The entity reconciliation (ER) problem aroused much interest as a research topic in today's
Big Data era, full of big and open heterogeneous data sources. This problem poses when …

Conceptual models are artifacts representing conceptualizations of particular domains.
Hence, multi-domain model catalogs serve as empirical sources of knowledge and insights …

Semantic Heterogeneity is the problem which arises when multiple resources present
differences in how they represent the same real world phenomenon. In KR, an early …

A common solution to the semantic heterogeneity problem is to perform knowledge graph
(KG) extension exploiting the information encoded in one or more candidate KGs, where the …

The present article aims to review and evaluate the practiced and classical techniques,
tools, models, and systems concerning automatic information extraction (IE) from published …

Topic models are widely used to thematically describe a collection of text documents and
have become an important technique for systems that measure document similarity for …

The paper focuses on two pivotal cognitive functions of both natural and AI agents, namely
classification and identification. Inspired from the theory of teleosemantics, itself based on …

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. In …

The mainstream approach to the development of ontologies is merging ontologies encoding
different information, where one of the major difficulties is that the heterogeneity motivates …

Knowledge graphs have emerged as a sophisticated advancement and refinement of
semantic networks, and their deployment is one of the critical methodologies in …