projects.json

{
  "Research & Academic Projects": [
    {
      "title": "CriticalMAAS MinMod",
      "subtitle": "at USC-ISI",
      "thumbnail": "images/proj_2023_08.jpg",
      "desc": "Critical minerals underpin modern tech and the global economy. MinMod uses AI to process diverse datasets, automating grade and tonnage model creation, expediting mineral site data updates, and aiding mine deposit location.",
      "links": {
        "Project Website": "https://knowledge-computing.github.io/criticalmaas-web/",
        "Explorer": "https://minmod.isi.edu"
      },
      "year": "2023-25",
      "tags_tools": [ "Python", "PyTorch", "TTL/RDF" ],
      "tags_fields": [ "Semantic Web", "Knowledge Graphs", "Machine Learning", "NLP" ],
      "publications": [
        {
          "citation": "Knoblock, C.A., Vu, B., <b>Shbita, B.</b>, Chiang, Y.-Y., Krishna, P.P., Lin, X., Muric, G., Pyo, J., Trejo-Sheu, A., & Ye, M. (2025). <i>Exploiting LLMs and Semantic Technologies to Build a Knowledge Graph of Historical Mining Data</i>. In <i>International Semantic Web Conference</i> (pp. 451–471). Springer.",
          "links": {
            "Paper": "https://link.springer.com/chapter/10.1007/978-3-032-09530-5_26"
          }
        },
        {
          "citation": "Knoblock, C.A., Vu, B., <b>Shbita, B.</b>, Krishna, P.P., & Sharma, N. (2025). <i>Demonstrating MinMod: A Large-scale Knowledge Graph of Historical Mining Data</i>. In <i>Proceedings of the 24th International Semantic Web Conference (ISWC), Demo Track</i>.",
          "links": {
            "Paper": "https://ceur-ws.org/Vol-4085/paper65.pdf"
          }
        },
        {
          "citation": "Lin, F., Knoblock, C.A., Vu, B., <b>Shbita, B.</b>, & Chiang, Y.-Y. (2025). <i>Exploiting Polygon Metadata to Colorize Draft Maps</i>. In <i>Proceedings of the ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL)</i>."
        },
        {
          "citation": "<b>Shbita, B.</b>, Sharma N., Vu B., Lin F., & Knoblock, C.A. (2024, May). Constructing a Knowledge Graph of Historical Mining Data. In <i>6th International Workshop on Geospatial Linked Data (GeoLD) Co-located with the 21st Extended Semantic Web Conference (ESWC 2024)</i>. CEUR",
          "links": {
            "Paper": "http://usc-isi-i2.github.io/papers/shbita24-geold_ewsc.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita24-geold_ewsc-slides.pdf",
            "Code": "https://github.com/DARPA-CRITICALMAAS/ta2-minmod-data"
          }
        }
      ]
    },
    {
      "title": "Linked Maps",
      "subtitle": "at USC-ISI",
      "thumbnail": "images/proj_2020_02.jpg",
      "desc": "Extracting and semantically linking content from vast historical map archives. The initiative unlocks rich insights from maps, promoting interdisciplinary research in socio-geographic processes.",
      "links": {
        "Project Website": "https://usc-isi-i2.github.io/linked-maps/"
      },
      "year": "2019-25",
      "tags_tools": [ "Python", "PostGIS", "TTL/RDF" ],
      "tags_fields": [ "Semantic Web", "Knowledge Graphs" ],
      "publications": [
        {
          "citation": "<b>Shbita, B.</b>, Vu, B., Lin, F. & Knoblock, C.A. (2025, April). Embedding Spatial and Semantic Contexts for Geo-Entity Typing in Smart City Applications. <i>In Companion Proceedings of the ACM Web Conference 2025 (WWW '25)</i>. ACM",
          "links": {
            "Link": "https://doi.org/10.1145/3701716.3718325",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita25-webandcity_www.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita25-webandcity_www-slides.pdf",
            "Code": "https://github.com/basels/GeoEntityContextNet/",
            "Talk (video)": "https://vimeo.com/1106825516"
          }
        },
        {
          "citation": "<b>Shbita, B.</b> & Knoblock, C.A. (2024, Feb). Automatically Constructing a Geospatial Feature Taxonomy from OpenStreetMap Data. <i>In Proceedings of the 18th International Conference on Semantic Computing (ICSC 2024)</i>, (pp. 208–211). IEEE",
          "links": {
            "Link": "https://doi.org/10.1109/ICSC59802.2024.00039",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita24-icsc.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita24-icsc-slides.pdf",
            "Code": "https://github.com/basels/osm-taxonomy/"
          }
        },
        {
          "citation": "<b>Shbita, B.</b>, Knoblock, C.A., Duan, W., Chiang, Y.Y., Uhl, J.H. & Leyk, S. (2023, April). Building Spatio-Temporal Knowledge Graphs from Vectorized Topographic Historical Maps. <i>Semantic Web</i>, 14(3), (pp. 527–549). IOS Press",
          "links": {
            "Link": "https://content.iospress.com/articles/semantic-web/sw222918",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita23-swj.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita23-swj-slides.pdf",
            "Code": "https://github.com/usc-isi-i2/linked-maps"
          }
        },
        {
          "citation": "<b>Shbita, B.</b>, Knoblock, C. A., Duan, W., Chiang, Y. Y., Uhl, J. H., & Leyk, S. (2020, May). Building Linked Spatio-Temporal Data from Vectorized Historical Maps. In <i>European Semantic Web Conference</i> (pp. 409-426). Springer, Cham",
          "links": {
            "Link": "https://link.springer.com/chapter/10.1007/978-3-030-49461-2_24",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita20-eswc.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita20-eswc-slides.pdf",
            "Talk (video)": "https://vimeo.com/418737303"
          }
        }
      ]
    },
    {
      "title": "Understanding Customer Requirements - an Enterprise Knowledge Graph Approach",
      "subtitle": "with Dr. Anna Lisa Gentile, Dr. Pengyuan Li, Mr. Chad DeLuca and Dr. Guang-Jie Ren (IBM Research)",
      "thumbnail": "images/proj_2022_08.jpg",
      "desc": "Leveraging linked open data and Enterprise Knowledge Graphs to guide the extraction and formalization of customer requirements from text.",
      "links": {
        "Project Website": "https://research.ibm.com/publications/understanding-customer-requirements-an-enterprise-knowledge-graph-approach"
      },
      "year": "2022-23",
      "tags_tools": [ "Python", "PyTorch", "TTL/RDF" ],
      "tags_fields": [ "Deep Learning", "Machine Learning", "Knowledge Graphs", "NLP", "Semantic Web" ],
      "publications": [
        {
          "citation": "<b>Shbita, B.</b>, Gentile, A. L., Li, P., DeLuca, C., & Ren, G. (2023). Understanding Customer Requirements-An Enterprise Knowledge Graph Approach. In <i>Extended Semantic Web Conference</i>, pages 625–643, Cham, Springer Nature Switzerland",
          "links": {
            "Link": "https://doi.org/10.1007/978-3-031-33455-9_37",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita23-eswc.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita23-eswc-slides.pdf"
          }
        }
      ]
    },
    {
      "title": "Automated Generation of Control Concepts Annotation Rules Using Inductive Logic Programming",
      "subtitle": "with Dr. Abha Moitra (GE Research)",
      "thumbnail": "images/proj_2021_08.jpg",
      "desc": "Automatically generating classification and annotation rules for control concepts in cyber-physical systems (CPS) using semantic technologies and Inductive Logic Programming.",
      "year": "2021-22",
      "tags_tools": [ "SWI-Prolog" ,"Python", "TTL/RDF" ],
      "tags_fields": [ "Machine Learning", "Knowledge Graphs", "Inductive Logic Programming" ],
      "publications": [
        {
          "citation": "<b>Shbita, B.</b>, & Moitra, A. (2022). Automated Generation of Control Concepts Annotation Rules Using Inductive Logic Programming. In Hanus, M.; and Igarashi, A., editor(s), <i>Functional and Logic Programming</i>, pages 171–185, Cham, Springer International Publishing",
          "links": {
            "Link": "https://link.springer.com/chapter/10.1007/978-3-030-99461-7_10",
            "Paper": "http://usc-isi-i2.github.io/papers/shbita22-flops.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita22-flops-slides.pdf",
            "Talk (video)": "https://www.youtube.com/watch?v=uvISCzd5Hoc"
          }
        }
      ]
    },
    {
      "title": "Table Understanding",
      "subtitle": "at USC-ISI",
      "thumbnail": "images/proj_2019_05.jpg",
      "desc": "Extracting and Interpreting Time Series for Causal Discovery.",
      "links": {
        "Project Website": "https://usc-isi-i2.github.io/Timeseries/"
      },
      "year": "2019-21",
      "tags_tools": [ "Python", "TTL/RDF" ],
      "tags_fields": [ "Semantic Web", "Knowledge Graphs", "NLP" ],
      "publications": [
        {
          "citation": "<b>Shbita, B.</b>, Rajendran, A., Pujara, J., & Knoblock, C. A. (2019). Parsing, Representing and Transforming Units of Measure. In <i>Modeling the World’s Systems</i>",
          "links": {
            "Paper": "http://usc-isi-i2.github.io/papers/shbita19-mws.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita19-mws-slides.pdf",
            "Code": "https://github.com/basels/ccut",
            "Code (API)": "https://github.com/basels/ccut-wrapper"
          }
        }
      ]
    },
    {
      "title": "MINT (Model INTegration)",
      "subtitle": "at USC-ISI",
      "thumbnail": "images/proj_2019_09.jpg",
      "desc": "Scientific Model Integration through Knowledge-Rich Data and Process Composition.",
      "links": {
        "Project Website": "http://mint-project.info/"
      },
      "year": "2019-21",
      "tags_tools": [ "Python", "TTL/RDF" ],
      "tags_fields": [ "Semantic Web", "Knowledge Graphs" ],
      "publications": [
        {
          "citation": "<b>Shbita, B.</b>, Vu, B., Feldman, D., Pham, M., Rajendran, A., Knoblock, C. A., Pujara, J., & Chiang, Y. Y. (2019). Creating a FAIR Data Catalog to Support Scientific Modeling. In <i>Workshop on Advanced Knowledge Technologies for Science in a FAIR World (AKTS)</i>",
          "links": {
            "Paper": "http://usc-isi-i2.github.io/papers/shbita19-escience.pdf",
            "Slides": "http://usc-isi-i2.github.io/slides/shbita19-escience-slides.pdf"
          }
        }
      ]
    },
    {
      "title": "Viola @ Socialbot Grand Challenge 4",
      "subtitle": "<b>semi-finalists</b> at university challenge to advance human-computer interaction (Amazon Science)",
      "thumbnail": "images/proj_2021_07.jpg",
      "desc": "An open-domain dialogue system for spoken conversation that uses a topic-agnostic dialogue manager based on a simple generate-and-rank approach.",
      "links": {
        "Team Webpage": "https://www.amazon.science/alexa-prize/teams/viola",
        "Video (find me @ 17:36)": "https://www.youtube.com/watch?v=1Cm36w3GLKw"
      },
      "year": "2020-21",
      "tags_tools": [ "Python", "PyTorch", "TTL/RDF" ],
      "tags_fields": [ "Deep Learning", "Machine Learning", "Knowledge Graphs", "NLP", "Conversational Bot" ],
      "publications": [
        {
          "citation": "H. Cho, <b>B. Shbita</b>, K. Shenoy, S. Liu, N. Patel, H. Pindikanti, J. Lee, and J. May. (2021). Viola: A Topic Agnostic Generate-and-Rank Dialogue System. In <i>Proceedings of the 4th Alexa Prize</i>",
          "links": {
            "Link": "https://arxiv.org/abs/2108.11063",
            "Paper": "https://arxiv.org/pdf/2108.11063.pdf",
            "Slides (pre semi-final)": "http://usc-isi-i2.github.io/slides/shbita21-alexa-slides.pdf"
          }
        }
      ]
    },
    {
      "title": "Augmenting Conversational Dialogue Datasets with Commonsense and Adaptive Local Knowledge",
      "subtitle": "with Hyundong Cho (USC)",
      "thumbnail": "images/proj_2020_12.jpg",
      "desc": "Designed a dialogue dataset augmentation framework to expand multi-turn dialogue systems to incorporate external knowledge sources and ground pre-trained language models with commonsense reasoning. <br>Project conducted as part of the 'CSCI 662: Advanced Natural Language Processing' class.",
      "links": {
        "Paper": "docs/shbita_cs662_augmenting.pdf"
      },
      "year": "2020",
      "tags_tools": [ "Python", "PyTorch", "TTL/RDF" ],
      "tags_fields": [ "Deep Learning", "Machine Learning", "Knowledge Graphs", "NLP", "Conversational Bot" ]
    },
    {
      "title": "Robust Defense Against Lp-Norm-Based Attacks by Learning Robust Representations",
      "subtitle": "with Iordanis Fostiropoulos, and Myrl Marmarelis (USC)",
      "thumbnail": "images/proj_2020_06.jpg",
      "desc": "Investigated the vulnerablity of image classifiers to adversarial perturbations and proposed a new regularization objective based on the Euclidean distance between classes in the hypothetical latent space found in the features of the penultimate layer. <br>Project conducted as part of the 'CSCI 699: Adversarial Machine Learning' class.",
      "links": {
        "Paper": "docs/shbita_cs699_robust.pdf"
      },
      "year": "2020",
      "tags_tools": [ "Python", "PyTorch" ],
      "tags_fields": [ "Deep Learning", "Machine Learning", "Security" ]
    },
    {
      "title": "Music Latent Representation and Disentanglement for Genre Classification",
      "subtitle": "with Ehsan Qasemi, Binh Vu, and Minh Pham (USC)",
      "thumbnail": "images/proj_2019_12.jpg",
      "desc": "Investigated the use of unsupervised representation learning techniques to compress musical samples into a low-dimensional representation and use it for the task of music genre classification. <br>Project conducted as part of the 'CSCI 699: Representation Learning: Theory and Practice' class.",
      "links": {
        "Paper": "docs/shbita_cs699_music.pdf"
      },
      "year": "2019",
      "tags_tools": [ "Python", "PyTorch" ],
      "tags_fields": [ "Deep Learning", "Machine Learning", "Information Theory" ]
    },
    {
      "title": "Marvel and DC-Comics comic-books Knowledge Graph",
      "subtitle": "Supervisors: Prof. Pedro Szekely, Dr. Jay Pujara (USC)",
      "thumbnail": "images/proj_06.jpg",
      "desc": "Planned, designed, implemented and deployed a knowledge graph which captures Marvel and DC-Comics brands comic-book characters, comic-book issues and movies. <br>Project conducted as part of the 'INF 558/CSCI 563: Building Knowledge Graphs' class.",
      "links": {
        "Paper": "https://github.com/basels/Marvel-and-DC-Comic-books-KG/blob/master/final_submission/Basel_Shbita_Ke_Xu_project_summary.pdf",
        "Slides": "https://github.com/basels/Marvel-and-DC-Comic-books-KG/blob/master/final_submission/Basel_Shbita_Ke_Xu_final_presentation.pdf",
        "Demo (video)": "https://www.youtube.com/watch?v=MYFi45EeZV0",
        "Code": "https://github.com/basels/Marvel-and-DC-Comic-books-KG#readme"
      },
      "year": "2018",
      "tags_tools": [ "Python", "TTL/RDF" ],
      "tags_fields": [ "Semantic Web", "Knowledge Graphs" ]
    },
    {
      "title": "IPoIB Router in the Switch-X Product Family: from Firmware Design to Implementation of the Verification Environment and the Debug Tools",
      "subtitle": "under direction of Mr. Ami Marelli (Mellanox Technologies / Nvidia)",
      "thumbnail": "images/proj_02.jpg",
      "desc": "Executed an implementation and verification process of the IPolB Router firmware mechanism in the Switch-X IC, a Switch System Silicon designed by Mellanox Technologies.<br>The development was done in the firmware layer using different methods and tools, according to the company’s required methodologies.",
      "year": "2014",
      "tags_tools": [ "C", "C++", "Python", "OOP", "RTOS" ],
      "tags_fields": [ "Computer Networks", "Embedded Software" ]
    },
    {
      "title": "Physical Design of 12-bit Two's Complement Adder",
      "subtitle": "Supervisors: Prof. Yosi Shacham, Mr. Avi Efrati (Tel Aviv University)",
      "thumbnail": "images/proj_03.jpg",
      "desc": "The project included hierarchical layout design, floorplanning, manual estimations and full layout simulations.<br>The Design was implemented using the 90nm Generic Process Design Kit (gpdk090) Technology and the gsclib090 library and was designed in Assura Virtuoso software.<br>Project conducted as part of the 'Introduction to VLSI design' course.",
      "year": "2014",
      "tags_tools": [ "PSpice", "Virtuoso", "CAD" ],
      "tags_fields": [ "VLSI" ]
    },
    {
      "title": "Logic Design of Full Simplified DLX using FPGA",
      "subtitle": "Supervisors: Mrs. Liron David, Mr. Marko Markov (Tel Aviv University)",
      "thumbnail": "images/proj_04.jpg",
      "desc": "This project includes hardware design, implementation and debugging of a simple RISC processor (including data and control planes) on an FPGA development platform.<br>Used Xilinx software package and RESA software suite to code schematic and hardware description language (VHDL) designs and conduct logical simulations. <br>Project conducted as part of the 'Advanced Computer Structure Lab' course.",
      "year": "2013",
      "tags_tools": [ "VHDL", "Assembly" ],
      "tags_fields": [ "FPGA" ]
    },
    {
      "title": "Kiosk Management: A Thesis in Systems Design and Programming with Web Services",
      "subtitle": "Supervisor: Mrs. Inessa Bolotin (Israel Arts and Science Academy)",
      "thumbnail": "images/proj_05.jpg",
      "desc": "A user-friendly website for product purchasing.<br>The application features different levels of users and managers and enables them to view and add products to the catalog, prepare supplier-report and closely monitor expenses and revenues.<br>The system was used during senior high school year to raise money for students.",
      "year": "2010",
      "tags_tools": [ "C#", "ASP.NET", "JS", "CSS" ],
      "tags_fields": [ "Web", "Fullstack" ]
    },
    {
      "title": "Improving Text-Independent Speaker Identification Performance Using Gaussian Mixture Speaker Models",
      "subtitle": "under direction of Dr. David Palmer (HP Autonomy)",
      "thumbnail": "images/proj_01.jpg",
      "desc": "Studied Statistical Natural Language Processing, Automatic Speech Recognition and Machine Learning. Performed model training and testing with Python.<br>The research was conducted as part of the Research Science Institute (RSI), an intensive six-week program under sponsorship of the Center for Excellence in Education and hosted by the MIT.",
      "links": {
        "Paper": "docs/RSI_paper_Basel_Shbita.pdf"
      },
      "year": "2009",
      "tags_tools": [ "Python" ],
      "tags_fields": [ "Machine Learning", "NLP" ]
    }
  ]
}