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\n  \n 2025\n \n \n (6)\n \n \n
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\n \n\n \n \n \n \n \n \n Embedding Spatial and Semantic Contexts for Geo-Entity Typing in Smart City Applications.\n \n \n \n \n\n\n \n Shbita, B.; Vu, B.; Lin, F.; and Knoblock, C. A.\n\n\n \n\n\n\n In Companion Proceedings of the ACM on Web Conference 2025, of WWW '25, pages 1724–1732, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"EmbeddingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 17 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3701716.3718325,\n  author    = {Shbita, Basel and Vu, Binh and Lin, Fandel and Knoblock, Craig A.},\n  title     = {Embedding Spatial and Semantic Contexts for Geo-Entity Typing in Smart City Applications},\n  year      = {2025},\n  isbn      = {9798400713316},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  url       = {https://doi.org/10.1145/3701716.3718325},\n  doi       = {10.1145/3701716.3718325},\n  abstract  = {Geospatial data are critical for urban planning and smart city applications, yet understanding and classifying geo-entities in diverse datasets remains challenging. Accurate representation and classification of geo-entities are essential for tasks such as geo-entity typing and linking, enabling better map understanding and informed decision-making. This paper presents a self-supervised learning approach to classify geo-entities by embedding their geometric, spatial, and semantic neighborhood contexts, creating robust representations for geo-entity typing. Using OpenStreetMap (OSM) data, our method links geo-referenced entities to Wikidata classes and OSM tags with high performance, achieving an F1 score of approximately 0.85. Beyond the technical contribution, our method addresses Responsible AI challenges, including transparency, and data standardization on the Web, aligning with sustainable smart city development.},\n  booktitle = {Companion Proceedings of the ACM on Web Conference 2025},\n  pages     = {1724–1732},\n  numpages  = {9},\n  keywords  = {digital twin, geospatial data integration, open data, representation learning, semantic typing, smart cities, web technologies},\n  location  = {Sydney NSW, Australia},\n  series    = {WWW '25}\n}\n\n
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\n Geospatial data are critical for urban planning and smart city applications, yet understanding and classifying geo-entities in diverse datasets remains challenging. Accurate representation and classification of geo-entities are essential for tasks such as geo-entity typing and linking, enabling better map understanding and informed decision-making. This paper presents a self-supervised learning approach to classify geo-entities by embedding their geometric, spatial, and semantic neighborhood contexts, creating robust representations for geo-entity typing. Using OpenStreetMap (OSM) data, our method links geo-referenced entities to Wikidata classes and OSM tags with high performance, achieving an F1 score of approximately 0.85. Beyond the technical contribution, our method addresses Responsible AI challenges, including transparency, and data standardization on the Web, aligning with sustainable smart city development.\n
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\n \n\n \n \n \n \n \n Demonstrating MinMod: A Large-scale Knowledge Graph of Historical Mining Data.\n \n \n \n\n\n \n Knoblock, C. A; Vu, B.; Shbita, B.; Krishna, P. P.; and Sharma, N.\n\n\n \n\n\n\n In International Semantic Web Conference, 2025. \n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{knoblock2025demonstrating,\n  title     = {Demonstrating MinMod: A Large-scale Knowledge Graph of Historical Mining Data},\n  author    = {Knoblock, Craig A and Vu, Binh and Shbita, Basel and Krishna, Pothula Punith and Sharma, Namrata},\n  booktitle = {International Semantic Web Conference},\n  year      = {2025}\n}\n\n
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\n \n\n \n \n \n \n \n Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data.\n \n \n \n\n\n \n Knoblock, C. A; Vu, B.; Shbita, B.; Chang, Y.; Lin, X.; Muric, G.; Krishna, P.; Pyo, J.; Trejo-Sheu, A.; and Ye, M.\n\n\n \n\n\n\n In The Semantic Web–ISWC 2025: 24th International Semantic Web Conference, ISWC 2025, November 2–6, 2025, Proceedings 20, 2025. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{minmod2025,\n  title        = {Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data},\n  author       = {Knoblock, Craig A and Vu, Binh and Shbita, Basel and Chang, Yao-Yi and Lin, Xiao and Muric, Goran and Krishna, Pothula and Pyo, Jiyoon and Trejo-Sheu, Adriana and Ye, Meng},\n  booktitle    = {The Semantic Web--ISWC 2025: 24th International Semantic Web Conference, ISWC 2025, November 2--6, 2025, Proceedings 20},\n  year         = {2025},\n  organization = {Springer International Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n A Domain-Independent Approach for Semantic Table Interpretation.\n \n \n \n\n\n \n Vu, B.; Knoblock, C. A; and Lin, F.\n\n\n \n\n\n\n In The Semantic Web–ISWC 2025: 24th International Semantic Web Conference, ISWC 2025, November 2–6, 2025, Proceedings 20, 2025. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2025,\n  title        = {A Domain-Independent Approach for Semantic Table Interpretation},\n  author       = {Vu, Binh and Knoblock, Craig A and Lin, Fandel},\n  booktitle    = {The Semantic Web--ISWC 2025: 24th International Semantic Web Conference, ISWC 2025, November 2--6, 2025, Proceedings 20},\n  year         = {2025},\n  organization = {Springer International Publishing}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Polygon Metadata to Recolor Historical Maps.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; Vu, B.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '25, pages 1122–1125, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3748636.3763209,\n  author    = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Chiang, Yao-Yi},\n  title     = {Exploiting Polygon Metadata to Recolor Historical Maps},\n  year      = {2025},\n  isbn      = {9798400720864},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  url       = {https://doi.org/10.1145/3748636.3763209},\n  doi       = {10.1145/3748636.3763209},\n  abstract  = {Historical maps often suffer from coloring errors caused by artifacts during map production or scanning. These errors result in color mismatches between important map features (e.g., polygon layers) and their corresponding map keys, which hinders both human interpretation and automated feature extraction. This paper targets the problem of automatically correcting polygon coloring errors in historical maps using only in-map information, such as the map keys. The challenge lies in the diverse visual representations of map keys and variations in coloring errors, which differ significantly both within and across maps. We propose a machine-learning model that automatically identifies and corrects color inconsistencies between map polygon layers and their visual appearances defined by the map keys on the same map. Our approach leverages polygon metadata, such as map keys describing the visual and semantic properties of each polygon on maps, to detect mismatches in color histograms and representations and recolor the incorrect areas in the map content accordingly. We evaluate our approach on USGS geological maps; it outperforms comparative methods by at least 7.5\\%. In addition, our approach improves the downstream automated polygon-extraction task by 18.0\\% in precision.},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  pages     = {1122–1125},\n  numpages  = {4},\n  keywords  = {historical map, map recoloring, image processing},\n  location  = {The Graduate Hotel Minneapolis, Minneapolis, MN, USA},\n  series    = {SIGSPATIAL '25}\n}\n\n
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\n Historical maps often suffer from coloring errors caused by artifacts during map production or scanning. These errors result in color mismatches between important map features (e.g., polygon layers) and their corresponding map keys, which hinders both human interpretation and automated feature extraction. This paper targets the problem of automatically correcting polygon coloring errors in historical maps using only in-map information, such as the map keys. The challenge lies in the diverse visual representations of map keys and variations in coloring errors, which differ significantly both within and across maps. We propose a machine-learning model that automatically identifies and corrects color inconsistencies between map polygon layers and their visual appearances defined by the map keys on the same map. Our approach leverages polygon metadata, such as map keys describing the visual and semantic properties of each polygon on maps, to detect mismatches in color histograms and representations and recolor the incorrect areas in the map content accordingly. We evaluate our approach on USGS geological maps; it outperforms comparative methods by at least 7.5%. In addition, our approach improves the downstream automated polygon-extraction task by 18.0% in precision.\n
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\n \n\n \n \n \n \n \n \n Exploiting Polygon Metadata to Colorize Draft Maps.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; Vu, B.; Shbita, B.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '25, pages 1126–1129, New York, NY, USA, 2025. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3748636.3763210,\n  author    = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh and Shbita, Basel and Chiang, Yao-Yi},\n  title     = {Exploiting Polygon Metadata to Colorize Draft Maps},\n  year      = {2025},\n  isbn      = {9798400720864},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  url       = {https://doi.org/10.1145/3748636.3763210},\n  doi       = {10.1145/3748636.3763210},\n  abstract  = {Black-and-white draft geological maps, produced during fieldwork, often contain dense handwritten annotations overlaid on monochromatic contour basemaps. Although interpretable in grayscale, the lack of color makes it difficult to visually distinguish overlapping or adjacent geological units, especially when boundaries are unclear and annotation styles vary. However, colorizing these draft maps is labor-intensive but essential, as they may be the only source of detailed geological information for certain regions. This hinders both human interpretation and downstream tasks such as map digitization and critical mineral resource assessment. We target the problem of automated colorization of draft geological maps. The challenge lies in interpreting noisy visual cues from uncolored sketches and assigning appropriate colors according to their geological categories. We propose a novel machine learning approach that exploits polygon metadata, including map keys that explicitly define geological units and implicitly suggest their intended colors, along with the semantic interpretation of the sketch content in the maps, to assign colors to the draft maps accordingly. We evaluate our method on USGS draft geological maps; it outperforms comparative methods by 15.7\\%. In addition, our approach improves downstream polygon-extraction performance by 9\\% in F1 score.},\n  booktitle = {Proceedings of the 33rd ACM International Conference on Advances in Geographic Information Systems},\n  pages     = {1126–1129},\n  numpages  = {4},\n  keywords  = {geological map, sketch colorization, image processing},\n  location  = {The Graduate Hotel Minneapolis, Minneapolis, MN, USA},\n  series    = {SIGSPATIAL '25}\n}
\n
\n\n\n
\n Black-and-white draft geological maps, produced during fieldwork, often contain dense handwritten annotations overlaid on monochromatic contour basemaps. Although interpretable in grayscale, the lack of color makes it difficult to visually distinguish overlapping or adjacent geological units, especially when boundaries are unclear and annotation styles vary. However, colorizing these draft maps is labor-intensive but essential, as they may be the only source of detailed geological information for certain regions. This hinders both human interpretation and downstream tasks such as map digitization and critical mineral resource assessment. We target the problem of automated colorization of draft geological maps. The challenge lies in interpreting noisy visual cues from uncolored sketches and assigning appropriate colors according to their geological categories. We propose a novel machine learning approach that exploits polygon metadata, including map keys that explicitly define geological units and implicitly suggest their intended colors, along with the semantic interpretation of the sketch content in the maps, to assign colors to the draft maps accordingly. We evaluate our method on USGS draft geological maps; it outperforms comparative methods by 15.7%. In addition, our approach improves downstream polygon-extraction performance by 9% in F1 score.\n
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\n  \n 2024\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n \n Constructing a Knowledge Graph of Historical Mining Data.\n \n \n \n \n\n\n \n Shbita, B.; Sharma, N.; Vu, B.; Lin, F.; and Knoblock, C. A\n\n\n \n\n\n\n . 2024.\n \n\n\n\n
\n\n\n\n \n \n \"Constructing paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 13 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{shbita2024constructing,\n  title     = {Constructing a Knowledge Graph of Historical Mining Data},\n  author    = {Shbita, Basel and Sharma, Namrata and Vu, Binh and Lin, Fandel and Knoblock, Craig A},\n  year      = {2024},\n  url_paper = {https://usc-isi-i2.github.io/papers/shbita24-geold_ewsc.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n Results of GRAMS+ at SemTab 2024.\n \n \n \n\n\n \n Vu, B.; Knoblock, C; and Lin, F.\n\n\n \n\n\n\n 2024.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@misc{vu2024results,\n  title     = {Results of GRAMS+ at SemTab 2024},\n  author    = {Vu, Binh and Knoblock, C and Lin, Fandel},\n  year      = {2024},\n  publisher = {SemTab}\n}\n\n
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\n \n\n \n \n \n \n \n \n Exploiting Distant Supervision to Learn Semantic Descriptions of Tables with Overlapping Data.\n \n \n \n \n\n\n \n Vu, B.; Knoblock, C. A; Shbita, B.; and Lin, F.\n\n\n \n\n\n\n In The Semantic Web–ISWC 2024: 23th International Semantic Web Conference, ISWC 2024, November 11–15, 2024, Proceedings 20, 2024. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"Exploiting paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 8 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2024,\n  title        = {Exploiting Distant Supervision to Learn Semantic Descriptions of Tables with Overlapping Data},\n  author       = {Vu, Binh and Knoblock, Craig A and Shbita, Basel and Lin, Fandel},\n  booktitle    = {The Semantic Web--ISWC 2024: 23th International Semantic Web Conference, ISWC 2024, November 11--15, 2024, Proceedings 20},\n  year         = {2024},\n  organization = {Springer International Publishing},\n  url_paper    = {https://binh-vu.github.io/assets/publications/iswc24-paper.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n A Hierarchical Voronoi Approach to Deploying New Charging Stations in an Existing Network.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; and Vu, B.\n\n\n \n\n\n\n In Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '24, pages 741–744, New York, NY, USA, 2024. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"APaper\n  \n \n \n \"A paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{10.1145/3678717.3700829,\n  author    = {Lin, Fandel and Knoblock, Craig A. and Vu, Binh},\n  title     = {A Hierarchical Voronoi Approach to Deploying New Charging Stations in an Existing Network},\n  year      = {2024},\n  isbn      = {9798400711077},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  url       = {https://doi.org/10.1145/3678717.3700829},\n  doi       = {10.1145/3678717.3700829},\n  abstract  = {With the rapid development of electric vehicles that facilitate sustainable transportation, the need to ensure their accessibility has increased. Determining the optimal locations for extending an existing charging station network is crucial to addressing rising infrastructure needs and reducing environmental impacts. The 13th ACM SIGSPATIAL Cup competition (GISCUP 2024) targets the problem of optimal minimum charging station deployment within an existing network. The recommendation of station locations aims to maximize accessibility over long-distance travel and ensure both coverage and proximity to points of interest or areas with high vehicle usage, while minimizing traffic congestion and reducing impacts on the power grid and the existing charging network. We leverage the categorical Voronoi diagram with hierarchical reconciliation to recommend stations that dovetail with the existing charging network under zero access to historical data. Our approach maximizes station deployment in high-demand areas with equitable distribution. Meanwhile, it complements the existing network to minimize unfavorable impacts on the power grid. Qualitative evaluation on a large-scale real-world dataset shows that our approach effectively balances objectives across areas with diverse geographical and demographic characteristics. In addition, our approach ranked in the top five among all participants in the GISCUP 2024.},\n  booktitle = {Proceedings of the 32nd ACM International Conference on Advances in Geographic Information Systems},\n  pages     = {741–744},\n  numpages  = {4},\n  keywords  = {Charging network expansion, Charging station deployment, Electric vehicle, Voronoi diagram},\n  location  = {Atlanta, GA, USA},\n  series    = {SIGSPATIAL '24},\n  url_paper = {https://dl.acm.org/doi/pdf/10.1145/3678717.3700829}\n}\n\n
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\n With the rapid development of electric vehicles that facilitate sustainable transportation, the need to ensure their accessibility has increased. Determining the optimal locations for extending an existing charging station network is crucial to addressing rising infrastructure needs and reducing environmental impacts. The 13th ACM SIGSPATIAL Cup competition (GISCUP 2024) targets the problem of optimal minimum charging station deployment within an existing network. The recommendation of station locations aims to maximize accessibility over long-distance travel and ensure both coverage and proximity to points of interest or areas with high vehicle usage, while minimizing traffic congestion and reducing impacts on the power grid and the existing charging network. We leverage the categorical Voronoi diagram with hierarchical reconciliation to recommend stations that dovetail with the existing charging network under zero access to historical data. Our approach maximizes station deployment in high-demand areas with equitable distribution. Meanwhile, it complements the existing network to minimize unfavorable impacts on the power grid. Qualitative evaluation on a large-scale real-world dataset shows that our approach effectively balances objectives across areas with diverse geographical and demographic characteristics. In addition, our approach ranked in the top five among all participants in the GISCUP 2024.\n
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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction.\n \n \n \n \n\n\n \n Lin, F.; Knoblock, C. A.; Shbita, B.; Vu, B.; Li, Z.; and Chiang, Y.\n\n\n \n\n\n\n In Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems, of SIGSPATIAL '23, New York, NY, USA, 2023. Association for Computing Machinery\n \n\n\n\n
\n\n\n\n \n \n \"ExploitingPaper\n  \n \n \n \"Exploiting paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n\n\n\n
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@inproceedings{lin2023exploiting,\n  author    = {Lin, Fandel and Knoblock, Craig A. and Shbita, Basel and Vu, Binh and Li, Zekun and Chiang, Yao-Yi},\n  title     = {Exploiting Polygon Metadata to Understand Raster Maps - Accurate Polygonal Feature Extraction},\n  year      = {2023},\n  isbn      = {9798400701689},\n  publisher = {Association for Computing Machinery},\n  address   = {New York, NY, USA},\n  url       = {https://doi.org/10.1145/3589132.3625659},\n  doi       = {10.1145/3589132.3625659},\n  abstract  = {Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52\\%.},\n  booktitle = {Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems},\n  articleno = {109},\n  numpages  = {12},\n  keywords  = {raster map, polygon extraction, image processing},\n  location  = {Hamburg, Germany},\n  series    = {SIGSPATIAL '23},\n  url_paper = {https://dl.acm.org/doi/pdf/10.1145/3589132.3625659}\n}\n\n
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\n Locating undiscovered deposits of critical minerals requires accurate geological data. However, most of the 100,000 historical geological maps of the United States Geological Survey (USGS) are in raster format. This hinders critical mineral assessment. We target the problem of extracting geological features represented as polygons from raster maps. We exploit the polygon metadata that provides information on the geological features, such as the map keys indicating how the polygon features are represented, to extract the features. We present a metadata-driven machine-learning approach that encodes the raster map and map key into a series of bitmaps and uses a convolutional model to learn to recognize the polygon features. We evaluated our approach on USGS geological maps; our approach achieves a median F1 score of 0.809 and outperforms state-of-the-art methods by 4.52%.\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Sand: A tool for creating semantic descriptions of tabular sources.\n \n \n \n \n\n\n \n Vu, B.; and Knoblock, C. A\n\n\n \n\n\n\n In European Semantic Web Conference, pages 63–67, 2022. Springer International Publishing Cham\n \n\n\n\n
\n\n\n\n \n \n \"Sand: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2022sand,\n  title        = {Sand: A tool for creating semantic descriptions of tabular sources},\n  author       = {Vu, Binh and Knoblock, Craig A},\n  booktitle    = {European Semantic Web Conference},\n  pages        = {63--67},\n  year         = {2022},\n  organization = {Springer International Publishing Cham},\n  url_paper    = {https://binh-vu.github.io/assets/publications/eswc22.pdf}\n}\n\n
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\n  \n 2021\n \n \n (4)\n \n \n
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\n \n\n \n \n \n \n \n Picasso: Model-free feature visualization.\n \n \n \n\n\n \n Vu, B.; and Markov, I.\n\n\n \n\n\n\n arXiv preprint arXiv:2111.12795. 2021.\n \n\n\n\n
\n\n\n\n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{vu2021picasso,\n  title   = {Picasso: Model-free feature visualization},\n  author  = {Vu, Binh and Markov, Igor},\n  journal = {arXiv preprint arXiv:2111.12795},\n  year    = {2021}\n}\n\n
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\n \n\n \n \n \n \n \n \n A graph-based approach for inferring semantic descriptions of wikipedia tables.\n \n \n \n \n\n\n \n Vu, B.; Knoblock, C. A; Szekely, P.; Pham, M.; and Pujara, J.\n\n\n \n\n\n\n In The Semantic Web–ISWC 2021: 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24–28, 2021, Proceedings 20, pages 304–320, 2021. Springer International Publishing\n \n\n\n\n
\n\n\n\n \n \n \"A paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 6 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2021graph,\n  title        = {A graph-based approach for inferring semantic descriptions of wikipedia tables},\n  author       = {Vu, Binh and Knoblock, Craig A and Szekely, Pedro and Pham, Minh and Pujara, Jay},\n  booktitle    = {The Semantic Web--ISWC 2021: 20th International Semantic Web Conference, ISWC 2021, Virtual Event, October 24--28, 2021, Proceedings 20},\n  pages        = {304--320},\n  year         = {2021},\n  organization = {Springer International Publishing},\n  url_paper    = {https://binh-vu.github.io/assets/publications/iswc21-paper.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making.\n \n \n \n \n\n\n \n Gil, Y.; Garijo, D.; Khider, D.; Knoblock, C. A.; Ratnakar, V.; Osorio, M.; Vargas, H.; Pham, M.; Pujara, J.; Shbita, B.; Vu, B.; Chiang, Y.; Feldman, D.; Lin, Y.; Song, H.; Kumar, V.; Khandelwal, A.; Steinbach, M.; Tayal, K.; Xu, S.; Pierce, S. A.; Pearson, L.; Hardesty-Lewis, D.; Deelman, E.; Silva, R. F. D.; Mayani, R.; Kemanian, A. R.; Shi, Y.; Leonard, L.; Peckham, S.; Stoica, M.; Cobourn, K.; Zhang, Z.; Duffy, C.; and Shu, L.\n\n\n \n\n\n\n ACM Trans. Interact. Intell. Syst., 11(2). July 2021.\n \n\n\n\n
\n\n\n\n \n \n \"ArtificialPaper\n  \n \n \n \"Artificial paper\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n  \n \n 25 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{10.1145/3453172,\n  author     = {Gil, Yolanda and Garijo, Daniel and Khider, Deborah and Knoblock, Craig A. and Ratnakar, Varun and Osorio, Maximiliano and Vargas, Hern\\'{a}n and Pham, Minh and Pujara, Jay and Shbita, Basel and Vu, Binh and Chiang, Yao-Yi and Feldman, Dan and Lin, Yijun and Song, Hayley and Kumar, Vipin and Khandelwal, Ankush and Steinbach, Michael and Tayal, Kshitij and Xu, Shaoming and Pierce, Suzanne A. and Pearson, Lissa and Hardesty-Lewis, Daniel and Deelman, Ewa and Silva, Rafael Ferreira Da and Mayani, Rajiv and Kemanian, Armen R. and Shi, Yuning and Leonard, Lorne and Peckham, Scott and Stoica, Maria and Cobourn, Kelly and Zhang, Zeya and Duffy, Christopher and Shu, Lele},\n  title      = {Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making},\n  year       = {2021},\n  issue_date = {June 2021},\n  publisher  = {Association for Computing Machinery},\n  address    = {New York, NY, USA},\n  volume     = {11},\n  number     = {2},\n  issn       = {2160-6455},\n  url        = {https://doi.org/10.1145/3453172},\n  doi        = {10.1145/3453172},\n  abstract   = {Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort.We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.},\n  journal    = {ACM Trans. Interact. Intell. Syst.},\n  month      = jul,\n  articleno  = {11},\n  numpages   = {49},\n  keywords   = {remote sensing data, regional-level decision-making, model metadata, integrated modeling, Intelligent user interfaces},\n  url_paper  = {https://dl.acm.org/doi/pdf/10.1145/3453172}\n}\n\n
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\n Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort.We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.\n
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\n \n\n \n \n \n \n \n \n SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables.\n \n \n \n \n\n\n \n Pham, M.; Knoblock, C. A; Chen, M.; Vu, B.; and Pujara, J.\n\n\n \n\n\n\n In IJCAI, pages 3543–3551, 2021. \n \n\n\n\n
\n\n\n\n \n \n \"SPADE: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 1 download\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{pham2021spade,\n  title     = {SPADE: A Semi-supervised Probabilistic Approach for Detecting Errors in Tables.},\n  author    = {Pham, Minh and Knoblock, Craig A and Chen, Muhao and Vu, Binh and Pujara, Jay},\n  booktitle = {IJCAI},\n  pages     = {3543--3551},\n  year      = {2021},\n  url_paper = {https://www.ijcai.org/proceedings/2021/0488.pdf}\n}\n\n
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\n  \n 2019\n \n \n (5)\n \n \n
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\n \n\n \n \n \n \n \n \n D-REPR: a language for describing and mapping diversely-structured data sources to RDF.\n \n \n \n \n\n\n \n Vu, B.; Pujara, J.; and Knoblock, C. A\n\n\n \n\n\n\n In Proceedings of the 10th International Conference on Knowledge Capture, pages 189–196, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"D-REPR: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 18 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2019d,\n  title     = {D-REPR: a language for describing and mapping diversely-structured data sources to RDF},\n  author    = {Vu, Binh and Pujara, Jay and Knoblock, Craig A},\n  booktitle = {Proceedings of the 10th International Conference on Knowledge Capture},\n  pages     = {189--196},\n  year      = {2019},\n  url_paper = {https://binh-vu.github.io/assets/publications/kcap19-paper.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n Learning semantic models of data sources using probabilistic graphical models.\n \n \n \n \n\n\n \n Vu, B.; Knoblock, C.; and Pujara, J.\n\n\n \n\n\n\n In The world wide web conference, pages 1944–1953, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"Learning paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 21 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{vu2019learning,\n  title     = {Learning semantic models of data sources using probabilistic graphical models},\n  author    = {Vu, Binh and Knoblock, Craig and Pujara, Jay},\n  booktitle = {The world wide web conference},\n  pages     = {1944--1953},\n  year      = {2019},\n  url_paper = {https://binh-vu.github.io/assets/publications/www-p1944-vu.pdf}\n}\n\n
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\n \n\n \n \n \n \n \n \n An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models.\n \n \n \n \n\n\n \n Garijo, D.; Khider, D.; Ratnakar, V.; Gil, Y.; Deelman, E.; Da Silva, R. F.; Knoblock, C.; Chiang, Y.; Pham, M.; Pujara, J.; and others\n\n\n \n\n\n\n In Companion Proceedings of the 24th International Conference on Intelligent User Interfaces, pages 111–112, 2019. \n \n\n\n\n
\n\n\n\n \n \n \"An paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n  \n \n 4 downloads\n \n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@inproceedings{garijo2019intelligent,\n  title     = {An intelligent interface for integrating climate, hydrology, agriculture, and socioeconomic models},\n  author    = {Garijo, Daniel and Khider, Deborah and Ratnakar, Varun and Gil, Yolanda and Deelman, Ewa and Da Silva, Rafael Ferreira and Knoblock, Craig and Chiang, Yao-Yi and Pham, Minh and Pujara, Jay and others},\n  booktitle = {Companion Proceedings of the 24th International Conference on Intelligent User Interfaces},\n  pages     = {111--112},\n  year      = {2019},\n  url_paper = {https://dl.acm.org/doi/pdf/10.1145/3308557.3308711}\n}\n\n
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\n \n\n \n \n \n \n \n MINT: An intelligent interface for understanding the impacts of climate change on hydrological, agricultural and economic systems.\n \n \n \n\n\n \n Khider, D.; Gil, Y.; Cobourn, K. M; Deelman, E.; Duffy, C.; Ferreira da Silva, R; Kemanian, A.; Knoblock, C.; Kumar, V.; Peckham, S. D.; and others\n\n\n \n\n\n\n In AGU Fall Meeting Abstracts, volume 2019, pages PA33C–1108, 2019. \n \n\n\n\n
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@inproceedings{khider2019mint,\n  title     = {MINT: An intelligent interface for understanding the impacts of climate change on hydrological, agricultural and economic systems},\n  author    = {Khider, Deborah and Gil, Yolanda and Cobourn, Kelly M and Deelman, Ewa and Duffy, Christopher and Ferreira da Silva, R and Kemanian, Armen and Knoblock, Craig and Kumar, Vipin and Peckham, Scott Dale and others},\n  booktitle = {AGU Fall Meeting Abstracts},\n  volume    = {2019},\n  pages     = {PA33C--1108},\n  year      = {2019}\n}\n\n
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\n \n\n \n \n \n \n \n Music Latent Representation and Disentanglement for Genre Classification.\n \n \n \n\n\n \n Qasemi, E.; Vu, B.; Pham, M.; and Shbita, B.\n\n\n \n\n\n\n . 2019.\n \n\n\n\n
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@article{qasemi2019music,\n  title  = {Music Latent Representation and Disentanglement for Genre Classification},\n  author = {Qasemi, Ehsan and Vu, Binh and Pham, Minh and Shbita, Basel},\n  year   = {2019}\n}\n\n
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\n \n\n \n \n \n \n \n Integrating models through knowledge-powered data and process composition.\n \n \n \n\n\n \n Garijo, D.; Gil, Y.; Cobourn, K. M; Deelman, E.; Duffy, C.; Ferreira da Silva, R; Kemanian, A.; Knoblock, C.; Kumar, V.; Peckham, S. D.; and others\n\n\n \n\n\n\n In AGU Fall Meeting Abstracts, volume 2018, pages IN31A–02, 2018. \n \n\n\n\n
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@inproceedings{garijo2018integrating,\n  title     = {Integrating models through knowledge-powered data and process composition},\n  author    = {Garijo, Daniel and Gil, Yolanda and Cobourn, Kelly M and Deelman, Ewa and Duffy, Christopher and Ferreira da Silva, R and Kemanian, Armen and Knoblock, Craig and Kumar, Vipin and Peckham, Scott Dale and others},\n  booktitle = {AGU Fall Meeting Abstracts},\n  volume    = {2018},\n  pages     = {IN31A--02},\n  year      = {2018}\n}\n\n
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\n \n\n \n \n \n \n \n \n Mint: Model integration through knowledge-powered data and process composition.\n \n \n \n \n\n\n \n Gil, Y.; Cobourn, K.; Deelman, E.; Duffy, C.; Ferreira da Silva, R.; Kemanian, A.; Knoblock, C.; Kumar, V.; Peckham, S.; Carvalho, L. A.; and others\n\n\n \n\n\n\n . 2018.\n \n\n\n\n
\n\n\n\n \n \n \"Mint: paper\n  \n \n\n \n\n \n link\n  \n \n\n bibtex\n \n\n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n\n\n\n
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@article{gil2018mint,\n  title     = {Mint: Model integration through knowledge-powered data and process composition},\n  author    = {Gil, Yolanda and Cobourn, Kelly and Deelman, Ewa and Duffy, Chris and Ferreira da Silva, Rafael and Kemanian, Armen and Knoblock, Craig and Kumar, Vipin and Peckham, Scott and Carvalho, Lucas Augusto and others},\n  year      = {2018},\n  url_paper = {https://binh-vu.github.io/assets/publications/gil2018mint.pdf}\n}\n\n
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