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From Knowledge Organization to Knowledge Representation and Back
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.
From Knowledge Representation to Knowledge Organization and Back
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.
Towards a Gateway for Knowledge Graph Schemas Collection, Analysis, and Embedding
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.
A semantics-driven methodology for high-quality image annotation
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.
Building Interoperable Electronic Health Records as Purpose-Driven Knowledge Graphs
When building a new application we are increasingly confronted with the need of reusing and integrating pre-existing knowledge.
Incremental Image Labeling via Iterative Refinement
Data quality is critical for multimedia tasks, while various types of systematic flaws are found in image benchmark datasets, as discussed in recent work.
Disentangling Domain Ontologies
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.
Aligning Visual and Lexical Semantics
We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics.
Popularity Driven Data Integration
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
A Diversity-Aware Domain Development Methodology
The development of domain ontological models, though being a mature research arena backed by well-established methodologies, still suffer from two key shortcomings.
LiveSchema: A Gateway Towards Learning on Knowledge Graph Schemas
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.
Representation Heterogeneity
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.
Visual Ground Truth Construction as Faceted Classification
Recent work in Machine Learning and Computer Vision has provided evidence of systematic design flaws in the development of major object recognition benchmark datasets.
GENOME: A GENeric methodology for Ontological Modelling of Epics
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.
Object Recognition as Classification via Visual Properties
We base our work on the teleosemantic modelling of concepts as abilities implementing the distinct functions of recognition and classification.
Towards Knowledge Organization Ecosystems
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.
Stratified Data Integration
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).
iTelos -- Purpose Driven Knowledge Graph Generation
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.
Classifying concepts via visual properties
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.
