{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T08:52:37Z","timestamp":1770540757486,"version":"3.49.0"},"reference-count":31,"publisher":"Association for Computing Machinery (ACM)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Proc. VLDB Endow."],"published-print":{"date-parts":[[2014,3]]},"abstract":"<jats:p>Querying complex graph databases such as knowledge graphs is a challenging task for non-professional users. Due to their complex schemas and variational information descriptions, it becomes very hard for users to formulate a query that can be properly processed by the existing systems. We argue that for a user-friendly graph query engine, it must support various kinds of transformations such as synonym, abbreviation, and ontology. Furthermore, the derived query results must be ranked in a principled manner.<\/jats:p>\n          <jats:p>\n            In this paper, we introduce a novel framework enabling &lt;u&gt;s&lt;\/u&gt;chema&lt;u&gt;l&lt;\/u&gt;ess and &lt;u&gt;s&lt;\/u&gt;tructure&lt;u&gt;l&lt;\/u&gt;ess graph &lt;u&gt;q&lt;\/u&gt;uerying (SLQ), where a user need not describe queries precisely as required by most databases. The query engine is built on a set of transformation functions that automatically map keywords and linkages from a query to their matches in a graph. It automatically\n            <jats:italic>learns<\/jats:italic>\n            an effective ranking model,\n            <jats:italic>without<\/jats:italic>\n            assuming manually labeled training examples, and can efficiently return top ranked matches using graph sketch and belief propagation. The architecture of SLQ is elastic for \"plug-in\" new transformation functions and query logs. Our experimental results show that this new graph querying paradigm is promising: It identifies high-quality matches for both keyword and graph queries over real-life knowledge graphs, and outperforms existing methods significantly in terms of effectiveness and efficiency.\n          <\/jats:p>","DOI":"10.14778\/2732286.2732293","type":"journal-article","created":{"date-parts":[[2015,5,12]],"date-time":"2015-05-12T15:37:52Z","timestamp":1431445072000},"page":"565-576","source":"Crossref","is-referenced-by-count":55,"title":["Schemaless and structureless graph querying"],"prefix":"10.14778","volume":"7","author":[{"given":"Shengqi","family":"Yang","sequence":"first","affiliation":[{"name":"University of California, Santa Barbara"}]},{"given":"Yinghui","family":"Wu","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara"}]},{"given":"Huan","family":"Sun","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara"}]},{"given":"Xifeng","family":"Yan","sequence":"additional","affiliation":[{"name":"University of California, Santa Barbara"}]}],"member":"320","published-online":{"date-parts":[[2014,3]]},"reference":[{"key":"e_1_2_1_1_1","unstructured":"Dbpedia. dbpedia.org.  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