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Conceptual and content-based annotation of (multimedia) documents

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Abstract

This paper focuses on the techniques used in an NKRL environment (NKRL = Narrative Knowledge Representation Language) to deal with a general problem affecting the so-called “semantic/conceptual annotations” techniques. These last, mainly ontology-based, aim at “annotating” multimedia documents by representing, in some way, the “inner meaning/deep content” of these documents. For documents of sufficient size, the content modeling operations are separately executed on ‘significant fragments’ of the documents, e.g., “sentences” for natural language texts or “segments” (minimal units for story advancement) in a video context. The general problem above concerns then the possibility of collecting all the partial conceptual representations into a global one. This integration operation must, moreover, be carried out in such a way that the meaning of the full document could go beyond the simple addition of the ‘meanings’ conveyed by the single fragments. In this context, NKRL makes use of second order knowledge representation structures, “completive construction” and “binding occurrences”, for collecting within the conceptual annotation of a whole “narrative” the basic building blocks corresponding to the representation of its composing elementary events. These solutions, of a quite general nature, are discussed in some depth in this paper. This last includes also a short “state of the art” in the annotation domain and some comparisons with the different methodologies proposed in the past for solving the above ‘integration’ problem.

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Notes

  1. In McCarthy’s theory, the main formulas are sentences of the form , which are to be taken as assertions that the proposition is true in (ist) the context , itself asserted in an outer context . A well-known concrete implementation of McCarthy’s theory is represented by Guha’s “microtheories”, largely used in a CYC framework [55]; a recent re-interpretation of McCarthy’s ideas in Description Logics terms is [48].

  2. Work in a Dublin Core’s context is now mainly focused on the definition and use of the so-called “Dublin Core Application Profiles” (DCAP) [16]. A DCAP is a document (or set of documents) that describes the metadata used in a particular application. More precisely, a “DCAP declaration” identifies the specific DCMI terms (and/or the terms derived from other, more specialized, vocabularies) that are utilized by an organization, an information provider, or a user community in their metadata. A DCAP explains also how those terms have been customized or adapted to be used within the application.

  3. An implementation tool largely used for the set up of ontologically-oriented systems is Protégé, both in its standard frame version [71] and in the W3C congruent OWL version [50]. This last is endowed with an OWL plugin that allows the users to load and save OWL and RDF ontologies, edit and visualize OWL classes and their properties and to make use of reasoners such as the description logics classifiers (see, e.g., [84]).

  4. For completeness’ sake, we should also mention here the annotation, indexing and retrieval techniques for still images and videos that are sometimes denoted as “semantic-“or “content-based” techniques see, e.g., CBIR = Content-Based Image Retrieval. They are based on the use of some primary features like color structure, shape properties, textures etc. corresponding to MPEG-7 (low-level) Visual Descriptors and are computed using various image analysis algorithms. Substantial progresses have been accomplished these last years in the CBIR domain; see [57] for example. However, two important caveats must still be taken into account. According to the first, we can note that, at least from a description of the content point of view, these “primary features” are not very useful by themselves. The well-known remark: “… color distribution feature values of an image for red, black, and yellow still do not allow the conclusion that the image shows a sunset” [12] can then be considered as at least partially valid. Secondly, the so-called “semantic gap” between the results of the low-level signal processing and the corresponding explicit concepts needed for executing ‘intelligent’ querying/inference operations at the semantic level is far from being definitely bridged. A vast literature exists about the semantic gap problem and the possible solutions see, e.g., [29, 92] for recent work in this context.

  5. More in general, Computational Linguistics techniques can be utilized to produce, in a semi-automatic way, full NKRL-like formal representations from NL texts. The procedures used to implement this NL/NKRL “translation’ all derive from those developed in the eighties in the framework of RESEDA (in French, Reseau Sémantique Documentaire), an NKRL’s ancestor, see [96]. A recent prototype in this style, created in the framework of an “assisted living” application, in described in [5].

  6. In NKRL, the instances of concepts, i.e., the “individuals”, are denoted conventionally in upper case characters. Moreover, the “conceptual labels” of both concepts and individuals must include at least an underscore, see or , to emphasize that they are not surface NL terms but pertain on the contrary to the “deep level” (conceptual) domain.

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  1. LaLIC/STIH Laboratory, Sorbonne University, Maison de la Recherche, 28 rue Serpente, 75006, Paris, France

    Gian Piero Zarri

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Zarri, G.P. Conceptual and content-based annotation of (multimedia) documents. Multimed Tools Appl 72, 2359–2391 (2014). https://doi.org/10.1007/s11042-013-1463-3

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