AIISC Knowledge Graph

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Description

In recent years, knowledge graphs (KGs) have been increasingly used by both academia and industry to incorporate semantics into various intelligent applications. However, the creation of these knowledge graphs are mainly done manually with the help of domain experts and/or by using structured knowledge sources such as Wikipedia. Kno.e.sis Knowledge Graph team works on different aspects to improve creation and consumption of knowledge graphs as given below:

  • Contextualized knowledge graphs
  • Bootstrap domain-specific knowledge graphs by leveraging existing knowledge sources
  • Summarization of the knowledge graphs
  • Leveraging knowledge graphs to improve NLP applications
  • Dynamically evolve knowledge graphs for real-time events such as twitter campaigns
  • Question answering on knowledge graphs
  • Ontology quality and best practices
  • Ontology methodology to reuse ontologies
  • Ontology alignment
  • Knowledge extraction from ontologies to reuse the domain knowledge already designed in previous domains.
  • Semantic interoperability with a focus on ontologies

Knowledge Graphs (KG) for Information Processing

Knowledge Extraction

Knowledge Extraction from Ontologies

Automatic Knowledge Extraction to build Semantic Web of Things Applications

  • Mahda Noura, Amelie Gyrard, Sebastian Heil, Martin Gaedke.
  • IEEE Internet of Things (IoT) Journal 2019.
  • Impact factor: 5.863 in 2019

Concept Extraction from Web of Things Knowledge Bases

  • Mahda Noura, Amelie Gyrard, Sebastian Heil, and Martin Gaedke.
  • International Conference WWW/Internet, 2018
  • Outstanding Paper Award

Knowledge Extraction for the Web of Things (KE4WoT) Challenge

  • Co-located with International World Wide Web Conference (WWW) 2018
  • Amelie Gyrard, Mihaela Juganaru-Mathieu, Manas Gaur, Swati Padhee, Amit Sheth

Knowledge Graphs Summarization

Contextualized Knowledge Graphs

Vinh Nguyen PhD Slides:

Vinh Nguyen PhD Video:

Knowledge Graphs for Contextualization

  • Biomedical Vocabulary Alignment at Scale in the UMLS Metathesaurus The Unified Medical Language System (UMLS) 1 is a rich repository of biomedical vocabularies developed by the US National Library of Medicine. It is an effort to overcome challenges to the effective retrieval of machine-readable information. The enormous knowledge accumulated over 30 years of manual curation coupled with the advent of statistical and symbolic AI research 2 open up opportunities to improve the UMLS Metathesaurus construction and maintenance process. Specifically by developing novel approaches to aid and complement the efforts of the UMLS human editors in the insertion and updates of new biomedical vocabularies in the existing UMLS Metathesaurus for future releases.

Knowledge Graphs for Personalization

  • Personalized Health Knowledge Graph Our current health applications do not adequately take into account contextual and personalized knowledge about patients. In order to design "Personalized Coach for Healthcare" applications to manage chronic diseases, there is a need to create a Personalized Healthcare Knowledge Graph (PHKG) that takes into consideration a patient's health condition (personalized knowledge) and enriches that with contextualized knowledge from environmental sensors and Web of Data (e.g., symptoms and treatments for diseases).

Knowledge Graphs for Autonomous Driving

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

Knowledge graph for Drug Abuse

  • Knowledge graph for Drug AbuseDAO provides a powerful framework and a useful resource that can be expanded and adapted to a wide range of substance use and mental health domains to help advance big data analytics of web-based data for substance use epidemiology research.

Knowledge Graphs (KG) Applications

EMPWR

The AIISC EMPWR: Knowledge Graph Development Platform effort involves the development of a comprehensive tool and platform for KG development with the following aims

1. Develop a KG development platform capable of instantiating KGs in any domains from structured, semi-structured, and unstructured data.

2. Improve & address the limitations of existing KG platforms

3. Constructs a Knowledge Graph (based on a combination of)

    • Enrich an existing Knowledge Graph (Top-down declarative)
    • Construct a Knowledge Graph out of given entities (Bottom-up data driven)

Scooner

Scooner Scooner is a knowledge-based literature search and exploration system where recently published results are computationally extracted and used a background KB to guide the search process. The key here is that the knowledge base that guides the search is extracted from the same universe of literature that is being explored.

Human Performance and Cognition Ontology

HPCO The human performance and cognition ontology (HPCO) project aims to achieve the following two major objectives

  1. Build a knowledge base using semi-automatic domain hierarchy construction and relationship extraction from PubMed citations;
  2. Build a tool to browse and explore scientific literature with the help of the knowledge base created in 1.

The project involves extending our work in focused knowledge (entity-relationship) extraction from scientific literature, automatic taxonomy extraction from selected community authored content (eg Wikipedia), and semi-automatic ontology development with limited expert guidance.

Percuro KG

Leaders: Hong Yung (Joey) Yip

Healthcare KG

Leaders: Dr. Saeedeh Shekarpour, Dr. Amelie Gyrard

Internet of Things (IoT) KG

Leader: Dr. Amelie Gyrard


Linked Open Vocabularies for Internet of Things (LOV4IoT), an ontology catalog for Internet of Things, references ontology-based IoT projects:
- Almost 500 ontology-based projects for IoT, smart cities, etc.
- More than 20 domains relevant to IoT referenced such as building, smart grid, smart agriculture, robotics, smart transportation, healthcare, etc.
- We provide the LOV4IoT ontology catalog as an HTML view.
- We also provide the LOV4IoT RDF dataset.
LOV4IoT is an extension of the LOV (Linked Open Vocabulary) catalog.

  • Demo:



Knowledge Extraction for the Web of Things (KE4WoT) Challenge co-located with The Web Conference 2018 (WWW 2018)

Disaster Management KG

Leader: Hussein Al-Olimat, Shruti Kar

NSF Project: Hazards SEES: Social and Physical Sensing Enabled Decision Support for Disaster Management and Response

LOV4IoT-Disaster

Some publications:

  1. Shruti Kar, Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth, and Srinivasan Parthasarathy. "D-record: Disaster Response and Relief Coordination Pipeline". In Proceedings of the ACM SIGSPATIAL International Workshop on Advances in Resilient and Intelligent Cities (ARIC 2018). ACM, 2018.

Security KG

Security Toolbox: Attacks and Countermeasures (STAC) is a project to assist developers in: 1) Designing secured applications or architectures. 2) Being aware of main security threats. 3) Exploring security in various technologies such as: Sensor Networks, Cellular Networks (2G, 3G, 4G), Wireless Networks (Wi-Fi, Wimax, Zigbee, Bluetooth), Mesh/M2M/MANET, Network Management, Web Applications, Cryptography, Attacks & Countermeasures, Security Properties (e.g., authentication, integrity), Etc.

  • Demo:


Robotics KG

Leader: Dr. Amelie Gyrard

Affective Science (Well-Being and Happiness) KG

Leader: Dr. Amelie Gyrard

Scientific paper: IAMHAPPY: Towards An IoT Knowledge-Based Cross-Domain Well-Being Recommendation System for Everyday Happiness. IEEE/ACM Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE) Conference 2019. Elsevier Smart Health Journal

Teaching: Advanced Topics in Semantic Web

Some slides related to KG:

  • Knowlege Graphs (KG) - Advanced Semantic Web Class, Knoesis Lab, Wright State University - Amelie Gyrard, 11 September 2018

  • Contextualized Knowlege Graphs from two perspectives Semantic Web and Graph Database with an application in PubChem - Advanced Semantic Web Class, Knoesis Lab, Wright State University - Vinh Nguyen, 25 October 2018

  • Health Knowlege Graphs (KG) - Advanced Semantic Web Class, Knoesis Lab, Wright State University - Amelie Gyrard, 2 October 2018

  • Linked Open Data (LOD) - Advanced Semantic Web Class, Knoesis Lab, Wright State University - Amelie Gyrard, 2 October 2018

Event Organisation or PC members

Contextualized Knowledge Graphs (CKG) Workshop at ISWC 2018

Contextualized Knowledge Graphs (CKG) Workshop co-located with International Semantic Web Conference (ISWC 2018)

Tutorial at CIKM2018

Graphs: In Theory and Practice co-located with 26th ACM International Conference on Information and Knowledge Management (CIKM)

Publication

Knowledge Extraction for the Web of Things (KE4WoT) Challenge at WWW 2018

Knowledge Extraction for the Web of Things (KE4WoT) Challenge co-located with The Web Conference 2018 (WWW 2018)

PC members for KG events

Talks

  • Talk at Ontolog Community: CKG Portal: A knowledge publishing proposal for open knowledge network - Vinh Nguyen, 28 March 2018

  • Talk at Ontolog Community: Evolving Open Health Knowledge Network - Amit Sheth, 28 March 2018


Publications

  • [NEW] Yip, H. Y., & Sheth, A. (2024). The EMPWR Platform: Data and Knowledge-Driven Processes for the Knowledge Graph Lifecycle. IEEE Internet Computing, 28(1), 61-69.
  • [NEW] Jaimini, U., Henson, C., & Sheth, A. (2024). Causal Neuro-Symbolic AI: A Synergy Between Causality and Neuro-Symbolic Methods. IEEE Intelligent Systems, 39(2).
  • [NEW] Venkataramanan, R., Tripathy, A., Foltin, M., Yip, H. Y., Justine, A., & Sheth, A. (2023). Knowledge graph empowered machine learning pipelines for improved efficiency, reusability, and explainability. IEEE Internet Computing, 27(1), 81-88.
  • [NEW] El Kalach, F., Wickramarachchi, R., Harik, R., & Sheth, A. (2023). A semantic web approach to fault tolerant autonomous manufacturing. IEEE Intelligent Systems, 38(1), 69-75.
  • Sheth, A., Gaur, M., Roy, K., Venkataraman, R., & Khandelwal, V. (2022). Process knowledge-infused ai: Toward user-level explainability, interpretability, and safety. IEEE Internet Computing, 26(5), 76-84.
  • Gaur, M., Gunaratna, K., Bhatt, S., & Sheth, A. (2022). Knowledge-infused learning: A sweet spot in neuro-symbolic ai. IEEE Internet Computing, 26(4), 5-11.
  • Jaimini, U., & Sheth, A. (2022). CausalKG: Causal Knowledge Graph Explainability using interventional and counterfactual reasoning. IEEE Internet Computing, 26(1), 43-50.
  • Jaimini, U., Zhang, T., Brikis, G. O., & Sheth, A. (2022). imetaversekg: Industrial metaverse knowledge graph to promote interoperability in design and engineering applications. IEEE Internet Computing, 26(6), 59-67.
  • Sheth, A., Gaur, M., Roy, K., & Faldu, K. (2021). Knowledge-intensive language understanding for explainable ai. IEEE Internet Computing, 25(5), 19-24.
  • Sheth, A., & Thirunarayan, K. (2021). The duality of data and knowledge across the three waves of AI. IT professional, 23(3), 35-45.
  • Gaur, M., Faldu, K., & Sheth, A. (2021). Semantics of the black-box: Can knowledge graphs help make deep learning systems more interpretable and explainable?. IEEE Internet Computing, 25(1), 51-59.
  • Purohit, H., Shalin, V. L., & Sheth, A. P. (2020). Knowledge graphs to empower humanity-inspired AI systems. IEEE Internet Computing, 24(4), 48-54.
  • Bhatt, S., Sheth, A., Shalin, V., & Zhao, J. (2020). Knowledge graph semantic enhancement of input data for improving AI. IEEE Internet Computing, 24(2), 66-72.
  • Sheth, A., Gaur, M., Kursuncu, U., & Wickramarachchi, R. (2019). Shades of knowledge-infused learning for enhancing deep learning. IEEE Internet Computing, 23(6), 54-63.
  • Sheth, A., Padhee, S., & Gyrard, A. (2019). Knowledge graphs and knowledge networks: the story in brief. IEEE Internet Computing, 23(4), 67-75.
  • Wickramarachchi, R., Henson, C., & Sheth, A. (2022). Knowledge-based entity prediction for improved machine perception in autonomous systems. IEEE Intelligent Systems, 37(5), 42-49.
  • Amelie Gyrard, Manas Gaur, Krishnaprasad Thirunarayan, Amit Sheth and Saeedeh Shekarpour. Personalized Health Knowledge Graph. 1st Workshop on Contextualized Knowledge Graph (CKG) co-located with International Semantic Web Conference (ISWC), 8-12 October 2018, Monterey, USA.

PhD Thesis

See above links for slides and videos as well:

Projects or Tools

  • Contextualized Knowledge Graph (CKG) community

Online community discussion forum ckg-community@googlegroups.com, https://groups.google.com/forum/#!forum/ckg-community/join

  • LOV4IoT project: Ontology catalog for the Internet of Things which comprises an extension for healthcare.
  • PerfectO project: Ontology quality and best practices

Team

Faculty:

External Collaboration:

Post-doc:

Graduate Students:

Alumni:

References

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