1,431 research outputs found
Neurocognitive Informatics Manifesto.
Informatics studies all aspects of the structure of natural and artificial information systems. Theoretical and abstract approaches to information have made great advances, but human information processing is still unmatched in many areas, including information management, representation and understanding. Neurocognitive informatics is a new, emerging field that should help to improve the matching of artificial and natural systems, and inspire better computational algorithms to solve problems that are still beyond the reach of machines. In this position paper examples of neurocognitive inspirations and promising directions in this area are given
Measuring concept similarities in multimedia ontologies: analysis and evaluations
The recent development of large-scale multimedia concept ontologies has provided a new momentum for research in the semantic analysis of multimedia repositories. Different methods for generic concept detection have been extensively studied, but the question of how to exploit the structure of a multimedia ontology and existing inter-concept relations has not received similar attention. In this paper, we present a clustering-based method for modeling semantic concepts on low-level feature spaces and study the evaluation of the quality of such models with entropy-based methods. We cover a variety of methods for assessing the similarity of different concepts in a multimedia ontology. We study three ontologies and apply the proposed techniques in experiments involving the visual and semantic similarities, manual annotation of video, and concept detection. The results show that modeling inter-concept relations can provide a promising resource for many different application areas in semantic multimedia processing
Learning New Facts From Knowledge Bases With Neural Tensor Networks and Semantic Word Vectors
Knowledge bases provide applications with the benefit of easily accessible,
systematic relational knowledge but often suffer in practice from their
incompleteness and lack of knowledge of new entities and relations. Much work
has focused on building or extending them by finding patterns in large
unannotated text corpora. In contrast, here we mainly aim to complete a
knowledge base by predicting additional true relationships between entities,
based on generalizations that can be discerned in the given knowledgebase. We
introduce a neural tensor network (NTN) model which predicts new relationship
entries that can be added to the database. This model can be improved by
initializing entity representations with word vectors learned in an
unsupervised fashion from text, and when doing this, existing relations can
even be queried for entities that were not present in the database. Our model
generalizes and outperforms existing models for this problem, and can classify
unseen relationships in WordNet with an accuracy of 75.8%
Natural Language Processing in-and-for Design Research
We review the scholarly contributions that utilise Natural Language
Processing (NLP) methods to support the design process. Using a heuristic
approach, we collected 223 articles published in 32 journals and within the
period 1991-present. We present state-of-the-art NLP in-and-for design research
by reviewing these articles according to the type of natural language text
sources: internal reports, design concepts, discourse transcripts, technical
publications, consumer opinions, and others. Upon summarizing and identifying
the gaps in these contributions, we utilise an existing design innovation
framework to identify the applications that are currently being supported by
NLP. We then propose a few methodological and theoretical directions for future
NLP in-and-for design research
Automatic taxonomy evaluation
This thesis would not be made possible without the generous support of IATA.Les taxonomies sont une représentation essentielle des connaissances, jouant un rôle central dans de nombreuses applications riches en connaissances. Malgré cela, leur construction est laborieuse que ce soit manuellement ou automatiquement, et l'évaluation quantitative de taxonomies est un sujet négligé. Lorsque les chercheurs se concentrent sur la construction d'une taxonomie à partir de grands corpus non structurés, l'évaluation est faite souvent manuellement, ce qui implique des biais et se traduit souvent par une reproductibilité limitée. Les entreprises qui souhaitent améliorer leur taxonomie manquent souvent d'étalon ou de référence, une sorte de taxonomie bien optimisée pouvant service de référence.
Par conséquent, des connaissances et des efforts spécialisés sont nécessaires pour évaluer une taxonomie.
Dans ce travail, nous soutenons que l'évaluation d'une taxonomie effectuée automatiquement et de manière reproductible est aussi importante que la génération automatique de telles taxonomies. Nous proposons deux nouvelles méthodes d'évaluation qui produisent des scores moins biaisés: un modèle de classification de la taxonomie extraite d'un corpus étiqueté, et un modèle de langue non supervisé qui sert de source de connaissances pour évaluer les relations hyperonymiques. Nous constatons que nos substituts d'évaluation corrèlent avec les jugements humains et que les modèles de langue pourraient imiter les experts humains dans les tâches riches en connaissances.Taxonomies are an essential knowledge representation and play an important role in classification and numerous knowledge-rich applications, yet quantitative taxonomy evaluation remains to be overlooked and left much to be desired. While studies focus on automatic taxonomy construction (ATC) for extracting meaningful structures and semantics from large corpora, their evaluation is usually manual and subject to bias and low reproducibility. Companies wishing to improve their domain-focused taxonomies also suffer from lacking ground-truths. In fact, manual taxonomy evaluation requires substantial labour and expert knowledge.
As a result, we argue in this thesis that automatic taxonomy evaluation (ATE) is just as important as taxonomy construction. We propose two novel taxonomy evaluation methods for automatic taxonomy scoring, leveraging supervised classification for labelled corpora and unsupervised language modelling as a knowledge source for unlabelled data. We show that our evaluation proxies can exert similar effects and correlate well with human judgments and that language models can imitate human experts on knowledge-rich tasks
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
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