Jaypee Institute of Information Technology. Noida, India. Jorge Morato, Christos Dimou. Dept. Computer Science & Engineering. U. Carlos III. Madrid, Spain.
Ontologies evaluation for a conceptual retrieval system of metadata schemes: case study Vicente Palacios, Sonia Sanchez-Cuadrado
Divakar Yadav
Dept. Computer Science & Engineering U. Carlos III Madrid, Spain
Dept. Computer Science & Engineering Jaypee Institute of Information Technology Noida, India
Jorge Morato, Christos Dimou Dept. Computer Science & Engineering U. Carlos III Madrid, Spain
them, users must either previously know the existence and location of this resource or they must develop their own knowledge systems. For this reason, different ad-hoc knowledge representations and metadata vocabularies worsen the capability to reuse and retrieval. The increasing number of metadata schemes has motivated a need for mechanisms that allow finding and selecting the right scheme for a specific context. To accomplish this goal, we need inference engines that operate on a domain model which includes: the vocabulary of concepts, the properties that relate them and the rules that control it. This is where ontologies come into play. Addressing the domain of knowledge conceptualization [6], ontologies can be classified into: a) representation, b) generic, c) domain and d) application ontologies. Representation ontologies provide the underlying concepts to the paradigms or formalisms of knowledge representation. They provide the necessary vocabulary for modeling other ontologies using a specific knowledge representation paradigm. Generic ontologies (including upper ontologies) provide generic terms that are reusable in different domains. Domain ontologies describe a field of knowledge only. Finally, application ontologies are implemented for particular uses. Upper ontologies can relate domain and application ontologies through a share and abstract conceptualization. Concepts are related through alignment between the schemas and the reference ontology. The latter provide the necessary ontological framework for linking specialized ontologies [7, 8]. Bodenreider and Burgun describe reference ontologies "as developed independently of any particular purpose and serve as modules sharable across domains" [9]. However, applying reference ontologies requires an evaluation to select the
Abstract— In this article we perform a qualitative analysis of well-known generic ontologies according to their retrieval potential in order to implement a conceptual retrieval system. This retrieval system aims to search metadata schemes. The main problem in the implementation of retrieval system has been finding a reference ontology covering the domain and matching the system's requirements. We performed an evaluation of ontologies' characteristics for their suitability to represent the semantic of specific metadata scheme. Finally, PROTON has been selected as ontology due to its extensibility, adaptability to the domain, adequacy to the retrieval system and its availability. The principal contribution of this study is the provision of guidelines towards the selection of ontologies to be mapped, based on qualitative analysis and experience. Keywords— Semantic Web; information retrieval; alignment ontologie;, upper ontologies; ontology reuse
I.
INTRODUCTION
The Semantic Web services need knowledge based systems that solve complex problems such as combining information from heterogeneous sources. Palacios [1] claims that there are two problems for semantic retrieval that should be fulfilled before including knowledge resources on a large scale. First, the knowledge resources are characterized the by lack of agreement on the representation; the same facts can be represented by different knowledge structures and naming conventions. For example, there are many vocabularies representing the same terms, like Zthes [2, 3], Thesaurus’ PSI [4] or SKOS [5]. Second, the retrieval techniques used in many semantic search engines are very limited to process these semantic relationships. In consequence, there is poor accuracy to locate resources, that implies that in order to use
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appropriate resource, in accordance to many criteria, properties, questions, and aspects related with validation and verification. There arre also several upper or top-level ontologies available that have been elaborated with diverse classification ccriteria, such as number of concepts, degree of axiomaatization, views of the world, and typology of relatioonships, so an assessment process is required.. Scalability, navigability and usability are some othher features that are also considered in evaluation of ontoologies [10]. Brank et al. [11] have gathered claassical methods to evaluate ontologies as gold-standarrd, applicationbased, data-driven and assessment by humans. Assessment by humans with muultiple criteria approaches is the method most applied by researchers. In MITRE’s comparison, SUMO, U UpperCyc and DOLCE were compared according to eevaluate criteria like open license, modularity and evidennce of use [12]. Mascardi et al. [13] evaluate seven uppper ontologies (BFO, Cyc, DOLCE, GFO, PROT TON, Sowa’s ontology, and SUMO) according to ccriteria such as dimension, language, modularity, applications, alignments with WordNet and liccensing. Other researchers have studied the effective exploitation of semantic annotations such as WSMO O and OWL-S [14]. The basic goal for framing this stuudy is the reuse of generic ontologies, in order to apply it to a conceptual reuse system for metaadata schemes represented by application ontoologies. The methodology has been the following: 11) definition of requirements for the selection of the onntology; 2) preselection of reference ontologies; and 33) evaluation of semantic coverage in generic ontologiess. II.
Fig. 1.
System architecture and goals in SEMSE project
Formalized in OWL-Full, th he Specific Ontology includes definitions, synonyms and multilingual support for each element. Once the schema has been formalized and its semantics haas been defined, the proposal takes a step forward aim ming interoperability among schemas. The reference ontology provides a common vocabulary, common sem mantics and semantic disambiguation. It avoids the onee-to-one mapping and improves the interoperability am mong schemas. The correspondence between the sem mantic representation of the schemas and the backbone ontology is carried out by an independent alignm ment ontology which facilitates the syntactic and semantic extensibility, adaptability and reuse of o the concepts’ correspondences. B. Requirement for the referencee ontology In order to analyze the candid date ontologies, a set of fundamental requirements and a list of characteristics have been defined d which allow us to determine the quality and adeq quacy of a resource. These are mainly based on the criiteria of extensibility, compatibility and reuse. The requirements r for the selection of ontologies are: 1) Extensible/Modifiable, to facilitate the addition of concep pts, relations, axioms etc for the representation of metadata m schemes; 2) Queriable/Navigable, access for realizing queries on its concepts and relations; 3) Laanguage, available in any of the languages recommend ded for the semantic web; 4) Referenced by fo ormal identification, preferably using URIs; and 5) Downloadable. Moreover, desirable characteristtics for the positive assessment of the ontology aree: 1) Popularity; 2) Maintenance/Update; 3) Efficien ncy (query response time and inferences); 4) Com mpleteness; and 5) Structure.
ANALYSIS AND EVALUATION OFF ONTOLOGIES
A. Scenario for reuse ontology The framework for this work is to design of conceptual retrieval system for mettadata schema called SEmantic Metadata SEarch (SEM MSE). The goal of the SEMSE project is to develop a system that allows the representation and conceptuual recovery of metadata schemes available on the w web in order to explore and combine them. We studiedd available open resources, in order to reuse and link seeveral metadata vocabularies and providing a retrieval syystem. In the SEMSE project, the approacch has been to split each metadata vocabulary in two different views (Figure 1Error! Reference source noot found.): the Qualified Schema and the Specific O Ontology. The Qualified Schema is a normalized foormalization in RDF of the metadata vocabulary. IIt also allows including the semantics of each elem ment (‘a’ in the figure), annotated using the hasSemaantics property, against the concepts represented in a sppecific ontology (‘A’ in the figure). The Specific O Ontology is a representation aimed to clarify the semaantics.
We then realized a pre-selection amongst available and free access ontologies. We W have taken into account that various ontologies can serve both for purpose of reference and disam mbiguation of terms. Following the classification prroposed in [8], the selection of reference ontologies started from those of
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for Linguistic and Cognitive Engineering) is an ontology whose objective is capturing through a set of categories and primitives, the underlying ontological categories in natural language, and common sense expressions. Hence, DOLCE’s objective is not to be a universal ontology but a foundational ontology, that is a basis for the construction of ontologies through the establishment of relations and integration of existing ontologies. DOLCE has been reused in various ontologies such as PROTON, SUMO or GUM.
broad coverage in order to obtain a wide semantic coverage with a broad spectrum resource. A large number of elements covered by the semantic of the reference ontology would diminish the necessity for modifications and extension. C. Requirement for the reference ontology Initially, we selected ontologies of broad coverage such as: WordNet, WebKB2 [15, 16Error! Reference source not found.], OpenCyc [17]. WebKB incorporates WordNet along with other ontologies such as SUMO [18] or DOLCE [19]. Next, we justify the most important reasons that have led us to select them as candidates.
SUMO includes general concepts with the objective to serve as a basis for the development of more specific ontologies. These types of ontologies are known as high level or foundational ontologies. SUMO also provides a set of domain ontologies and a Mid-Level Ontology (MILO) permitting links with the generic ontology. The ontologies provided by SUMO are developed under GNU license and are available for downloading.
WordNet has been selected for the following arguments: It is a downloadable ontology which provides a great flexibility with respect to its use and management. It is queriable, navigable and editable. It is a popular ontology since it has been used in numerous research projects and for the development of other ontologies. Finally, the possibility to find a version expressed in RDF/OWL has been positively assessed.
PROTo ONtology (PROTON) is a generic ontology that provides coverage to different aspects, such as semantic annotation, indexing or document retrieval [23]. Its design characteristics are: domain independence, light logical definitions, adherence to the most popular standards and coverage of specific domains, allowing its modeling through the specialization of the included concepts. It is specified through the OWL-Lite language, contains 300 classes and 100 properties developed from OpenCyc, WordNet 2.0, DOLCE and EuroWordNet, amongst others.
WebKB-2: This ontology is chosen because it is shared on the Web, allows querying and updating of its concepts. It incorporates WordNet amongst other ontologies such as SUMO or DOLCE. Also, it resolves WordNet’s ontological representation problems and allows the addition of new concepts and relations shared on Web, preserving them in future updates of the ontology. OpenCyc: It is selected for its extension and completeness, as it contains hundreds of millions of terms. OpenCyc is the open source version of the Cyc technology. It is periodically updated, permits browsing and editing of databases, as well as the construction of inferences.
Table I shows a summary of the characteristics of ontologies. To test the validity of each of the preselected ontologies, we initially selected the latest version expressed in a semantic language and then we evaluated the semantic coverage of the ontology. In fact, in the Linked Open Data Cloud (LoD) there are thousands of ontologies, but there is a need to limit the resources to evaluate. Besides the arguments to select the ontologies already expressed, there are criteria that lead us to not take others into account. The reasons to disregard these ontologies are: their low acceptance by the community (checking statistics published by Swoogle); overlapping with other ontologies analyzed (i.e. Yago with Wordnet); problems in their semantic structure (i.e.Dbpedia), or lack of enough expressivity for the study carried out (i.e. FOAF).
We have also selected well-known lightweight ontologies.that include: SIOC [20] and UMBEL [21]. SIOC (Semantically Interlinked Online Communities) is an RDF ontology designed for the Web whose objective is the integration of information originating from online communities [22]. Recently it has enjoyed a great popularity through its use in various opensource and commercial applications. It is usually utilized in conjunction with the FOAF vocabulary in order to express personal information complementary to social information. UMBEL (Upper Mapping and Binding Exchange Layer) is a lightweight ontology designed to relate ontologies in the Web. It is defined in OWL-Full language, it facilitates interoperability, and provides adequate efficiency in queries and inference [23].
III.
EVALUATION OF SEMANTIC COVERAGE
The objective of this process is to determine if the meaning of the schema elements is included in the ontology. This is a necessary step to perform the semantic correspondence (matching) among concepts. For the process of evaluation of the semantic coverage, the Dublin Core metadata elements scheme was selected. The elements’ senses were obtained
Finally, some reputed foundational ontologies have been included in the evaluation like Dolce, SUMO and PROTON. DOLCE (Descriptive Ontology
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according to their DCMI definition, and then their TABLE I.
WordNet v.2.0 corresponding definitions were sough.
REQUIREMENT ANALYSIS OF ONTOLOGIES a.
Download Navigable Language Extensible URI Coverage Completeness Update Popularity Efficiency Structure Institution
The asterisk indicates that this feature is poorly implemented
WordNet Yes Yes RDF/OWL* Yes N/A N/A
WebKB-2 Yes Yes N/A * Yes Yes
OpenCyc Yes Yes CycL/OWL* * Yes *
SIOC N/A No RDF/OWL No N/A N/A
UMBEL N/A N/A N/A N/A Yes N/A
DOLCE Yes N/A KIF Yes N/A Yes
SUMO Yes Yes KIF/OWL? Yes Yes
PROTON Yes Yes OWL Yes Yes Yes
* Yes N/A No Princeton W3C*
Yes N/A N/A N/A DSTC
Yes Yes * complex Cycorp
Yes N/A N/A N/A Nat. Univ. Ireland
Yes N/A N/A N/A Struct. Dyn. LLC.
Yes Yes N/A simple LOACNR
Yes Yes N/A simple IEEE
Yes N/A N/A simple Karlsruhe Univ.
establish if the significance is correctly represented or not. An example of the difficulties found in the analysis for this DCMI element is hereby presented.
A. WordNet evaluation The search for the latest WordNet version was quite difficult because the version developed by Princeton is not yet expressed in an ontological language. Various versions of WordNet, expressed in RDF [23, 24, 25] were analyzed but they have unsolved ontological representation problems [7]. Some of these problems were: categories do not have intuitive names, superior levels are lightly structured, or concepts do not coincide with the superior levels that are usually represented in foundational ontologies. For example in some cases, the relations subTypeOf and instanceOf are mixed up. This problem stems from the confusion amongst concrete entity types. Finally, the semantic part of, member of, material, and object is not always clear and causes inconsistencies. An analysis of projects, related to the ontological representation of WordNet was carried out. KnOWLer project [26], is a representation of WordNet 1.6 in OWL, but does not resolve the representation problems previously exposed. OntoWordNet [27] proposes the ontological representation of WordNet in OWL and resolves various representation problems, even though it seems that the project has been currently discontinued. WebKB-2 [16] is an ontology that originated from WordNet and resolves the representation problems, however it is not downloadable. W3C has a representation of WordNet in RDF/OWL [25]. This proposal is based on the previous projects, resolves their problems and provides a downloadable version although it is not yet an approved recommendation. For the process of evaluation of the semantic coverage, the Dublin Core metadata elements schema was selected. The elements’ senses were obtained according to their DCMI definition, and then their WordNet v.2.0 corresponding definitions were sought. For each of the fifteen elements of the schema, we present: the element’s name according to the original schema; the sense and the comment of the element according to DCMI; it’s definitions according to WordNet; and a semantic coverage analysis of the term, in order to
Element: title 1. DCMI Sense (a) Definition: A name given to the resource. (b) Comment: Typically, a Title will be a name by which the resource is formally known. 2. WordNet definition(s) (a) (n) title#2 (the name of a work of art or literary composition etc.; ’he looked for books with the word ‘jazz’ in the title’; ’he refused to give titles to his paintings’; ’I can never remember movie titles’) 3. Coverage evaluation result: The meaning is imprecise.
For the evaluation of the semantic coverage amongst the senses, the criteria established in order to perform the data analysis are: equivalence relation (A and A’ are equivalent senses); generic relation (A'⊂A); specific relation (A⊂A'); and no relation among the terms (A≠A'). Twenty three synsets have been analyzed, from which 56.52 presented a partial coverage of their respective senses. Further out of these 23 synsets, 17.39% had specific senses and 39.13% had generic senses. Some of WordNet’s synsets (21.74%) had no relation at all with the DC schema elements senses while an equal percentage had matched exactly (equivalence relationship). From this evaluation the conclusion drawn is that, in most cases the definitions provided by WordNet are imprecise or incomplete. This lack of semantic coverage could be treated with the inclusion of new, more precise and more proximate synsets to the DC elements. Surely, WordNet´s maintenance realized by Princeton, would not take into account those changes and so the new senses should be added again in future versions. On the other hand and with equal importance, Princeton’s WordNet is not exactly an ontology, therefore it is not usually expressed in a semantic language such as OWL. In fact the evaluated version is a W3C’s proposal [25]. Therefore; changes incorporated into WordNet by W3C should in fact be
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been Dublin´s Core schema. While the definitions have been modified, the terms are precisely covered. The ontology adapts to the actual necessities of the proposal for the given schema. An example for the element Title of the WebKB-2 and DCMI coverage is as follows:
made by Princeton before been used as reference ontology. B. WebKB-2 evaluation The evaluation of the semantic coverage of the schema for the WebKB-2 ontology [16], has demonstrated that one of the sources included in it has TABLE II.
CORRESPONDENCES BETWEEN DUBLIN CORE ELEMENTS AND WORDNET
Elements
WN’s senses
WN closer senses
Mapping
Title Creator Subject
10 (title) 2 (creator) 2 (writter) 8 (subject)
Partial (subset) Partial (superset) Partial (subset) Partial
Description
2 (message) 3 (description)
Inexact equivalence Inexact equivalence
no yes
Publisher Contributor Date Type Format
3 (publisher) 2 (contributor) 8 (date) 6 (type) 2 (format)
Partial (subset) Partial (subset) Inexact equivalence Inexact equivalence Exact equivalence
no no no yes no
Identifier Source
1 (identifier) 9 (source)
Partial (superset) Exact equivalence
yes no
Language Relation Coverage Rights
6 (language) 6 (relation) 3 (coverage) 8 (rights)
(06258440) title#2 {09477889} creator#2 {10632698} writer#1, author#1 {06511803} subject#1, topic#1, theme#1–{05742500} topic#2, subject#2, issue#4, matter#3–{05920396} discipline#1, subject#3, subject area#1–{04295930} subject#4, content#7, depicted object#1{06510930} message#2, content#2, subject matter#1, substance#6 {06634244} description#1, verbal description#1–{07102711} description#2 {10334275} publisher#2 {09813697} contributor#2 {14960543} date#1, day of the month#1–{14960779} date#2 {05767228} type#1 {06548555} format#1, formatting#1, data format#1, data formatting#1 {07171397} identifier#1 {07159460} reference#8, source#3–{06585944} source#4– {05761949} source#6, seed#4, germ#1 {06199918} language#1, linguistic communication#1 {00030334} relation#1 {05063545} coverage#2 {13170198} right#2
WN ambiguity no yes no no
Inexact equivalence Exact equivalence Exact equivalence Exact equivalence
no no yes no
Element: title 1. DCMI Sense (a) Definition: A name given to the resource. (b) Comment: Typically, a Title will be a name by which the resource is formally known. 2. WebKB-2 Definition(s) (a) tap#title (? →entity) to specify a name of a resource. Supertype: name. Equal: Title. (b) dc#Title (? →entity) to specify a name of a resource. Supertype: name. Equal: title.
the resource, but this is not the case for other schemas. The realized modifications on the ontology, the new concepts and relations are shared through the Web but not with other users. This means that they are private versions for the user that realizes them. Moreover, they ought to be validated by the managers of the resource. The language in which the ontology is expressed and the mode of their publication in the Web perplex the use of possible local ontologies and impede their download. These disadvantages led to the rejection of WebKB-2 for use as reference ontology.
(c) pm#title ( ) for connecting an object to its title in a natural language. Supertype: Description. (d) #title the name of a work of art or literary composition etc.; ’he looked for books with the word jazz in the title’; ’he refused to give titles to his paintings’; ’I can never remember movie titles’. Supertype: name. Subtype: rubric.heading 3. Coverage evaluation result: The term is covered by the first two definitions.
C. OpenCyc evaluation Even though OpenCyc is one of the most complete ontologies, the semantic coverage of the evaluated elements is occasionally partial or inexistent. Also, the complexity of a high level structure that the ontology presents sometimes complicates the process of searching and/or adding new concepts. The formulation of the ontology as far as its use and maintenance is concerned, is based on the installation of a server in order to share and manage the ontology. However, the main disadvantage is the actual language ‘CYcL’ in which the ontology is expressed. CycL is based on predicate logic and offers a good performance in inference and queries. In addition, it is not a suitable language for the web publication of an ontology and impedes the interoperability with ontologies of this domain. OpenCyc’s version
As illustrated by this example, WebKB-2 includes an acronym (tap#, dc#, pm#), as a prefix in the element’s identifiers which represents the source from which the term comes from. Although it is not exactly proposed by DCMI, an equivalent sense (dc#Title) is included that permits the match between the ontology and the metadata scheme. However, WebKB-2 evaluation presented various problems. The coverage for the presented schema is complete due to the fact that it was selected as a source for the generation of
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which is to be used. The application ontology includes concepts and properties which are not provided by the original reference ontology. Moreover, it includes the modifications on the original elements, i.e. the extensions of the original ontology. The definition language of the ontology is OWL enabling the inclusion of relations with the concepts and properties of the original ontology along with the publication of the resource on the Web. The definition of concepts and properties is realized making use of URIs as a formal identification system, which in turn will facilitate querying and accessing it once it is published.
expressed in OWL, which would permit the interoperability with other web ontologies, presents two important problems. On the one hand it does not provide the complete ontology; it lacks many terms and does not include assertions. On the other hand, the efficiency of that version is much inferior to the outcome of the native OpenCyc’s version. In summary, it implicated the installation of a high performance server in order to obtain a partial implementation of the ontology. These reasons led to the rejection of OpenCyc for use as reference ontology. D. SIOC and UMBEL evaluation The evaluation of the SIOC ontology demonstrated that its application scope is very specific for the objectives that have been proposed for this study. SIOC is centered in the information integration of online communities (fora, blogs, etc.). UMBEL’s evaluation presented the as a disadvantage its extensibility. The extensibility depends on the UMBEL’s principles. The concepts included in UMBEL come from a filtering process applied to the OpenCyc ontology, so, the inclusion of concepts that were not included in OpenCyc and were not obtained from the filtering process, are against this principle.
IV.
RESULTS
The evaluated ontologies are broad spectrum resources as far as semantic coverage is concerned which in any case will need to be extended. Extensibility appears to be a critical characteristic in the selection process. Interoperability is also a requirement for this project due to the necessity for integration of several resources as vocabulary schemas. The languages in which this type of ontologies are expressed do not tend to be thought for the web environment nor to facilitate interoperability with other ontologies and in case an adaptation for this environment is found, they tend to be incomplete or outdated versions which penalize the performance of the resource. WordNet was discarded due to its lack of semantic coverage for the tested schemas and for its dependence on Princeton’s updates and W3C’s versions. WebKB-2 did not represent the coverage of the different analyzed schemas and above all the modifications in the reference ontology were not public. Moreover, OpenCyc presented a partial or inexistent coverage and the formalization of its language along with the structure of the ontology were serious disadvantages for the system designed in this proposal. UMBEL ontology was designed using OpenCyc and inherits some of the same disadvantages. The high level ontologies DOLCE and SUMO are discarded due to the language in which they are expressed as they are inadequate for the requisites set for the system. PROTON has been selected as reference ontology because it has a modular and simple hierarchical structure, with broad semantic coverage, it is designed to be extended and provides a structure which facilitates its inclusion; it is defined using OWL for a Web environment. Moreover it includes light definitions of concepts and relations, adheres to the standards and is well documented.
E. DOLCE and SUMO evaluation Dolce and SUMO cover a broad semantic spectrum. They are extensible and they organize the superior levels of knowledge in a simple way. F. PROTON evaluation The PROTON ontology is also extensible and they structure the superior levels of knowledge in a simple way. PROTON uses OWL Lite as the definition language of concepts and relations. PROTON possesses a hierarchical, simple and modular structure with a broad semantic coverage partially taken from DOLCE. Although it does not cover specific domains, it does provide the structure that facilitates their inclusion. It is defined using OWL which enables its publication and integration with ontologies published on the Web. An evaluation of the semantic coverage of the resource was realized, in order to test the coverage of the concepts’ senses which were included in the selected schemas. In this evaluation Dublin Core’s elements were in fact matching those of PROTON. Similarly, a second evaluation with FOAF also presented a partial coverage. In both cases, the inclusion of the necessary semantic was viable, thanks to the extension possibilities that the ontology offers. Using importing mechanisms, an application ontology was elaborated which relates the extensions with the original reference ontology. This way the application ontology depends on the reference ontology, but maintains the original ontology as an independent element. The representation implicates the definition of an application ontology for each reference ontology
V.
CONCLUSIONS
Various ontologies have been evaluated according to a set of capacity and quality requirements in order to be reused in a metadata conceptual retrieval system. Broad coverage domain ontologies such as WordNet, WebKB-2 and OpenCyc presented various
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disadvantages in their reuse. Despite their prestige and popularity, a reuse analysis for a specific environment may present deficiencies concerning their update, compatibility to the web environment, interoperability with other ontologies, and complexity or performance. Not all high level ontologies present favorable conditions for reuse in any system. Ontologies such as SIOC, UMBEL, DOLCE and SUMO have presented disadvantages related to their update and compatibility with the web environment. Even though PROTON proved to be the most adequate to the requirements of the system, it is not exempt of various disadvantages such as the necessity for update. The main contribution of this study is the provision of guidelines towards the selection of ontologies to be reused, based on qualitative analysis and experience. Our proposal evaluates different referent ontologies in order to facilitate the alignment with the schemas improving conceptual retrieval. This approach enables retrieving different schemes with just one query thanks to the semantic links between their concepts, improving reuse and interoperability. The results of the evaluation conducted in this work proves to be useful in our effort to overcome the problems that exist in today's Semantic Web, namely the large number of local semantic resources and the fact that metadata vocabularies are scarcely formalized and agreed on. Thus, we strive towards meeting the objectives of the Semantic Web Proposal which is to promote a shared terminology and metadata vocabularies reaching a higher degree of consensus and facilitate semantic retrieval among different resources.
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