@article {894, title = {Diverse data! Diverse schemata?}, journal = {Semantic Web}, volume = {13}, year = {2022}, pages = {1{\textendash}3}, doi = {10.3233/SW-210453}, url = {https://doi.org/10.3233/SW-210453}, author = {Krzysztof Janowicz and Cogan Shimizu and Pascal Hitzler and Gengchen Mai and Shirly Stephen and Rui Zhu and Ling Cai and Lu Zhou and Mark Schildhauer and Zilong Liu and Zhangyu Wang and Meilin Shi} } @article {977, title = {Know, Know Where, Knowwheregraph: {A} Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence}, journal = {AI Mag.}, volume = {43}, year = {2022}, pages = {30{\textendash}39}, doi = {10.1609/AIMAG.V43I1.19120}, url = {https://doi.org/10.1609/aimag.v43i1.19120}, author = {Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Colby K. Fisher and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez Estrecha and Bryce D. Mecum and Anna Lopez-Carr and Andrew Schroeder and Dave Smith and Dawn J. Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D{\textquoteright}Onofrio and Zhining Gu and Kitty Currier} } @article {887, title = {Know, Know Where, KnowWhereGraph: A Densely Connected, Cross-Domain Knowledge Graph and Geo-Enrichment Service Stack for Applications in Environmental Intelligence}, journal = {AI Magazine}, year = {2022}, author = {Krzysztof Janowicz and Pascal Hitzler and Wenwen Li and Dean Rehberger and Mark Schildhauer and Rui Zhu and Cogan Shimizu and Colby K. Fisher and Ling Cai and Gengchen Mai and Joseph Zalewski and Lu Zhou and Shirly Stephen and Seila Gonzalez and Bryce Mecum and Anna Lopez Carr and Andrew Schroeder and Dave Smith and Dawn Wright and Sizhe Wang and Yuanyuan Tian and Zilong Liu and Meilin Shi and Anthony D{\textquoteright}Onofrio and Zhining Gu} } @article {897, title = {Neuro-symbolic approaches in artificial intelligence}, journal = {National Science Review}, volume = {9}, year = {2022}, month = {06/2022}, doi = {https://doi.org/10.1093/nsr/nwac035}, url = {https://academic.oup.com/nsr/article/9/6/nwac035/6542460}, author = {Pascal Hitzler and Aaron Eberhart and Monireh Ebrahimi and Md Kamruzzaman Sarker and Lu Zhou} } @article {862, title = {Neuro-Symbolic Artificial Intelligence: Current Trends}, journal = {AI Communications}, volume = {34}, year = {2022}, month = {2021}, chapter = {197}, author = {Md Kamruzzaman Sarker and Lu Zhou and Aaron Eberhart and Pascal Hitzler} } @proceedings {871, title = {Aligning Patterns to the Wikibase Model}, journal = {Workshop on Ontology Design and Patterns}, year = {2021}, author = {Andrew Eells and Cogan Shimizu and Lu Zhou and Pascal Hitzler and Seila Gonzalez Estrecha and Dean Rehberger} } @conference {869, title = {Environmental Observations in Knowledge Graphs}, booktitle = {DaMaLOS 2021 @ ISWC}, year = {2021}, abstract = {

The notion of Linked Open Science rests on the assumption that Linked Data principles contribute to science and scientific data management in several distinct ways (e.g., by adding rich semantics to improve retrieval and reuse of data). This begs the question of the right level of granularity for such semantic enrichment. On the one extreme of the spectrum, one may provide semantic annotations on the level of entire datasets to improve retrieval while leaving the actual data untouched. On the other end, one may semantically describe every single datum, such as a particular observation leading to data that supports reasoning, automated conflation, and so on, while, at the same time, dramatically increasing the size of data, including redundancy. This paper reports on our experience in modeling heterogeneous environmental data using a semantically-enabled observation framework, namely the SOSA ontology and its extensions to handle observation collections. We discuss different means of using these observation collections and compare their pros and cons in terms of data size and ease of querying.\ 

}, author = {Rui Zhu and Shirly Stephen Ambrose and Lu Zhou and Cogan Shimizu and Ling Cai and Gengchen Mai and Krzysztof Janowicz and Pascal Hitzler and Mark Schildhauer} } @conference {877, title = {InK Browser - The Interactive Knowledge Browser}, booktitle = {International Semantic Web Conference}, volume = {20}, year = {2021}, abstract = {

We present an improved implementation of the Interactive Knowledge Browser (InK Browser), a tool for exploring knowledge graphs visually, using a schema diagram.

}, author = {Joseph Zalewski and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @article {858, title = {Open Science data and the Semantic Web journal}, journal = {Semantic Web}, volume = {12(3)}, year = {2021}, month = {2021}, type = {Editorial}, chapter = {401-402}, doi = {10.3233/SW-210427}, author = {Pascal Hitzler and Krzysztof Janowicz and Cogan Shimizu and Lu Zhou and Andrew Eells} } @proceedings {964, title = {Results of the Ontology Alignment Evaluation Initiative 2021}, journal = {Proceedings of the 16th International Workshop on Ontology Matching co-located with the 20th International Semantic Web Conference (ISWC 2021)}, year = {2021}, month = {10/2021}, author = {Mina Abd Nikooie Pour and Alsayed Algergawy and Florence Amardeilh and Reihaneh Amini and Omaima Fallatah and Daniel Faria and Irini Fundulaki and Ian Harrow and Sven Hertling and Pascal Hitzler and Martin Huschka and Liliana Ibanescu and Ernesto Jimenez- Ruiz and Naouel Karam and Amir Laadhar and Patrick Lambrix and Huanyu Li and Ying Li and Franck Michel and Engy Nasr and Heiko Paulheim and Catia Pesquita and Jan Portisch and Catherine Roussey and Tzanina Saveta and Pavel Shvaiko and Andrea Splendiani and Cassia Trojahn and Jana Vatascinova and Beyza Yaman and Ondrej Zamazal and Lu Zhou} } @conference {899, title = {SOSA-SHACL: Shapes Constraint for the Sensor, Observation, Sample, and Actuator Ontology}, booktitle = {The 10th International Joint Conference on Knowledge Graphs, IJCKG 2021, December 6-8, 2021, Virtual Event, Thailand}, year = {2021}, publisher = {ACM}, organization = {ACM}, author = {Rui Zhu and Cogan Shimizu and Shirly Stephen and Lu Zhou and Ling Cai and Gengchen Mai and Krzysztof Janowicz and Mark Schildhauer and Pascal Hitzler} } @proceedings {852, title = {AROA Results of OAEI 2020}, journal = {Ontology Matching}, year = {2020}, month = {12/2020}, publisher = {Springer}, author = {Lu Zhou and Pascal Hitzler} } @proceedings {770, title = {Completion Reasoning Emulation for the Description Logic EL+}, journal = {Proceedings of the AAAI 2020 Spring Symposium on Combining Machine Learning and Knowledge Engineering in Practice}, volume = {2600}, year = {2020}, month = {03/2020}, publisher = {CEUR-WS.org}, address = {Stanford University, Palo Alto, California, USA}, abstract = {

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be inspected part-way through, rather than simply learning reasoner answers, which is typical in many of the black-box systems currently in use. We demonstrate that this idea is feasible by training a long short-term memory (LSTM) artificial neural network to learn EL+ reasoning patterns with two different data sets. We also show that this trained system is resistant to noise by corrupting a percentage of the test data and comparing the reasoner{\textquoteright}s and LSTM{\textquoteright}s predictions on corrupt data with correct answers.

}, keywords = {Deep Learning, Description Logic, EL+, LSTM, NeSy, Reasoning}, url = {http://ceur-ws.org/Vol-2600/paper5.pdf}, author = {Aaron Eberhart and Monireh Ebrahimi and Lu Zhou and Cogan Shimizu and Pascal Hitzler} } @conference {788, title = {The Enslaved Dataset: A Real-world Complex Ontology Alignment Benchmark using Wikibase}, booktitle = {29th ACM International Conference on Information and Knowledge Management}, year = {2020}, month = {10/2020}, publisher = {ACM}, organization = {ACM}, author = {Lu Zhou and Cogan Shimizu and Pascal Hitzler and Alicia M Sheill and Seila Gonzalez Estrecha and Catherine Foley and Duncan Tarr and Dean Rehberger} } @conference {789, title = {GeoLink Cruises: A Non-Synthetic Benchmark for Co-Reference Resolution on Knowledge Graphs}, booktitle = {International conference on information and knowledge management}, year = {2020}, month = {10/2020}, publisher = {ACM DL}, organization = {ACM DL}, author = {Reihaneh Amini and Lu Zhou and Pascal Hitzler} } @article {755, title = {GeoLink Dataset: A Complex Alignment Benchmark from Real-world Ontology}, journal = {Data Intelligence }, year = {2020}, author = {Lu Zhou and Michelle Cheatham and Adila Krisnadhi and Pascal Hitzler} } @proceedings {854, title = {Results of theOntology Alignment Evaluation Initiative 2020}, journal = {15th International Workshop on Ontology Matching}, year = {2020}, author = {Mina Abd Nikooie Pour and Alsayed Algergawy and Reihaneh Amini and Daniel Faria and Irini Fundulaki and Ian Harrow and Sven Hertling and Ernesto Jim{\'e}nez-Ruiz and Clement Jonquet and Naouel Karam and Abderrahmane Khiat and Amir Laadhar and Patrick Lambrix and Huanyu Li and Ying Li and Pascal Hitzler and Heiko Paulheim and Catia Pesquita and Tzanina Saveta and Pavel Shvaiko and Andrea Splendiani and Elodie Thieblin and Cassia Trojahn and Jana Vatascinova and Beyza Yaman and Ondrej Zamazal and Lu Zhou} } @mastersthesis {879, title = {Towards automated complex ontology alignment using rule-based machine learning}, volume = {Doctorate}, year = {2020}, school = {Kansas State University}, type = {Dissertation}, address = {Manhattan}, author = {Lu Zhou} } @article {768, title = {Towards Evaluating Complex Ontology Alignments}, journal = {Knowledge Engineering Review}, volume = {35}, year = {2020}, chapter = {e21}, author = {Lu Zhou and Elodie Thieblin and Michelle Cheatham and Daniel Faria and Catia Pesquita and Cassia Trojahn and Ondrej Zamazal} } @conference {813, title = {Wikipedia Knowledge Graph for Explainable AI}, booktitle = {Second Iberoamerican Knowledge Graphs and Semantic Web Conference (KGSWC)}, year = {2020}, month = {11/2020}, abstract = {

Explainable artificial intelligence (XAI) requires domain information to explain a system{\textquoteright}s decisions, for which structured forms of domain information like Knowledge Graphs (KGs) or ontologies are best suited. As such, readily available KGs are important to accelerate progress in XAI. To facilitate the advancement of XAI, we present the Wikipedia Knowledge Graph (WKG), based on information from English Wikipedia. Each Wikipedia article title, its corresponding category, and the category hierarchy are transformed into different entities in the knowledge graph. As the Wikipedia category hierarchy is not a tree, instead forming a graph, to make the finding process of the parent category easier, we break cycles in the category hierarchy. We evaluate whether the WKG is helpful to improve XAI compared with existing KGs, finding that WKG is better suited than the current state of the art. We also compare the cycle-free WKG with the Suggested Upper Merged Ontology (SUMO) and DBpedia schema KGs, finding minimal to no information loss.

}, author = {Md Kamruzzaman Sarker and Joshua Schwartz and Pascal Hitzler and Lu Zhou and Srikanth Nadella and Brandon Minnery and Ion Juvina and Michael L. Raymer and William R. Aue} } @article {763, title = {Alignment of Surface Water Ontologies: A comparison of manual and automated approaches}, journal = {Journal of Geographical Systems}, year = {2019}, doi = {10.1007/s10109-019-00312-3}, author = {Michelle Cheatham and Dalia Varanka and Fatima Arauz and Lu Zhou} } @booklet {943, title = {Alignment Rules from GBO to GMO}, year = {2019}, author = {Lu Zhou} } @conference {769, title = {AROA Results of 2019 OAEI}, booktitle = {Ontology Matching}, year = {2019}, publisher = {CEUR}, organization = {CEUR}, address = {Auckland, New Zealand}, author = {Lu Zhou and Michelle Cheatham and Pascal Hitzler} } @conference {767, title = {Towards Association Rule-Based Complex Ontology Alignment}, booktitle = {Joint International Semantic Technology Conference}, year = {2019}, publisher = {Springer}, organization = {Springer}, address = {Hangzhou, China}, author = {Lu Zhou and Michelle Cheatham and Pascal Hitzler} } @conference {694, title = {A Complex Alignment Benchmark: Geolink dataset}, booktitle = {ISWC}, year = {2018}, publisher = {Springer}, organization = {Springer}, author = {Lu Zhou and Michelle Cheatham and Adila Krisnadhi and Pascal Hitzler} } @article {693, title = {The First Version of the OAEI Complex Alignment Benchmark}, journal = {ISWC Poster and Demo Session}, year = {2018}, author = {Elodie Thieblin and Michelle Cheatham and Cassia Trojahn and Ondrej Zamazal and Lu Zhou} } @conference {695, title = {A Journey From Simple to Complex Alignment on Real-World Ontologies}, booktitle = {ISWC}, year = {2018}, author = {Lu Zhou} } @article {609, title = {A Replication Study: Understanding What Drives the Performance in WikiMatch}, year = {2017}, abstract = {

We replicate and demonstrate that the performance of the WikiMatch automated ontology alignment system may be driven not by the particular information from Wikipedia directly used by the system, but rather by string similarity and Wikipedia{\textquoteright}s manually curated synonym sets, as encoded in the site{\textquoteright}s query resolution and page redirection system. In order to gain a detailed understanding of how Wikipedia contributes to WikiMatch, we replicate results reported for WikiMatch and analyze the results to evaluate our hypothesis.

}, url = {http://disi.unitn.it/~pavel/om2017/papers/om2017_poster5.pdf}, author = {Lu Zhou and Michelle Cheatham} } @conference {508, title = {Tweet Properly: Analyzing Deleted Tweets to Understand and Identify Regrettable Ones}, booktitle = {International Conference on World Wide Web }, year = {2016}, month = {04/2016}, publisher = {International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland}, organization = {International World Wide Web Conferences Steering Committee Republic and Canton of Geneva, Switzerland}, abstract = {

}, isbn = {978-1-4503-4143-1}, doi = {doi>10.1145/2872427.2883052}, author = {Lu Zhou and Wenbo Wang and Keke Chen} } @article {497, title = {Identifying Regrettable Messages from Tweets}, year = {2015}, month = {05/2015}, publisher = {International World Wide Web Conferences Steering Committee}, type = {Posters}, isbn = {978-1-4503-3473-0}, doi = {doi>10.1145/2740908.2742735}, author = {Lu Zhou and Wenbo Wang and Keke Chen} } @conference {510, title = {Scalable Euclidean Embedding for Big Data}, booktitle = {International Conference on Cloud Computing }, year = {2015}, month = {07/2015}, publisher = {IEEE }, organization = {IEEE }, address = {New York City, NY }, doi = {10.1109/CLOUD.2015.107}, author = {Zohreh Alavi and Sagar Sharma and Lu Zhou and Keke Chen} } @article {509, title = {RASP-QS: Efficient and Confidential Query Services in the Cloud}, year = {2014}, address = {VLDB Endowment}, doi = {doi>10.14778/2733004.2733061}, author = {Zohreh Alavi and Lu Zhou and James Powers and Keke Chen} } @article {967, title = {InK Browser {\textendash} The Interactive Knowledge Browser}, pages = {Proceedings of the ISWC 2021 Posters, Demos and Industry Tracks: From Novel Ideas to Industrial Practice co-located with 20th In}, author = {Joseph Zalweski and Lu Zhou and Cogan Shimizu and Pascal Hitzler} }