Analyzing and Ranking Multimedia Ontologies for their Reuse
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  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Mencionar de palabras las Guias Metodológicas NeOn Analyzing and Ranking Multimedia Ontologies for their Reuse
  • -Mmultimedia is everywhere, with examples. - Definition of MM - Because it is everywhere, it is important to retrieve them efficiently. (need of correct semantic) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • -Mmultimedia is everywhere, with examples. - Definition of MM - Because it is everywhere, it is important to retrieve them efficiently. (need of correct semantic) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Knowledge resources are (ontologies, non-ontological resources, and ontology design patterns) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Explain here that it is difficult with such descriptors to identify the objects behind this journalist: Trees, machine, sky, etc. Decir que el fichero Xml tiene otras informaciones: MediaURI, mediaType, SpatialDecomposition Analyzing and Ranking Multimedia Ontologies for their Reuse
  • “ An ontology is a formal, explicit specification of a shared conceptualization” Studer, Benjamins, Fensel. Knowledge Engineering: Principles and Methods. Data and Knowledge Engineering . 25 (1998) 161-197 Analyzing and Ranking Multimedia Ontologies for their Reuse
  • M3 es la ontologia que se está desarrollando dentro del proyecto Buscamedia Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Desarrollo de la red de ontologías según el paradigma de representación de conocimiento basado en Lógica Descriptiva para la formalización utilizando el lenguaje de implementación de ontologías OWL-DL para su implementación y utilizando para ello la herramienta NTK. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • It is the standard mostly used in the domain of multimedia MPEG 7 is Descriptors (Ds) <---------relationship---  Description Schemes (DSs) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Quick presentation of the SoA Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Quick presentation of the SoA- Elegir MSO: Multimedia Structure Ontology. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Elegir Visual Descriptor Ontology (VDO) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Quick presentation of the SoA- Elegir Music Ontology with the OR reused. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Quick presentation of the SoA- Elegir AEO (Athletic Events Onto) with NORs Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Punto 2: Objetivo de la ontología M3. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Aquí se trata de ver de forma detallada cómo se ha hecho la búsqueda de las ontologias en función de los requisitos que se propone alcanzar la onto. M3. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Summary of SWEs- Describe column titles clearly and what they are used for. Swoogle, Watson: ontology –oriented web engines SWSE, Sindice: Triple-oriented Web engines Falcons: Hybrid oriented web engine. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Show the files embedded as links if necessary Explicar CQs– CA Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Decir que es una de tus aportación en el campo—el flujo de trabajo detallado de la búsqueda Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Explicar que es un proceso iterativo de búsqueda con cada termino. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Tabla de las ontologias encontradas con Swoogle. Pero faltan algunas que están en la literatura (un buen número)—Necesidad de unificar los resultados. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Emphasis on that those ontologies that were not discovered by Swoogle where completed by others ontologies in the SoA (papers, W3c, projects, etc) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • 4- Those categories are subset of the terms in the "Questions" and "Answers" columns of the CQs document. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Explicar que la columna “FRC” sale del proceso anterior Se hace igual Tell something about the wrong situations—Why ? 26, total of “useful” ontologies: SoA—12 + SWE: 14 For the next stage, 23 ontologies: 26 -2 (Nokia) – one intersection (Media Ontology) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Adequacy of features and theoretical support: not an upper ontology Knowledge clash: not possible for the lack of comparison Adapatation to the reasoner: a priori constant to every ontology Necessity of bridge terms: absence of explicit constraint in the M3 ontology Analyzing and Ranking Multimedia Ontologies for their Reuse
  • 1- T hus they have very low reuse cost Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Explain the values of Music ontology and M30. Última tabla: Media Ontology Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Say something about DIG35, SAPO.. And the final selection of Music ontology Analyzing and Ranking Multimedia Ontologies for their Reuse
  • They cover 70% of the CQs..Good news for the developer. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Explain “multilinguality “ concept in ontology Analyzing and Ranking Multimedia Ontologies for their Reuse
  • 1-Competency Questions are used to query a given Semantic Web Engine. Such an analysis can improve the quality of the analysis of the candidate ontologies. 2-Reason: i t is a time consuming task and to reduce it in domain reuse step, create an API. Therefore, it will reduce the ontology selection process and also improve the quality of the results . Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analyzing and Ranking Multimedia Ontologies for their Reuse

Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse Presentation Transcript

  • ANALYZING AND RANKING MULTIMEDIA ONTOLOGIES FOR THEIR REUSE Date: 27/02/11 Speaker: Ghislain Auguste Atemezing Master Thesis Máster de investigación en inteligencia artificial Author: Ghislain Auguste Atemezing Supervisor: Dr. María del Carmen Suárez de Figueroa Baonza
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Introduction (I)
    • Multimedia content is ubiquitous (Web, TV news, Film, Phone, etc.), and store huge collection of data (Library, Museum, Archives, etc.)
    • Multimedia includes a combination of text, audio, still images, animation, video, and interactivity content forms [Chapman 09]
    • Many of these contents are available online
    Analyzing and Ranking Multimedia Ontologies for their Reuse Jenny Chapman and Nigel Chapman. Digital Multimedia . John Niley & Sons Ltd, 2009 .
  • Introduction (II)
    • Continuously consuming multimedia contents of different formats and from different sources in web environment (e.g., Google, Flickr, Picassa, Youtube).
    • How to efficiently retrieve multimedia objects for web developers and ordinary users ?
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Introduction (III)
    • Descriptors based on the automatic analysis of audiovisual content are far from what users require .
    • Need for correct semantic annotation and representation of multimedia content.
    • Recent research focus on the reduction of semantic and conceptual gap between user and machine. That is, based on the content of high-level descriptions.  Reusing KNOWLEDGE in ontology engineering.
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Introduction (IV)
    • Many standards to describe MM content: MPEG-4, MPEG7, IPTC, etc.
    • Standards provide descriptors schemas for low level description .
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Introduction (V)
    • “ Semantic gap”: mismatch between the information that can be extracted from audio-visual data and the interpretation that each user makes in a given situation for the same data [Smeulders 00].
    • Many initiatives in the last decade to bridge the gap : MPEG 7 transformations [Hunter 01, Celma 05]; COMM [Arndt07] by creating ontologies for multimedia.
    • Methodologies for ontology engineering: METHONDOLOGY, On-To-Knowledge, DILIGENT, and recently NeOn Methodology.
    Analyzing and Ranking Multimedia Ontologies for their Reuse A. Smeulders, M. Worring. Content-based image retrieval at the end of the early years. IEEE Trans. Pattern Anal. Mach. Intell ., 22:1349–1380, December 2000. Jane Hunter. Adding Multimedia to the Semantic Web - Building an MPEG-7 Ontology. In International Semantic Web Working Symposium (SWWS), Stanford, 2001. R. Arnd R. Troncy. COMM: Designing a Well-Founded Multimedia Ontology for the Web. In 6th International Semantic Web Conference ISWC2007, Busan, Korea. Springer, 2007.
  • Introduction (VI) Main objective : To search, find, analyze, rank and select suitable multimedia (MM) ontologies to be reused in the development of a multimedia ontology called M3 (Multimedia-Multidominio-Multilingüe) Goal 1 : To o btain a rank of MM ontologies to select the most appropriate ones that will be reused in the development of the M3 ontology. Goal 2 : To d escribe in detail and in a pedagogic way an example of how to apply the methodological guidelines for reusing ontologies in the multimedia domain. Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Introduction (VII)
    • Apply and extend Neon Methodology guidelines [Suárez-Figueroa , 2010] for reusing domain ontology in MM:
      • domain ontology search: look for candidate domain ontologies that could satisfy the needs of the M3 Ontology.
      • domain ontology assessment: find out if the set of candidate domain ontologies are useful for the development of the M3 Ontology.
      • domain ontology selection: find out which domain ontologies are the most suitable for the development of the M3 Ontology.
      • domain ontology integration: integrate the domain ontologies selected in the M3 Ontology.
    General Process Analyzing and Ranking Multimedia Ontologies for their Reuse M.C. Suárez-Figueroa. PhD Thesis: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse . España. Universidad Politécnica de Madrid. Junio 2010.
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
      • MPEG-7
      • Ontologies describing MM objects
      • Ontologies describing Shapes and Images
      • Ontologies describing Visual Resource Object
      • Ontologies describing Audio and Music
      • Application Ontologies
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • MPEG 7 Standard: “Multimedia Content Description” Analyzing and Ranking Multimedia Ontologies for their Reuse Descriptors Components Visual Features Color, Texture, Shape, Motion, Localization, Face recognition. Color Descriptors Color space, Color Quantization, Dominant Colors, Scalable Color, Color Layout, Color-Structure, GoF/GoP Color. Texture Descriptors Homogeneous Texture, Edge Histogram, Texture Browsing Shape Descriptors Region Shape, Contour Shape, Shape 3D Motion Descriptors Camera Motion, Motion Trajectory, Parametric Motion, Motion Activity Localization Descriptors Region locator, Spatio-temporal locator Audio Framework Basic (AudioWaveform, AudioPower), Basic Spectral, Timbral Temporal and Timbral Spectral
  • Ontologies for describing MM objects Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse
    • It is composed of multimedia patterns specializing the DOLCE design patterns for Descriptions & Situations and Information Objects, [Arndt et al., 07], OWL DL.
    • Scope covered: Multimedia, audio/music, image .
    • Ontological Resource reused: DOLCE, DnS, IO .
    • Non Ontological Resource reused: MPEG 7
    COMM
    • It is targeted for rich presentations in the web like SMIL, SVG and Flash.,[C. Scherp , A.Saathoff 10], OWL Full.
    • Scope covered: Multimedia, audio/music, image, video
    • Ontological Resource reused: DOLCE & DnS Ultralight (DUL) .
    • Non Ontological Resource reused: N/A
    M3O
    • It aims at integrating data resources related to media, especially
    • those used on the Web. W3C initiative, OWL.
    • Scope covered: Multimedia, audio/music, video .
    • Ontological Resource reused: N/A.
    • Non Ontological Resource reused: SKOS
    Media Onto
    • MPEG-7_Hunter, MPEG-7x , MPEG-7_Tsinakari, MPEG-7_Rhizomik . [2001- 2006]
    • SWintO (mobile access, 2007): Multimedia, image, video . (RDFS)
    • Ontological Resource reused: DOLCE, SUMO.
    • Non Ontological Resource reused: MPEG 7
    MPEG7 transformations + SWintO
  • Ontologies for describing Shapes and Image Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse
    • Ontology for describing metadata for digital images , [ Raphael Troncy + Ughent University, 07], OWL Full.
    • Scope covered: image .
    • Ontological Resource reused: N/A .
    • Non Ontological Resource reused: IIA
    DIG 35
    • It defines concepts including image, video, video frame, region, as well as relations such as depicts, regionOf, etc.,[ Halaschek-Wiener et. al., 2005 ], OWL Full.
    • Scope covered: video, image
    • Non Ontological Resource reused: N/A
    MIRO
    • It combines high-level domain concepts and low-level multimedia descriptions, enabling for new media content analysis . [Kosmas Petridis et al., 2007] aceMedia Project ,OWL.
    • Scope covered: Multimedia, video .
    • Ontological Resource reused: DOLCE, MPEG-7(MDS)
    MSO
    • Metadata for any kind of shape
    • Creation and processing digital shapes. AM@SHAPE , 2005
    • Image, visual . (OWL Full)
    • Ontological Resource reused: N/A.
    CSO, SAPO
  • Ontologies for describing Visual Resource Object Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse
    • VRA is an Asociation maintining collections of slides, images and works of art.
    • Two versions of the ontology: SIMILE project (2003,RDFS ) and Assem (2005,OWL)
    • Scope covered: image, visual
    • Ontological Resource reused: N/A .
    • Non Ontological Resource reused: VRA Element Set
    Vra Core 3
    • Deals with semantic MM content, analysis and reasoning.
    • Developed within aceMedia Project, 2005
    • Scope covered: video, image.
    • Ontological Resource reused: DOLCE extension .
    • Non Ontological Resource reused: MPEG-7
    VDO
  • Ontologies for describing Audio and Music Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse
    • Vocabulary for linking music-related information (production process, temporal aspect and events in music)
    • [Frederick Giasson, Yves Raimonf, 2010] in RFS
    • Scope covered: audio
    • Ontological Resource reused: Foaf, Time, Event, TimeLine
    • Non Ontological Resource reused: ABC Data Model.
    Music Onto
    • It describes classical music and performance.
    • Difference between musical works (e.g. Ballet) from performance (Ballet_Event), or works (Choral_Music)
    • [Kanzaki, 2005 ], OWL DL.
    • Scope covered: audio
    • Non Ontological Resource reused: N/A
    Kanzaki’s Music Vocab
    • It describes artists, music titles and some descriptors from the audio (tonality, rhythm, tempo )
    • [Oscar Celma, 2006] ,OWL DL
    • Scope covered: audio
    • Ontological Resource reused: FOAF
    • Non Ontological Resource reused: RDF Site Summary (RSS)
    Recommendation OntologyMusic
  • Application Ontologies
    • Aims at cross-link of media campaigns over media TV, press and Internet
    • [MediaCampaign, 2006] in RFS
    • Scope covered: audiovisual
    • Ontological Resource reused: PROTON
    • Non Ontological Resource reused: NewsML, News Codes
    MEPCO Analyzing and Ranking Multimedia Ontologies for their Reuse Analyzing and Ranking Multimedia Ontologies for their Reuse
    • It describes athletics events (e.g. jumping, running, etc.) held in European cities.
    • [Boemie, 2008 ], OWL DL.
    • Scope covered: Multimedia, visual
    • Ontological Resource reused: GIO
    • Non Ontological Resource reused: TeleAtlas DB, MPEG-7, IAAF
    AEO
    • It aims at providing virtual representations of humans
    • [AM@SHAPE, 2007] ,OWL Full
    • Scope covered: image, visual
    • Ontological Resource reused: CSO
    • Non Ontological Resource reused: RDF Site Summary (RSS)
    VHO
  • Conclusion SoA
    • None of existing ontology integrate both low level descriptions (e.g., color, textures, fragments, etc.) and high level descriptions (voice, videoclip, slides presentation, domain content, etc.) of MM resources in all its five aspects ( audio, video, image, visual, audiovisual, multimedia )
    • None of the existing ontology describes MM resources in different domains and in different natural languages .
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Semantic Web Engines (SWEs) Analyzing and Ranking Multimedia Ontologies for their Reuse Semantic Web Engines are applications for finding ontologies where queries are usually written as natural language keywords and results are ranked . RDF-based search engines Ontology-based search engines Hybrid-based search engine
  • Selection of the most appropriate SWE Analyzing and Ranking Multimedia Ontologies for their Reuse
    • The total number of documents retrieved (T) for a specific keyword search.
    • The number of OWL documents per each 10 documents (OWL).
    • A valoration of the retrieval results using the symbols (+) and (-) of the result. We set to (+) if there are more than 2 OWL files per page, and (-) otherwise.
    • Terms used: Image, Multimedia, Audio, Music Style, Format
    Set of criteria Swoogle
  • Searching ontologies based on requirements ANALYZING AND RANKING MULTIMEDIA ONTOLOGIES FOR THEIR REUSE ORSD Functional requirements Non Functional requirements
  • Tasks for searching MM ontologies (I) Analyzing and Ranking Multimedia Ontologies for their Reuse Terms translated into English Terms extracted from the ORSD
  • Tasks for searching MM ontologies (II) Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Tasks for searching MM ontologies (III) Analyzing and Ranking Multimedia Ontologies for their Reuse But there are missing ontologies from SoA!! 25 ontologies retrieved with Swoogle
  • Tables of candidate MM ontologies: Unification process Analyzing and Ranking Multimedia Ontologies for their Reuse List of 40 ontologies : SWE + SoA
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Analysis based on requirements (I) Analyzing and Ranking Multimedia Ontologies for their Reuse 1-The competency questions (CQs) and one ontology selected from the searching activity. The result is a set of CQs identifiers that cover the given ontology. 3-Open the ontology to analyze in the Neon Toolkit. Open also the document with the list of CQs.
  • Analysis based on requirements (II) Analyzing and Ranking Multimedia Ontologies for their Reuse 4- For each CQs, detect the relevant categories and create a list of "Relevant Categories" (RelevCat). Example : " What are Audio Format ", with the answer: " AVI, MP3 "; RelevCat={Format, Audio, AVI, MP3}. 5- The matching task consists of finding for each term of the relevant categories, its presence in the ontology as a class or an individual . Update (CQ identifier)
  • Assessment table/ ”useful” ontologies Analyzing and Ranking Multimedia Ontologies for their Reuse Heuristic IF ( SimilarScope) OR ( Similar Purpose) OR ( Functional RequirementsCovered ) = No Then NotUseful ( CandidateOntology ) EliminateFromSetCandidate (CandidateOntology) Some wrong situations [Suárez-Figueroa, 2010] 26 “useful” ontologies: 12: SoA 14: SWE
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
      • Criteria for selecting MM ontologies
    Analyzing and Ranking Multimedia Ontologies for their Reuse      [Suárez-Figueroa, 2010] M.C. Suárez-Figueroa. PhD Thesis: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse . Spain. Universidad Politécnica de Madrid. Junio 2010.
  • Determining the most appropriate MM ontologies. Considerations
    • Easy accessibility of the ontologies
    • Most of the ontologies were developed within a project or institutional initiatives (e.g: Boemie) ,  highest scores in the Quality of the documentation , availability of external knowledge , and code clarity . The rest are made by academic researchers.
    • Some ontologies were developed or transformed by one author  reputation and purpose reliability lower than others ontologies.
    • In ” practical support ”, very relevant others publications referencing the ontology or the use of the same ontology in a large project (e.g.: COMM, SAPO, Boemie VDO)
    • Difficult to know if the ontologies were tested and/or evaluated after their implementation
    Analyzing and Ranking Multimedia Ontologies for their Reuse
      • Determining the most appropriate MM ontologies (I)
    Analyzing and Ranking Multimedia Ontologies for their Reuse
      • Determining the most appropriate MM ontologies (II)
    Analyzing and Ranking Multimedia Ontologies for their Reuse Value = Unknown  Value T = 0 Value = Low  Value T = 1 Value = Medium  Value T = 2 Value = High  Value T = 3 Formulae to rank ontologies [Suárez-Figueroa, 2010]
      • Integrating the MM ontologies reused. General vision
    Analyzing and Ranking Multimedia Ontologies for their Reuse Music Ontology: 1 CQ covered Media Ontology: 4 CQs covered COMM: 5 CQs covered Boemie VDO: 4 CQs covered They cover 70% of the CQs!!
      • Integrating the MM ontologies reused. Overview of the M3 Ontology
    Analyzing and Ranking Multimedia Ontologies for their Reuse
  • Outline
    • Introduction
    • State of the Art on MultiMedia Ontologies
    • Searching MM Ontologies
    • Assessing MM Ontologies
    • Selecting MM Ontologies
    • Conclusions
    Analyzing and Ranking Multimedia Ontologies for their Reuse
      • We used the NeOn Methodology [Suárez-Figueroa, 2010] to perform a systematic analysis of all the candidate ontologies.
      • Methodological guidelines for reusing domain ontologies.
      • More specifically, we have been focused on:
        • (1) searching for ontological resources in repositories and registries  40 ontologies found.
        • (2) assessing the ontological resources in order to find out if such resources satisfy the developers needs  23 ontologies obtained.
        • (3) comparing the ontological resources on the basis of a set of criteria and selecting the most appropriate ones based on the requirements: improving and extending them with specific rules to analyze the ontologies  Ontologies ranked.
        • (4) integrating the ontological resources: selection of 4 suitable ontologies to be reused in the M3 Ontology.
    Conclusions What we have done in this master thesis Analyzing and Ranking Multimedia Ontologies for their Reuse M.C. Suárez-Figueroa. PhD Thesis: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse . Spain. Universidad Politécnica de Madrid. June 2010.
      • Searching activity:
        • An overview of MM ontologies in the literature.
        • How to select an appropriate SWE to retrieve relevant ontologies in the MM domain.
        • Workflow to search relevant ontologies based on set of terms extracted from the CQs.
      • Assessing activity:
        • A comparative framework for MM ontologies.
        • Workflow to check if an ontology fits the requirements.
      • Selecting activity:
        • Adaptation of the criteria for selection proposed by [Suárez-Figueroa, 2010] to the MM domain.
        • Inclusion of a new criteria.
        • Integration and implementation of the M3 Ontology.
    Conclusions (II) Main contributions Analyzing and Ranking Multimedia Ontologies for their Reuse
      • Many of the processes described in the ontology reuse activities are described in natural language  need to be formalized and automatized.
      • Searching activity is not exclusive to the used Semantic Search Engines , and must be extended to articles, project web pages, and W3C groups related to the domain.
      • Semantic Web Engines do not clearly distinguish in the results from keywords queries, RDF data coming from blogs and DBPedia resources to ontologies documents implemented in OWL.
      • Some criteria proposed in [Suárez-Figueroa, 2010] concerning ranking ontologies need to be adapted to the domain of the ontology being developed.
      • There is lack of multilingual ontologies in multimedia domain.
    Conclusions (III) Lessons learned Analyzing and Ranking Multimedia Ontologies for their Reuse M.C. Suárez-Figueroa. PhD tesis: NeOn Methodology for Building Ontology Networks: Specification, Scheduling and Reuse . Spain. Universidad Politécnica de Madrid. Junio 2010.
  • Future Work (I)
      • How to choose the right SWE that gives better results? What could be the criteria that guide deciding which SWE to use in function of the domain?
    CQs Enhancing Ontology Search Tasks :
      • Many SWE presents their results mixing documents from blogs with ontologies. An API can help the developer to extract efficiently disseminated ontologies in the whole documents retrieved by a SWE.
    How to select the right ontology from the results retrieved by search engines
      • With the continuously growing of the DBPedia resources, analyze how to populate ontologies and their reliability with respect to the one to be built.
    Semi-automatic ontology population: Analyzing and Ranking Multimedia Ontologies for their Reuse
  • End Thanks! Analyzing and Ranking Multimedia Ontologies for their Reuse
  • ANALYZING AND RANKING MULTIMEDIA ONTOLOGIES FOR THEIR REUSE Date: 27/02/11 Speaker: Ghislain Auguste Atemezing Master Thesis Máster de investigación en inteligencia artificial Author: Ghislain Auguste Atemezing Supervisor: Dr. María del Carmen Suárez de Figueroa Baonza