no code implementations • 22 Jan 2024 • Fausto Giunchiglia, Mayukh Bagchi, Subhashis Das
Knowledge Organization (KO) and Knowledge Representation (KR) have been the two mainstream methodologies of knowledge modelling in the Information Science community and the Artificial Intelligence community, respectively.
no code implementations • 12 Dec 2023 • Fausto Giunchiglia, Mayukh Bagchi
Knowledge Representation (KR) and facet-analytical Knowledge Organization (KO) have been the two most prominent methodologies of data and knowledge modelling in the Artificial Intelligence community and the Information Science community, respectively.
no code implementations • 21 Nov 2023 • Mattia Fumagalli, Marco Boffo, Daqian Shi, Mayukh Bagchi, Fausto Giunchiglia
One of the significant barriers to the training of statistical models on knowledge graphs is the difficulty that scientists have in finding the best input data to address their prediction goal.
no code implementations • 26 Jul 2023 • Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
Recent work in Machine Learning and Computer Vision has highlighted the presence of various types of systematic flaws inside ground truth object recognition benchmark datasets.
no code implementations • 10 May 2023 • Simone Bocca, Alessio Zamboni, Gabor Bella, Yamini Chandrashekar, Mayukh Bagchi, Gabriel Kuper, Paolo Bouquet, Fausto Giunchiglia
When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge.
no code implementations • 18 Apr 2023 • Fausto Giunchiglia, Xiaolei Diao, Mayukh Bagchi
Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work.
no code implementations • 21 Mar 2023 • Mayukh Bagchi, Subhashis Das
In this paper, we introduce and illustrate the novel phenomenon of Conceptual Entanglement which emerges due to the representational manifoldness immanent while incrementally modelling domain ontologies step-by-step across the following five levels: perception, labelling, semantic alignment, hierarchical modelling and intensional definition.
no code implementations • 13 Dec 2022 • Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics.
no code implementations • 28 Sep 2022 • Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli, Mayukh Bagchi, Alessio Zamboni
The intuition is that data will be treated differently based on their popularity: the more a certain set of data have been reused, the more they will be reused and the less they will be changed across reuses, thus decreasing the overall data preprocessing costs, while increasing backward compatibility and future sharing
no code implementations • 27 Aug 2022 • Mayukh Bagchi
The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings.
no code implementations • 13 Jul 2022 • Mattia Fumagalli, Marco Boffo, Daqian Shi, Mayukh Bagchi, Fausto Giunchiglia
In this paper, we describe the LiveSchema initiative, namely a gateway that offers a family of services to easily access, analyze, transform and exploit knowledge graph schemas, with the main goal of facilitating the reuse of these resources in machine learning use cases.
no code implementations • 3 Jul 2022 • Fausto Giunchiglia, Mayukh Bagchi
Semantic Heterogeneity is conventionally understood as the existence of variance in the representation of a target reality when modelled, by independent parties, in different databases, schemas and/ or data.
no code implementations • 17 Feb 2022 • Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets.
no code implementations • 13 Feb 2022 • Udaya Varadarajan, Mayukh Bagchi, Amit Tiwari, M. P. Satija
The paper presents, as a unified solution to the above shortcomings, the design and development of GENOME - the first dedicated methodology for iterative ontological modelling of epics, potentially extensible to works in different research arenas of digital humanities in general.
no code implementations • 20 Dec 2021 • Fausto Giunchiglia, Mayukh Bagchi
We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification.
no code implementations • 23 May 2021 • Mayukh Bagchi
It is needless to mention the (already established) overarching importance of knowledge organization and its tried-and-tested high-quality schemes in knowledge-based Artificial Intelligence (AI) systems.
no code implementations • 19 May 2021 • Fausto Giunchiglia, Alessio Zamboni, Mayukh Bagchi, Simone Bocca
We propose a novel approach to the problem of semantic heterogeneity where data are organized into a set of stratified and independent representation layers, namely: conceptual(where a set of unique alinguistic identifiers are connected inside a graph codifying their meaning), language(where sets of synonyms, possibly from multiple languages, annotate concepts), knowledge(in the form of a graph where nodes are entity types and links are properties), and data(in the form of a graph of entities populating the previous knowledge graph).
no code implementations • 19 May 2021 • Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli, Mayukh Bagchi, Alessio Zamboni
When building a new application we are more and more confronted with the need of reusing and integrating pre-existing knowledge, e. g., ontologies, schemas, data of any kind, from multiple sources.
no code implementations • 19 May 2021 • Fausto Giunchiglia, Mayukh Bagchi
We assume that substances in the world are represented by two types of concepts, namely substance concepts and classification concepts, the former instrumental to (visual) perception, the latter to (language based) classification.