Notice bibliographique
Résumé
To survive in the 21st century, enterprises need to collaborate. Collaboration at the enterprise-level presupposes the interoperability of the underlying information systems. Access to heterogeneous information sources must be provided transparently while maintaining their autonomy. Further, the availability of nearly unlimited information calls for efficient and precise information retrieval, which can be achieved by making the semantics embedded in information sources explicit. Solving the semantic interoperability problem becomes imperative to the success of information search and retrieval applications and enterprises that rely on them. Inspired by self-organizing systems found in biology, physics, and computing, the approach of emergent semantics has been proposed as a solution to the semantic interoperability problem. Emergent semantics refers to the bottom-up construction of interoperable systems, in which semantically related peers are discovered and linked together during the normal operation of the system. Individual information source providers will provide mappings (so-called semantic bridges) between their own local and semantically related foreign information sources. Emergent Semantics in a peer-to-peer (P2P) network is the lowest common knowledge, semantically relevant concepts, among all the peers of the network. Local mappings between peers with different knowledge representations, and their correctness, are prerequisites for the creation of emergent semantics. Yet, approaches to emergent semantics often fail to distinguish between permanent and transient mapping faults. This may result in erroneously labeling peers as having incompatible knowledge representations. In turn, this can further prevent such peers from interacting with other semantically related peers . This is because, in emergent semantics, peers use past interactions to determine which peers they will interact with in future collaborations. This chapter will explore the issue of semantic mapping faults. This issue has not received enough attention in the literature. Specifically, it will focus on the effect of non-permanent semantic mapping faults on both inclusiveness of semantic emergence and robustness of applications and systems that use semantic mappings. A fault-tolerant emergent semantics algorithm with the ability to resist transient semantic mapping faults is also provided. The contributions of this chapter are: (a) an analysis of the impact of the semantic mapping faults on the inclusiveness of semantic knowledge sharing in P2P systems, (b) a preliminary solution to the problems created by semantic mapping faults in P2P semantic knowledge sharing systems, and (c) a qualitative analysis of the causal links between fault causes and fault types. The rest of this chapter is organized as follows. Section II provides broad discussion and literature review about semantic interoperability problem among heterogeneous information source. Section III defines what we mean by a semantic mapping fault and the types of faults. Section IV lists sources of semantic mapping faults. Section V classifies temporal semantic mapping faults. Section VI describes the emergent semantics approach. Section VII presents an algorithm to eliminate the harmful effects of transient mapping faults on emergent semantics (fault-tolerant emergent semantics). Section VIII concludes the chapter and Section IX identifies directions for future work.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,008 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,002 | 0,003 |
| Communication savante | 0,003 | 0,006 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».