{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,3,2]],"date-time":"2024-03-02T07:59:09Z","timestamp":1709366349764},"reference-count":14,"publisher":"World Scientific Pub Co Pte Ltd","issue":"03","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Int. J. Artif. Intell. Tools"],"published-print":{"date-parts":[[2008,6]]},"abstract":"<jats:p>We describe an algorithm and experiments for inference of edge replacement graph grammars. This method generates candidate recursive graph grammar productions based on isomorphic subgraphs which overlap by two nodes. If there is no edge between the two overlapping nodes, the method generates a recursive graph grammar production with a virtual edge. We guide the search for the graph grammar based on the size of the grammar and the portion of the graph described by the grammar. We show experiments where we generate graphs from known graph grammars, use our method to infer the grammar from the generated graphs, and then measure the error between the original and inferred grammars. Experiments show that the method performs well on several types of grammars, and specifically that error decreases with increased numbers of unique labels in the graph.<\/jats:p>","DOI":"10.1142\/s0218213008004047","type":"journal-article","created":{"date-parts":[[2008,6,24]],"date-time":"2008-06-24T09:38:40Z","timestamp":1214300320000},"page":"539-554","source":"Crossref","is-referenced-by-count":2,"title":["INFERENCE OF EDGE REPLACEMENT GRAPH GRAMMARS"],"prefix":"10.1142","volume":"17","author":[{"given":"JACEK P.","family":"KUKLUK","sequence":"first","affiliation":[{"name":"Department of Radiation Oncology, Dana-Farber\/Brigham and Women's Cancer Center, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA"}]},{"given":"LAWRENCE B.","family":"HOLDER","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Washington State University, Box 642752, Pullman, WA 99164, USA"}]},{"given":"DIANE J.","family":"COOK","sequence":"additional","affiliation":[{"name":"School of Electrical Engineering and Computer Science, Washington State University, Box 642752, Pullman, WA 99164, USA"}]}],"member":"219","published-online":{"date-parts":[[2011,11,21]]},"reference":[{"key":"rf1","doi-asserted-by":"publisher","DOI":"10.1016\/0167-8655(83)90033-8"},{"key":"rf2","doi-asserted-by":"crossref","first-page":"231","DOI":"10.1613\/jair.43","volume":"1","author":"Cook D.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"rf3","doi-asserted-by":"publisher","DOI":"10.1109\/5254.850825"},{"key":"rf4","doi-asserted-by":"publisher","DOI":"10.1016\/S0303-2647(97)00037-3"},{"key":"rf5","doi-asserted-by":"crossref","unstructured":"E.\u00a0Jeltsch and H.\u00a0Kreowski, Graph-Grammars, Lecture Notes in Computer Science\u00a0532 (1990)\u00a0pp. 461\u2013474, DOI: 10.1007\/BFb0017406.","DOI":"10.1007\/BFb0017406"},{"key":"rf7","doi-asserted-by":"publisher","DOI":"10.1142\/S0218213004001429"},{"key":"rf8","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3975(97)86542-5"},{"key":"rf11","doi-asserted-by":"publisher","DOI":"10.1126\/science.298.5594.824"},{"key":"rf12","doi-asserted-by":"crossref","unstructured":"S.\u00a0Neidle (ed.), Oxford Handbook of Nucleic Acid Structure (Oxford University Press, 1999)\u00a0p. 326.","DOI":"10.1093\/oso\/9780198500384.001.0001"},{"key":"rf13","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1613\/jair.374","volume":"7","author":"Nevill-Manning G.","journal-title":"Journal of Artificial Intelligence Research"},{"key":"rf14","doi-asserted-by":"publisher","DOI":"10.1038\/nature03607"},{"key":"rf15","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.0406278102"},{"key":"rf16","series-title":"Lecture Notes in Artificial Intelligence","first-page":"281","volume":"2835","author":"Oates T.","year":"2003"},{"key":"rf17","doi-asserted-by":"publisher","DOI":"10.1016\/S0304-3975(97)00014-5"}],"container-title":["International Journal on Artificial Intelligence Tools"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.worldscientific.com\/doi\/pdf\/10.1142\/S0218213008004047","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,2,27]],"date-time":"2024-02-27T20:34:10Z","timestamp":1709066050000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.worldscientific.com\/doi\/abs\/10.1142\/S0218213008004047"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2008,6]]},"references-count":14,"journal-issue":{"issue":"03","published-online":{"date-parts":[[2011,11,21]]},"published-print":{"date-parts":[[2008,6]]}},"alternative-id":["10.1142\/S0218213008004047"],"URL":"https:\/\/doi.org\/10.1142\/s0218213008004047","relation":{},"ISSN":["0218-2130","1793-6349"],"issn-type":[{"value":"0218-2130","type":"print"},{"value":"1793-6349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2008,6]]}}}