I am a Research Scientist in the Tetherless World Constellation at Rensselaer Polytechnic Institute, where I research and apply Semantic Web technologies in multidisciplinary domains for supporting more flexible, more efficient, and improved solutions in comparison with traditional approaches.
My research interests include data integration, knowledge representation, domain-specific reasoning, and explainable artificial intelligence. I have over 10 years of experience working with Semantic Web technologies and I hold a Doctor of Science degree (D.Sc.) in Applied Informatics from Universidade de Fortaleza, Brazil.
selected publications
From Data to City Indicators: A Knowledge Graph for Supporting Automatic Generation of Dashboards
Santos, Henrique, Dantas, Victor, Furtado, Vasco, Pinheiro, Paulo, and McGuinness, Deborah L.
In the context of Smart Cities, indicator definitions have been used to calculate values that enable the comparison among different cities. The calculation of an indicator values has challenges as the calculation may need to combine some aspects of quality while addressing different levels of abstraction. Knowledge graphs (KGs) have been used successfully to support flexible representation, which can support improved understanding and data analysis in similar settings. This paper presents an operational description for a city KG, an indicator ontology that support indicator discovery and data visualization and an application capable of performing metadata analysis to automatically build and display dashboards according to discovered indicators. We describe our implementation in an urban mobility setting.
@inproceedings{santos_data_2017,title={From {Data} to {City} {Indicators}: {A} {Knowledge} {Graph} for {Supporting} {Automatic} {Generation} of {Dashboards}},shorttitle={From {Data} to {City} {Indicators}},url={https://link.springer.com/chapter/10.1007/978-3-319-58451-5_7},doi={10.1007/978-3-319-58451-5_7},language={en},urldate={2017-05-20},booktitle={The {Semantic} {Web}},publisher={Springer, Cham},author={Santos, Henrique and Dantas, Victor and Furtado, Vasco and Pinheiro, Paulo and McGuinness, Deborah L.},month=may,year={2017},pages={94--108},selected={true},pdf={santos-indicators-2017.pdf},html={https://link.springer.com/chapter/10.1007/978-3-319-58451-5_7},slides={santos-indicators-2017-slides.pdf},presentation={http://videolectures.net/eswc2017_santos_knowledge_graph/},bibtex_show=true}
A Semantic Framework for Enabling Radio Spectrum Policy Management and Evaluation
Santos, Henrique, Mulvehill, Alice, Erickson, John S., McCusker, James P., Gordon, Minor, Xie, Owen, Stouffer, Samuel, Capraro, Gerard, Pidwerbetsky, Alex, Burgess, John, Berlinsky, Allan, Turck, Kurt, Ashdown, Jonathan, and McGuinness, Deborah L.
Because radio spectrum is a finite resource, its usage and sharing is regulated by government agencies. These agencies define policies to manage spectrum allocation and assignment across multiple organizations, systems, and devices. With more portions of the radio spectrum being licensed for commercial use, the importance of providing an increased level of automation when evaluating such policies becomes crucial for the efficiency and efficacy of spectrum management. We introduce our Dynamic Spectrum Access Policy Framework for supporting the United States government’s mission to enable both federal and non-federal entities to compatibly utilize available spectrum. The DSA Policy Framework acts as a machine-readable policy repository providing policy management features and spectrum access request evaluation. The framework utilizes a novel policy representation using OWL and PROV-O along with a domain-specific reasoning implementation that mixes GeoSPARQL, OWL reasoning, and knowledge graph traversal to evaluate incoming spectrum access requests and explain how applicable policies were used. The framework is currently being used to support live, over-the-air field exercises involving a diverse set of federal and commercial radios, as a component of a prototype spectrum management system.
@inproceedings{santos_semantic_2020,address={Cham},series={Lecture {Notes} in {Computer} {Science}},title={A {Semantic} {Framework} for {Enabling} {Radio} {Spectrum} {Policy} {Management} and {Evaluation}},isbn={978-3-030-62466-8},doi={10.1007/978-3-030-62466-8_30},language={en},booktitle={The {Semantic} {Web} – {ISWC} 2020},publisher={Springer International Publishing},author={Santos, Henrique and Mulvehill, Alice and Erickson, John S. and McCusker, James P. and Gordon, Minor and Xie, Owen and Stouffer, Samuel and Capraro, Gerard and Pidwerbetsky, Alex and Burgess, John and Berlinsky, Allan and Turck, Kurt and Ashdown, Jonathan and McGuinness, Deborah L.},editor={Pan, Jeff Z. and Tamma, Valentina and d’Amato, Claudia and Janowicz, Krzysztof and Fu, Bo and Polleres, Axel and Seneviratne, Oshani and Kagal, Lalana},year={2020},keywords={Dynamic spectrum access, Policies, Reasoning},pages={482--498},selected={true},pdf={santos-dsa-2020.pdf},html={https://link.springer.com/chapter/10.1007/978-3-030-62466-8_30},slides={santos-dsa-2020-slides.pdf},presentation={https://youtu.be/Kr88511c3dI},bibtex_show=true}
Designing a strong test for measuring true common-sense reasoning
Kejriwal, Mayank,Â
Santos, Henrique, Mulvehill, Alice M., and McGuinness, Deborah L.
Common-sense reasoning has recently emerged as an important test for artificial general intelligence, especially given the much-publicized successes of language representation models such as T5, BERT and GPT-3. Currently, typical benchmarks involve question answering tasks, but to test the full complexity of common-sense reasoning, more comprehensive evaluation methods that are grounded in theory should be developed.
@article{kejriwal_designing_2022,title={Designing a strong test for measuring true common-sense reasoning},volume={4},copyright={2022 Springer Nature Limited},issn={2522-5839},url={https://www.nature.com/articles/s42256-022-00478-4},doi={10.1038/s42256-022-00478-4},language={en},number={4},urldate={2022-04-24},journal={Nature Machine Intelligence},author={Kejriwal, Mayank and Santos, Henrique and Mulvehill, Alice M. and McGuinness, Deborah L.},month=apr,year={2022},note={Number: 4
Publisher: Nature Publishing Group},keywords={Computational science, Computer science},pages={318--322},pdf={https://rdcu.be/cLUHR},html={https://www.nature.com/articles/s42256-022-00478-4},bibtex_show=true,selected={true}}
