@article{642c6cb423f44d04a5308009b87e04e0,
  title     = "Structure-Based Partitioning of Large Concept Hierarchies",
  abstract  = "The increasing awareness of the benefits of ontologies for information processing has lead to the creation of a number of large ontologies about real-world domains. The size of these ontologies and their monolithic character cause serious problems in handling them. In other areas, e.g. software engineering, these problems are tackled by partitioning monolithic entities into sets of meaningful and mostly self-contained modules. In this paper, we suggest a similar approach for ontologies. We propose a method for automatically partitioning large ontologies into smaller modules based on the structure of the class hierarchy. We show that the structure-based method performs surprisingly well on real-world ontologies. We support this claim by experiments carried out on real-world ontologies including SUMO and the NCI cancer ontology. The results of these experiments are available online at http: //swserver. cs .vu.nl/partitioning/.",
  author    = "Heiner Stuckenschmidt and Michel Klein",
  year      = "2004",
  doi       = "10.1007/978-3-540-30475-3_21",
  volume    = "3298",
  pages     = "289--303",
  journal   = "Lecture Notes in Computer Science",
  issn      = "0302-9743",
  publisher = "Springer Verlag",
}

{"_id":"zWaFoXaN4vMHjM52X","bibbaseid":"stuckenschmidt-klein-structurebasedpartitioningoflargeconcepthierarchies-2004","downloads":0,"creationDate":"2017-06-06T19:13:39.704Z","title":"Structure-Based Partitioning of Large Concept Hierarchies","author_short":["Stuckenschmidt, H.","Klein, M."],"year":2004,"bibtype":"article","biburl":"https://raw.githubusercontent.com/KRRVU/website/master/publications/krr.bib","bibdata":{"bibtype":"article","type":"article","title":"Structure-Based Partitioning of Large Concept Hierarchies","abstract":"The increasing awareness of the benefits of ontologies for information processing has lead to the creation of a number of large ontologies about real-world domains. The size of these ontologies and their monolithic character cause serious problems in handling them. In other areas, e.g. software engineering, these problems are tackled by partitioning monolithic entities into sets of meaningful and mostly self-contained modules. In this paper, we suggest a similar approach for ontologies. We propose a method for automatically partitioning large ontologies into smaller modules based on the structure of the class hierarchy. We show that the structure-based method performs surprisingly well on real-world ontologies. We support this claim by experiments carried out on real-world ontologies including SUMO and the NCI cancer ontology. The results of these experiments are available online at http: //swserver. cs .vu.nl/partitioning/.","author":[{"firstnames":["Heiner"],"propositions":[],"lastnames":["Stuckenschmidt"],"suffixes":[]},{"firstnames":["Michel"],"propositions":[],"lastnames":["Klein"],"suffixes":[]}],"year":"2004","doi":"10.1007/978-3-540-30475-3_21","volume":"3298","pages":"289–303","journal":"Lecture Notes in Computer Science","issn":"0302-9743","publisher":"Springer Verlag","bibtex":"@article{642c6cb423f44d04a5308009b87e04e0,\n title = \"Structure-Based Partitioning of Large Concept Hierarchies\",\n abstract = \"The increasing awareness of the benefits of ontologies for information processing has lead to the creation of a number of large ontologies about real-world domains. The size of these ontologies and their monolithic character cause serious problems in handling them. In other areas, e.g. software engineering, these problems are tackled by partitioning monolithic entities into sets of meaningful and mostly self-contained modules. In this paper, we suggest a similar approach for ontologies. We propose a method for automatically partitioning large ontologies into smaller modules based on the structure of the class hierarchy. We show that the structure-based method performs surprisingly well on real-world ontologies. We support this claim by experiments carried out on real-world ontologies including SUMO and the NCI cancer ontology. The results of these experiments are available online at http: //swserver. cs .vu.nl/partitioning/.\",\n author = \"Heiner Stuckenschmidt and Michel Klein\",\n year = \"2004\",\n doi = \"10.1007/978-3-540-30475-3_21\",\n volume = \"3298\",\n pages = \"289--303\",\n journal = \"Lecture Notes in Computer Science\",\n issn = \"0302-9743\",\n publisher = \"Springer Verlag\",\n}\n\n\n","author_short":["Stuckenschmidt, H.","Klein, M."],"key":"642c6cb423f44d04a5308009b87e04e0","id":"642c6cb423f44d04a5308009b87e04e0","bibbaseid":"stuckenschmidt-klein-structurebasedpartitioningoflargeconcepthierarchies-2004","role":"author","urls":{},"metadata":{"authorlinks":{}},"downloads":0},"search_terms":["structure","based","partitioning","large","concept","hierarchies","stuckenschmidt","klein"],"keywords":[],"authorIDs":[],"dataSources":["H6xuGqu5uQ6rXhdJ4","5fFxD442nEJDpwwYr","dJmTXpbSWWjnxatYT"]}