Knowledge Graphs and Knowledge Networks: The Story in Brief

Sheth, Amit P.; Padhee, Swati; Gyrard, Amélie

doi:10.1109/mic.2019.2928449

Cited by 52 publications

(18 citation statements)

References 9 publications

(8 reference statements)

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“…Further, we introduce KG-driven unsupervised, semi-supervised, and supervised methods of representation learning over unstructured multimodal content. Forms of Knowledge-Infusion and its Applications: We describe three approaches to knowledge-infusion [10]: (a) Shallow Infusion: Both the external knowledge and the method of knowledge infusion is shallow, utilizing syntactic and lexical knowledge in the form of word embedding models. (b) Semi-Deep Infusion: External knowledge is involved through attention mechanisms or learnable knowledge constraints acting as a sentinel to guide model learning.…”

Section: Description Of the Tutorialmentioning

confidence: 99%

“…without using prior knowledge, especially in generic unstructured domains, where data is abundant (e.g., BERT, GPT-3). On the other hand, in problems concerning text mining that are dynamic and impact society at large, existing data-dependent, state-of-the-art deep learning methods remain vulnerable to veracity considerations, especially when high-volume masks small, emergent signals [10]. Statistical natural language processing (NLP) techniques have shown poor performance in capturing: (1) Human well being online especially in evolving events (e.g., mental health communications on Reddit [2,3]), (2) Culture and context-specific discussion on the web (e.g., sarcasm and humor detection, extremism on social media [6]), (3) Social Network Analysis during pandemic or disaster scenarios [7], and (4) Explainable methods of learning that drive technological innovations and inventions for social good [8,9,11].…”

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confidence: 99%

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Knowledge-infused Deep Learning

Kursuncu

Sheth

Wickramarachchi

et al. 2020

Proceedings of the 31st ACM Conference on Hypertext and Social Media

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Deep Learning has shown remarkable success during the last decade for essential tasks in computer vision and natural language processing. Yet, challenges remain in the development and deployment of artificial intelligence (AI) models in real-world cases, such as dependence on extensive data and trust, explainability, traceability, and interactivity. These challenges are amplified in high-risk fields, including healthcare, cyber threats, crisis response, autonomous driving, and future manufacturing. On the other hand, symbolic computing with knowledge graphs has shown significant growth in specific tasks with reliable performance. This tutorial (a) discusses the novel paradigm of knowledge-infused deep learning to synthesize neural computing with symbolic computing (b) describes different forms of knowledge and infusion methods in deep learning, and (c) discusses application-specific evaluation methods to assure explainability and reasoning using benchmark datasets and knowledge-resources. The resulting paradigm of "knowledgeinfused learning" combines knowledge from both domain expertise and physical models. A wide variety of techniques involving shallow, semi-deep, and deep infusion will be discussed along with the corresponding intuitions, limitations, use cases, and applications. More details can be found http://kidl2020.aiisc.ai/.

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Section: Description Of the Tutorialmentioning

confidence: 99%

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confidence: 99%

Knowledge-infused Deep Learning

Kursuncu

Sheth

Wickramarachchi

et al. 2020

Proceedings of the 31st ACM Conference on Hypertext and Social Media

Self Cite

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“…A knowledge graph provides a structured representation for knowledge that is accessible to both humans and machines 36 . Knowledge graphs have been used successfully in variety of problems arising in information processing domains such as search, recommendation, summarisation 37 . Sometimes the formal semantics of knowledge graphs such as domain ontologies are used as sources for external domain-knowledge 38 .…”

Section: Introductionmentioning

confidence: 99%

A review of some techniques for inclusion of domain-knowledge into deep neural networks

Dash¹,

Chitlangia²,

Ahuja³

et al. 2022

Sci Rep

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We present a survey of ways in which existing scientific knowledge are included when constructing models with neural networks. The inclusion of domain-knowledge is of special interest not just to constructing scientific assistants, but also, many other areas that involve understanding data using human-machine collaboration. In many such instances, machine-based model construction may benefit significantly from being provided with human-knowledge of the domain encoded in a sufficiently precise form. This paper examines the inclusion of domain-knowledge by means of changes to: the input, the loss-function, and the architecture of deep networks. The categorisation is for ease of exposition: in practice we expect a combination of such changes will be employed. In each category, we describe techniques that have been shown to yield significant changes in the performance of deep neural networks.

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“…KGs are contributing to various Artificial Intelligence (AI) applications including link prediction, node classification, and both recommendation and question answering systems (Ali et al,n.d. ;Sheth et al, 2019). KGs model heterogeneous knowledge domains by integrating information into advanced unified data schemas (i.e.…”

Section: Introductionmentioning

confidence: 99%

KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

Hassani‐Pak

Singh

Brandizi

et al. 2021

Plant Biotechnology Journal

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The generation of new ideas and scientific hypotheses is often the result of extensive literature and database searches, but, with the growing wealth of public and private knowledge, the process of searching diverse and interconnected data to generate new insights into genes, gene networks, traits and diseases is becoming both more complex and more time-consuming. To guide this technically challenging data integration task and to make gene discovery and hypotheses generation easier for researchers, we have developed a comprehensive software package called KnetMiner which is open-source and containerized for easy use. KnetMiner is an integrated, intelligent, interactive gene and gene network discovery platform that supports scientists explore and understand the biological stories of complex traits and diseases across species. It features fast algorithms for generating rich interactive gene networks and prioritizing candidate genes based on knowledge mining approaches. KnetMiner is used in many plant science institutions and has been adopted by several plant breeding organizations to accelerate gene discovery. The software is generic and customizable and can therefore be readily applied to new species and data types; for example, it has been applied to pest insects and fungal pathogens; and most recently repurposed to support COVID-19 research. Here, we give an overview of the main approaches behind KnetMiner and we report plant-centric case studies for identifying genes, gene networks and trait relationships in Triticum aestivum (bread wheat), as well as, an evidence-based approach to rank candidate genes under a large Arabidopsis thaliana QTL.

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Knowledge Graphs and Knowledge Networks: The Story in Brief

Cited by 52 publications

References 9 publications

Knowledge-infused Deep Learning

Knowledge-infused Deep Learning

A review of some techniques for inclusion of domain-knowledge into deep neural networks

KnetMiner: a comprehensive approach for supporting evidence‐based gene discovery and complex trait analysis across species

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