bibliography.bib

@InProceedings{Alexiev2016-rdfpuml-rdf2rml,
  author       = {Vladimir Alexiev},
  title        = {{RDF by Example: rdfpuml for True RDF Diagrams, rdf2rml for R2RML Generation}},
  booktitle    = {{Semantic Web in Libraries (SWIB)}},
  year         = 2016,
  month        = nov,
  address      = {Bonn, Germany},
  url_Slides   = {http://rawgit2.com/VladimirAlexiev/my/master/pres/20161128-rdfpuml-rdf2rml/index.html},
  url          = {http://rawgit2.com/VladimirAlexiev/my/master/pres/20161128-rdfpuml-rdf2rml/index-full.html},
  keywords     = {RDF, visualization, PlantUML, cultural heritage, NLP, NIF, EHRI, R2RML, generation, model-driven, RDF by Example, rdfpuml, rdf2rml},
  url_Video    = {https://youtu.be/4WoYlaGF6DE},
  howpublished = {presentation},
  abstract     = {RDF is a graph data model, so the best way to understand RDF data schemas (ontologies, application profiles, RDF shapes) is with a diagram. Many RDF visualization tools exist, but they either focus on large graphs (where the details are not easily visible), or the visualization results are not satisfactory, or manual tweaking of the diagrams is required. We describe a tool *rdfpuml* that makes true diagrams directly from Turtle examples using PlantUML and GraphViz. Diagram readability is of prime concern, and rdfpuml introduces various diagram control mechanisms using triples in the puml: namespace. Special attention is paid to inlining and visualizing various Reification mechanisms (described with PRV). We give examples from Getty CONA, Getty Museum, AAC (mappings of museum data to CIDOC CRM), Multisensor (NIF and FrameNet), EHRI (Holocaust Research into Jewish social networks), Duraspace (Portland Common Data Model for holding metadata in institutional repositories), Video annotation. If the example instances include SQL queries and embedded field names, they can describe a mapping precisely. Another tool *rdf2rdb* generates R2RML transformations from such examples, saving about 15x in complexity.},
}

@InProceedings{Alexiev-ENDORSE-2023,
  author       = {Vladimir Alexiev},
  title        = {{Generation of Declarative Transformations from Semantic Models}},
  booktitle    = {{European Data Conference on Reference Data and Semantics (ENDORSE 2023)}},
  year         = 2023,
  month        = mar,
  url_PPT      = {https://docs.google.com/presentation/d/1JCMQEH8Tw_F-ta6haIToXMLYJxQ9LRv6/edit},
  url          = {https://drive.google.com/open?id=1Cq5o9th_P812paqGkDsaEomJyAmnypkD},
  url_Slides   = {https://op.europa.eu/documents/10157494/12134844/DAY1-TRACK2-16.35-16.50-VladimirAlexiev_FORPUB.pdf},
  url_Video    = {https://youtu.be/yL5nI_3ccxs},
  keywords     = {semantic model, semantic data integration, ETL, semantic conversion, declarative approaches, PlantUML, R2RML, generation, model-driven, RDF by Example, rdfpuml, rdf2rml},
  abstract     = {The daily work of the Knowledge Graph Solutions group at Ontotext involves KG building activities such as investigating data standards and datasets, ontology engineering, harmonizing data through semantic models, converting or virtualizing data to semantic form, entity matching, semantic text enrichment, etc. Semantic pipelines have a variety of desirable properties, of which maintainability and consistency of the various artefacts are some of the most important ones. Despite significant recent progress (eg in the KG Building W3C community group), semantic conversion still remains one of the difficult steps. We favor generation of semantic transformations from semantic models that are both sufficiently precise, easily understandable, can be used to generate diagrams, and are valid RDF to allow processing with RDF tools. We call this approach "RDF by Example" and have developed a set of open source tools at https://github.com/VladimirAlexiev/rdf2rml. This includes "rdfpuml" for generating diagrams, "rdf2rml" for generating R2RML for semantization of relational data and ONTOP virtualization, "rdf2sparql" for semantization of tabular data with Ontotext Refine or TARQL. We describe our approach and illustrate it with complex and high-performance transformations in a variety of domains, such as company data and NIH research grants.},
}

@InProceedings{Alexiev2012-CRM-search,
  author       = {Vladimir Alexiev},
  title        = {{Implementing CIDOC CRM Search Based on Fundamental Relations and OWLIM Rules}},
  booktitle    = {{Workshop on Semantic Digital Archives (SDA 2012), part of International Conference on Theory and Practice of Digital Libraries (TPDL 2012)}},
  year         = 2012,
  volume       = 912,
  month        = sep,
  address      = {Paphos, Cyprus},
  publisher    = {CEUR WS},
  url          = {http://rawgit2.com/VladimirAlexiev/my/master/pubs/Alexiev2012-CRM-FR-search.pdf},
  url_Slides   = {http://rawgit2.com/VladimirAlexiev/my/master/pubs/Alexiev2012-CRM-Search-presentation.pdf},
  url_Published= {http://ceur-ws.org/Vol-912/paper8.pdf},
  keywords     = {cultural heritage, ontology, CIDOC CRM, semantic search, Fundamental Relations, GraphDB, semantic repository, inference, performance, ResearchSpace},
  keywords     = {cultural heritage, semantic technology, ontology, CIDOC CRM, semantic search, Fundamental Concepts, Fundamental Relations, ResearchSpace},
  abstract     = {The CIDOC CRM provides an ontology for describing entities, properties and relationships appearing in cultural heritage (CH) documentation, history and archeology. CRM promotes shared understanding by providing an extensible semantic framework that any CH information can be mapped to. CRM data is usually represented in semantic web format (RDF) and comprises complex graphs of nodes and properties. An important question is how a user can search through such complex graphs, since the number of possible combinations is staggering. One approach "compresses" the semantic network by mapping many CRM entity classes to a few "Fundamental Concepts" (FC), and mapping whole networks of CRM proper-ties to fewer "Fundamental Relations" (FR). These FC and FRs serve as a "search index" over the CRM semantic web and allow the user to use a simpler query vocabulary. We describe an implementation of CRM FR Search based on OWLIM Rules, done as part of the ResearchSpace (RS) project. We describe the technical de-tails, problems and difficulties encountered, benefits and disadvantages of using OWLIM rules, and preliminary performance results. We provide implementation experience that can be valuable for further implementation, definition and maintenance of CRM FRs.},
}

@InProceedings{Alexiev2013-CRM-reasoning,
  author       = {Vladimir Alexiev and Dimitar Manov and Jana Parvanova and Svetoslav Petrov},
  title        = {{Large-scale Reasoning with a Complex Cultural Heritage Ontology (CIDOC CRM)}},
  booktitle    = {Workshop Practical Experiences with CIDOC CRM and its Extensions (CRMEX 2013) at TPDL 2013},
  year         = 2013,
  volume       = 1117,
  month        = sep,
  address      = {Valetta, Malta},
  publisher    = {CEUR WS},
  url          = {http://ceur-ws.org/Vol-1117/paper8.pdf},
  url_Slides   = {http://rawgit2.com/VladimirAlexiev/my/master/pubs/Alexiev2013-CRM-reasoning-slides.ppt},
  url_Preprint = {http://rawgit2.com/VladimirAlexiev/my/master/pubs/Alexiev2013-CRM-reasoning.pdf},
  keywords     = {cultural heritage, ontology, CIDOC CRM, semantic search, Fundamental Relations, GraphDB, semantic repository, inference, performance, ResearchSpace},
  abstract     = {The CIDOC Conceptual Reference Model (CRM) is an important ontology in the Cultural Heritage (CH) domain. CRM is intended mostly as a data integration mechanism, allowing reasoning and discoverability across diverse CH sources represented in CRM. CRM data comprises complex graphs of nodes and properties. An important question is how to search through such complex graphs, since the number of possible combinations is staggering. One answer is the "Fundamental Relations" (FR) approach that maps whole networks of CRM properties to fewer FRs, serving as a "search index" over the CRM semantic web. We present performance results for an FR Search implementation based on OWLIM. This search works over a significant CH dataset: almost 1B statements resulting from 2M objects of the British Museum. This is an exciting demonstration of large-scale reasoning with real-world data over a complex ontology (CIDOC CRM). We present volumetrics, hardware specs, compare the numbers to other repositories hosted by Ontotext, performance results, and compare performance of a SPARQL implementation.},
}

@InProceedings{garbaczReasoningFIBOOntology2022,
  author       = {Paweł Garbacz and Elisa F. Kendall},
  title        = {Reasoning in the FIBO ontology - A challenge},
  booktitle    = {{2nd Semantic Reasoning Evaluation Challenge and 3rd SeMantic Answer Type, Relation and Entity Prediction Tasks Challenge
    (SemREC/SMART@ISWC)}},
  year         = 2022,
  url          = {https://ceur-ws.org/Vol-3337/semrec_paper1.pdf},
  howpublished = {short paper},
}

@InCollection{allemangInfrastructureCollaborativeOntology2021,
  author       = {Allemang, Dean and Garbacz, Pawel and Grądzki, Przemysław and Kendall, Elisa and Trypuz, Robert},
  title        = {{An Infrastructure for Collaborative Ontology Development: Lessons Learned from Developing the Financial Industry Business Ontology
    (FIBO)}},
  booktitle    = {{Formal Ontology in Information Systems: Proceedings of the Twelfth International Conference (FOIS 2021)}},
  publisher    = {{IOS Press}},
  year         = 2021,
  editor       = {Neuhaus, Fabian and Brodaric, Boyan},
  volume       = 344,
  series       = {Frontiers in {{Artificial Intelligence}} and {{Applications}}},
  url          = {https://ebooks.iospress.nl/doi/10.3233/FAIA210375},
  shorttitle   = {An {{Infrastructure}} for {{Collaborative Ontology Development}}},
  date         = {2021-12-23},
  doi          = {10.3233/FAIA210375},
  urldate      = {2023-09-11},
  abstract     = {Collaborative development of a shared or standardized ontology presents unique issues in workflow, version control, testing, and
    quality control. These challenges are similar to challenges faced in large-scale collaborative software development. We have taken this idea as
    the basis of a collaborative ontology development platform based on familiar software tools, including Continuous Integration platforms, version
    control systems, testing platforms, and review workflows. We have implemented these using open-source versions of each of these tools, and
    packaged them into a full-service collaborative platform for collaborative ontology development. This platform has been used in the development of
    FIBO, the Financial Industry Business Ontology, an ongoing collaborative effort that has been developing and maintaining a set of ontologies for
    over a decade. The platform is open-source and is being used in other projects beyond FIBO. We hope to continue this trend and improve the state
    of practice of collaborative ontology design in many more industries.},
  isbn         = {978-1-64368-248-8 978-1-64368-249-5},
}