Signifying ontology complexity for knowledge sharing

Ontologies are used in widespread application areas particularly to provide a shared semantically domain knowledge in a declarative formalism for intelligent reasoning. Even ontology enables knowledge sharing however complexity of knowledge being conceptualized in the ontology is critical to the success of knowledge sharing efforts. Other factor like trust in the source of knowledge can also affect knowledge transfer. In this paper we propose metrics to measure the complexity of ontology for knowledge sharing. We have chosen Software Engineering Ontology as our case study

Ontology-Based Information Integration and Decision Making in Prefabricated Construction Component Supply Chain

With rapid developments in cloud computing, the creation of a cloud based prefabricated component supply chain platform marks an important initiative signifying an industry breakthrough and innovation. The application of this cloud platform will effectively integrate the social resources of the prefabricated components supply chain and realize the reconfiguration of distributed resources. In order to facilitate this, much research is needed to develop a flexible prefabricated component data integration model using ontologies and semantics. Such a model can support adaptive heterogeneous system integration and interaction based on dynamic process optimization. In addition, this cloud platform can be used to support coordination within the supply chain using ontology rules. This can greatly enhance managerial decision support. This paper proposes a flexible distributed information integration mechanism and develops an ontology-based management support application, which will play an important role in resource integration and the optimal allocation of prefabricated components within the supply chain

The Post‐Modern Transcendental of Language in Science and Philosophy

In this chapter I discuss the deep mutations occurring today in our society and in our culture, the natural and mathematical sciences included, from the standpoint of the “transcendental of language”, and of the primacy of language over knowledge. That is, from the standpoint of the “completion of the linguistic turn” in the foundations of logic and mathematics using Peirce’s algebra of relations. This evolved during the last century till the development of the Category Theory as universal language for mathematics, in many senses wider than set theory. Therefore, starting from the fundamental M. Stone’s representation theorem for Boolean algebras, computer scientists developed a coalgebraic first-order semantics defined on Stone’s spaces, for Boolean algebras, till arriving to the definition of a non-Turing paradigm of coalgebraic universality in computation. Independently, theoretical physicists developed a coalgebraic modelling of dissipative quantum systems in quantum field theory, interpreted as a thermo-field dynamics. The deep connection between these two coalgebraic constructions is the fact that the topologies of Stone spaces in computer science are the same of the C*-algebras of quantum physics. This allows the development of a new class of quantum computers based on coalgebras. This suggests also an intriguing explanation of why one of the most successful experimental applications of this coalgebraic modelling of dissipative quantum systems is just in cognitive neuroscience

Topological network alignment uncovers biological function and phylogeny

Sequence comparison and alignment has had an enormous impact on our understanding of evolution, biology, and disease. Comparison and alignment of biological networks will likely have a similar impact. Existing network alignments use information external to the networks, such as sequence, because no good algorithm for purely topological alignment has yet been devised. In this paper, we present a novel algorithm based solely on network topology, that can be used to align any two networks. We apply it to biological networks to produce by far the most complete topological alignments of biological networks to date. We demonstrate that both species phylogeny and detailed biological function of individual proteins can be extracted from our alignments. Topology-based alignments have the potential to provide a completely new, independent source of phylogenetic information. Our alignment of the protein-protein interaction networks of two very different species--yeast and human--indicate that even distant species share a surprising amount of network topology with each other, suggesting broad similarities in internal cellular wiring across all life on Earth.Comment: Algorithm explained in more details. Additional analysis adde

Dance, Music and Dramaturgy: collaboration plan and dramaturgical apparatus

Dance, Music and Dramaturgy: collaboration plan and dramaturgical apparatus – The unfolding of the concept of dramaturgy and the problematics of contemporary choreography are, today, a vast and diverse field of research, bearing numerous disclosures that lead to their reciprocal implication. Apart from that, dance and music share significant complementary ties allowing for the consideration of a common compositional inquiry. Reflecting on the compositional processes of dance and music, this article cross-examines the collaboration between choreographers and composers, integrating the incidence of dramaturgy in the strategies of choreographic and musical composition

Artist Praxis: Studio as a Premise of Knowledge

The goal of this paper is to talk about the idea of art making as a studio art production process and to describe how studio praxis can be a fundamental ground for generating new knowledge in which an artist innovates, discovers, introduces, rejects, chooses, compromises, transforms, and analyses within the premise of making. The studio is defined as an ontological premise (what the studio is) and a methodological procedure (how things are made in the studio). Studio art production shows the studio as a place where theory and practice, thinking and making, meet in making new knowledge. Keywords: Artist Praxis, Art Making, Investigation, Knowledge Generation, Methodology, Ontology, Studio Process, eISSN: 2398-4287 © 2022. The Authors. Published for AMER ABRA CE-Bs by e-International Publishing House, Ltd., UK. This is an open-access article under the CC BY-CC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer–review under responsibility of AMER (Association of Malaysian Environment-Behaviour Researchers), ABRA (Association of Behavioural Researchers on Asians) and cE-Bs (Centre for Environment-Behaviour Studies), Faculty of Architecture, Planning & Surveying, Universiti Teknologi MARA, Malaysia. DO

An Integrated Network Representation Of Multiple Cancer-Specific Data For Graph-Based Machine Learning

Genomic profiles of cancer cells provide valuable information on genetic alterations in cancer. Several recent studies employed these data to predict the response of cancer cell lines to drug treatment. Nonetheless, due to the multifactorial phenotypes and intricate mechanisms of cancer, the accurate prediction of the effect of pharmacotherapy on a specific cell line based on the genetic information alone is problematic. Emphasizing on the system-level complexity of cancer, we devised a procedure to integrate multiple heterogeneous data, including biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. In order to construct compact, yet information-rich cancer-specific networks, we developed a novel graph reduction algorithm. Driven by not only the topological information, but also the biological knowledge, the graph reduction increases the feature-only entropy while preserving the valuable graph-feature information. Subsequent comparative benchmarking simulations employing a tissue level cross-validation protocol demonstrate that the accuracy of a graph-based predictor of the drug efficacy is 0.68, which is notably higher than those measured for more traditional, matrix-based techniques on the same data. Overall, the non-Euclidean representation of the cancer-specific data improves the performance of machine learning to predict the response of cancer to pharmacotherapy. The generated data are freely available to the academic community at https:/osf.io/dzx7b/