5 citations · 27 across the 22 of their papers we have counts for
22 papers
What can Computer Vision learn from Ranganathan?
Mayukh Bagchi, Fausto Giunchiglia
The Semantic Gap Problem (SGP) in Computer Vision (CV) arises from the misalignment between visual and lexical semantics leading to flawed CV dataset design and CV benchmarks. This…
Language and Knowledge Representation: A Stratified Approach
Mayukh Bagchi
The thesis proposes the problem of representation heterogeneity to emphasize the fact that heterogeneity is an intrinsic property of any representation, wherein, different observer…
From Knowledge Organization to Knowledge Representation and Back
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 Artific…
From Knowledge Representation to Knowledge Organization and Back
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 Inte…
Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and Embedding
Mattia Fumagalli, Marco Boffo, Daqian Shi +2
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 pre…
A semantics-driven methodology for high-quality image annotation
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. Our…
Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs
Simone Bocca, Alessio Zamboni, Gabor Bella +5
When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge. Nevertheless, it is a fact that this prior knowledge…
Incremental Image Labeling via Iterative Refinement
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. In particular, the existe…
Disentangling Domain Ontologies
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 model…
Aligning Visual and Lexical Semantics
Fausto Giunchiglia, Mayukh Bagchi, Xiaolei Diao
We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when…
Popularity Driven Data Integration
Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli +2
More and more, with the growing focus on large scale analytics, we are confronted with the need of integrating data from multiple sources. The problem is that these data are imposs…
A Diversity-Aware Domain Development Methodology
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. Firstly, the is…
LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas
Mattia Fumagalli, Marco Boffo, Daqian Shi +2
One of the major barriers to the training of algorithms on knowledge graph schemas, such as vocabularies or ontologies, is the difficulty that scientists have in finding the best i…
Representation Heterogeneity
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 datab…
Visual Ground Truth Construction as Faceted Classification
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. One such exa…
Smart Cities, Smart Libraries and Smart Knowledge Managers: Ushering in the neo-Knowledge Society
Mayukh Bagchi
The emergence of smart cities as a specific concept is not very old. In simple terms, it refers to cities which are sustainable and driven predominantly by their Information and Co…
GENOME: A GENeric methodology for Ontological Modelling of Epics
Udaya Varadarajan, Mayukh Bagchi, Amit Tiwari +1
Ontological knowledge modelling of epics, though being an established research arena backed by concrete multilingual and multicultural works, still suffer from two key shortcomings…
Object Recognition as Classification via Visual Properties
Fausto Giunchiglia, Mayukh Bagchi
We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification. Accordingly, we model two types of c…
Towards Knowledge Organization Ecosystems
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 Int…
Stratified Data Integration
Fausto Giunchiglia, Alessio Zamboni, Mayukh Bagchi +1
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(whe…
Classifying concepts via visual properties
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) percepti…
iTelos -- Purpose Driven Knowledge Graph Generation
Fausto Giunchiglia, Simone Bocca, Mattia Fumagalli +2
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 m…