refine the meaning of the concept that
must be searched for.
This type of representation makes it possible to
distinguish the concepts that belong to the domain
ontology from those that belong to the
MultiWordNet. In fact, the concepts of domain
ontology have different colors are drawn in a
different side of the screen than the general
ontology, figure 1. The layout used to draw the tree
will tend always to separate the nodes of an ontology
from those of the other. The number of nodes in the
domain ontology compared to the number of nodes
of the general ontology provides intuitively a
measure of how much the current search is relevant
with the aerospace domain.
This graphical user interface can be used by
expert users who are familiar with the use of
semantic relations; by user who are familiar with
domain terminology; by common users.
For the first group of users, the GUI lets them
select the semantic relations while for the other
groups of users, they simply selects the meanings of
a term and can freely ignore relations. In any case, it
is clear that the lower nodes of the tree the more
restrictive and specific meanings is associated to the
This paper describes an integrated SW tool aimed to
semantic search, extraction and classification of
information in the aerospace domain. A new lexical
aerospace domain ontology is proposed. The tool is
based on the integration between lexical ontologies
and algorithms operating on them and, in order to
get a better user experience, a GUI is also proposed.
Experimental results regarding the performances
of the tool was obtained in two ways. Precision and
Recall measure (Davis, 2006) were firstly calculated
on a test set consisting of 50 documents. The results
were approximately the same in (Davis, 2006). Due
to the small number of documents, these measures
were not been interpreted as a measure of the
performance of the disambiguation algorithm but
rather as a confirmation that the changes introduced
(i.e., Gate, TreeTager and caching) did not worsen
the original algorithm. On the other hand, the
response of domain users who have followed the
experimental phase, represented a very positive
qualitative assessment of the tool. In order to obtain
consistent experimental results about the global
performance of the described semantic search
module it is necessary a comparison between similar
systems developed for the aerospace domain that
expose similar functionalities (lemma search, natural
language search, etc.). Also, the employed set of
ontologies plays a central role in the performance of
the each system and how effective is a comparison
among systems based on different ontologies is not a
trivial matter. On the other hand, for the
disambiguation algorithm used in semantic search
module it is possible to refers to the experimental
results provided in (Basile, 2007). In particular, the
disambiguation algorithm has been evaluated by
SemEval-2007 task. The algorithms were scored
according to standard IR/CLIR measures as
implemented in the TREC evaluation package
( works will aim to the
maintenance of the lexical ontology. Updating and
correcting the implemented ontology will be
achieved by the publication of the ontology editing
functions and the preparation of a controlled change
management process for the approval of changes
suggested by users.
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