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1. Knowledge Graph-Empowered Materials Discovery

   https://doi.org/10.1109/BigData52589.2021.9671503  

   Zhao, Xintong; Greenberg, Jane; McClellan, Scott; Hu, Yong-Jie; Lopez, Steven; Saikin, Semion K.; Hu, Xiaohua; An, Yuan (December 2021, 2021 IEEE International Conference on Big Data (Big Data))

   In this position paper, we describe research on knowledge graph-empowered materials science prediction and discovery. The research consists of several key components including ontology mapping, materials data annotation, and information extraction from unstructured scholarly articles. We argue that although big data generated by simulations and experiments have motivated and accelerated the data-driven science, the distribution and heterogeneity of materials science-related big data hinders major advancements in the field. Knowledge graphs, as semantic hubs, integrate disparate data and provide a feasible solution to addressing this challenge. We design a knowledge-graph based approach for data discovery, extraction, and integration in materials science. 

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2. Enhancing Contextual Understanding in Knowledge Graphs: Integration of Quantum Natural Language Processing with Neo4j LLM Knowledge Graph

   https://doi.org/10.1109/bigdata62323.2024.10826002  

   Bharti, Suman; Lo, Dan Chia-Tien; Shi, Yong (December 2024, IEEE)

   Traditional Knowledge Graphs (KGs), such as Neo4j, face challenges in managing high-dimensional relationships and capturing semantic nuances due to their deterministic nature. Quantum Natural Language Processing (QNLP) introduces probabilistic reasoning into the KG context. This integration leverages quantum principles, such as superposition, which allows relationships to exist in multiple states simultaneously, and entanglement, where the state of one entity dynamically influences the state of another. This quantum-based probabilistic reasoning provides a richer, more flexible representation of connections, moving beyond binary relationships to model the nuances and variability of real-world interactions. Our research demonstrates that QNLP enhances Neo4j’s ability to analyze context-rich data, improving tasks like entity extraction nd knowledge inference. By modeling relationship states probabilistically, QNLP addresses limitations in traditional methods, providing nuanced insights and enabling more advanced, contextaware NLP applications. 

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3. Few-shot Knowledge Graph Relational Reasoning via Subgraph Adaptation

   https://doi.org/10.18653/v1/2024.naacl-long.183  

   Liu, Haochen; Wang, Song; Chen, Chen; Li, Jundong (June 2024, Association for Computational Linguistics)

   Few-shot Knowledge Graph (KG) Relational Reasoning aims to predict unseen triplets (i.e., query triplets) for rare relations in KGs, given only several triplets of these relations as references (i.e., support triplets). This task has gained significant traction due to the widespread use of knowledge graphs in various natural language processing applications. Previous approaches have utilized meta-training methods and manually constructed meta-relation sets to tackle this task. Recent efforts have focused on edge-mask-based methods, which exploit the structure of the contextualized graphs of target triplets (i.e., a subgraph containing relevant triplets in the KG). However, existing edge-mask-based methods have limitations in extracting insufficient information from KG and are highly influenced by spurious information in KG. To overcome these challenges, we propose SAFER (Subgraph Adaptation for Few-shot Relational Reasoning), a novel approach that effectively adapts the information in contextualized graphs to various subgraphs generated from support and query triplets to perform the prediction. Specifically, SAFER enables the extraction of more comprehensive information from support triplets while minimizing the impact of spurious information when predicting query triplets. Experimental results on three prevalent datasets demonstrate the superiority of our proposed framework SAFER. 

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4. Understanding ecological systems using knowledge graphs: an application to highly pathogenic avian influenza

   https://doi.org/10.1093/bioadv/vbaf016  

   Robertson, Hailey; Han, Barbara_A; Castellanos, Adrian_A; Rosado, David; Stott, Guppy; Zimmerman, Ryan; Drake, John_M; Graeden, Ellie; Mulder, ed., Nicola (February 2025, Bioinformatics Advances)

   Abstract MotivationEcological systems are complex. Representing heterogeneous knowledge about ecological systems is a pervasive challenge because data are generated from many subdisciplines, exist in disparate sources, and only capture a subset of interactions underpinning system dynamics. Knowledge graphs (KGs) have been successfully applied to organize heterogeneous data and to predict new linkages in complex systems. Though not previously applied broadly in ecology, KGs have much to offer in an era when system dynamics are responding to rapid changes across multiple scales. ResultsWe developed a KG to demonstrate the method’s utility for ecological problems focused on highly pathogenic avian influenza (HPAI), a highly transmissible virus with a broad host range, wide geographic distribution, and rapid evolution with pandemic potential. We describe the development of a graph to include data related to HPAI including pathogen–host associations, species distributions, and population demographics, using a semantic ontology that defines relationships within and between datasets. We use the graph to perform a set of proof-of-concept analyses validating the method and identifying patterns of HPAI ecology. We underscore the generalizable value of KGs to ecology including ability to reveal previously known relationships and testable hypotheses in support of a deeper mechanistic understanding of ecological systems. Availability and implementationThe data and code are available under the MIT License on GitHub at https://github.com/cghss-data-lab/uga-pipp. 

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5. Knowledge-infused Learning for Entity Prediction in Driving Scenes

   https://doi.org/10.3389/fdata.2021.759110  

   Wickramarachchi, Ruwan; Henson, Cory; Sheth, Amit (November 2021, Frontiers in Big Data)

   Scene understanding is a key technical challenge within the autonomous driving domain. It requires a deep semantic understanding of the entities and relations found within complex physical and social environments that is both accurate and complete. In practice, this can be accomplished by representing entities in a scene and their relations as a knowledge graph (KG). This scene knowledge graph may then be utilized for the task of entity prediction, leading to improved scene understanding. In this paper, we will define and formalize this problem as Knowledge-based Entity Prediction (KEP). KEP aims to improve scene understanding by predicting potentially unrecognized entities by leveraging heterogeneous, high-level semantic knowledge of driving scenes. An innovative neuro-symbolic solution for KEP is presented, based on knowledge-infused learning, which 1) introduces a dataset agnostic ontology to describe driving scenes, 2) uses an expressive, holistic representation of scenes with knowledge graphs, and 3) proposes an effective, non-standard mapping of the KEP problem to the problem of link prediction (LP) using knowledge-graph embeddings (KGE). Using real, complex and high-quality data from urban driving scenes, we demonstrate its effectiveness by showing that the missing entities may be predicted with high precision (0.87 Hits@1) while significantly outperforming the non-semantic/rule-based baselines. 

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