KnoesisKnowledgeGraph

Knoesis Knowledge Graph (KKG)

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

Event Organisation

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)

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

• A. Sheth, S. Perera, S. Wijeratne, and K. Thirunarayan. Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples. Proceedings of the 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). Leipzig, Germany. ISBN: 978-1-4503-4951-2/17/08.

• Vinh Nguyen, Olivier Bodenreider, and Amit Sheth. "Don't like RDF reification?: making statements about statements using singleton property." Proceedings of the 23rd international conference on World wide web. ACM, 2014.

• Gunaratna, K., Thirunarayan, K., & Sheth, A. P. (2015, January). FACES: Diversity-Aware Entity Summarization Using Incremental Hierarchical Conceptual Clustering. In AAAI (pp. 116-122).

• Gunaratna, K., Thirunarayan, K., Sheth, A., & Cheng, G. (2016, May). Gleaning types for literals in rdf triples with application to entity summarization. In International Semantic Web Conference (pp. 85-100). Springer, Cham.

• Gunaratna, Kalpa, et al. "Relatedness-based multi-entity summarization." IJCAI: proceedings of the conference. Vol. 2017. NIH Public Access, 2017.

• Shekarpour, S., Marx, E., Auer, S. & Sheth, A. (2017). RQUERY: Rewriting Natural Language Queries on Knowledge Graphs to Alleviate the Vocabulary Mismatch Problem. 31st AAAI Conference on Artificial Intelligence, San Francisco, California.

• Sarasi Lalithsena, Sujan Perera, Pavan Kapanipathi, Amit Sheth. Domain-specific Hierarchical Subgraph Extraction: A Recommendation Use Case. 2017 IEEE International Conference on Big Data (Big Data), Boston, MA, 2017, pp. 666-675. doi: 10.1109/BigData.2017.8257982

• Sarasi Lalithsena, Pavan Kapanipathi and Amit Sheth. Harnessing relationships for domain-specific subgraph extraction: A recommendation use case. 2016 IEEE International Conference on Big Data (Big Data), Washington, DC, 2016, pp. 706-715. doi: 10.1109/BigData.2016.7840663

• Amelie Gyrard, Martin Serrano, Ghislain Atemezing. Semantic Web Methodologies, Best Practices and Ontology Engineering Applied to Internet of Things. IEEE World Forum on Internet of Things (WF-IoT), Milan, Italy, December 14-16, 2015

• Amelie Gyrard, Soumya Kanti Datta Christian Bonnet. A survey and analysis of ontology-based software tools for semantic interoperability in IoT and WoT landscapes. IEEE 4th World Forum on Internet of Things (WF-IoT), 2018

• Amelie Gyrard, Ghislain Atemezing, Christian Bonnet, Karima Boudaoud and Martin Serrano. Reusing and Unifying Background Knowledge for Internet of Things with LOV4IoT. 4rd International Conference on Future Internet of Things and Cloud (FiCloud 2016), 22-24 August 2016, Vienna, Austria

• Amelie Gyrard, Christian Bonnet, Karima Boudaoud and Martin Serrano. LOV4IoT: A second life for ontology-based domain knowledge to build Semantic Web of Things applications. 4rd International Conference on Future Internet of Things and Cloud (FiCloud 2016), 22-24 August 2016, Vienna, Austria

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:

• Dr. Amit P. Sheth (Kno.e.sis, Wright State University)

External Collaboration:

• Dr. Saeedeh Shekarpour (University of Dayton)

Post-doc:

• Dr. Amelie Gyrard (Kno.e.sis, Wright State University)

Graduate Students:

• Sarasi Lalithsena (Kno.e.sis, Wright State University)
• Manas Gaur (Kno.e.sis, Wright State University)
• Swati Padhee (Kno.e.sis, Wright State University)
• Joy Prakash Sain (Kno.e.sis, Wright State University)

Alumni:

• Kalpa Gunaratne (Samsung)
• Vinh Nguyen (National Library of Medicine)

References

• Tutorial Getting started with knowledge graphs