东南大学学报(自然科学版)
東南大學學報(自然科學版)
동남대학학보(자연과학판)
JOURNAL OF SOUTHEAST UNIVERSITY
2010年
2期
270-274
,共5页
资源描述框架(RDF)%关键词查询%RDF句子%语义网
資源描述框架(RDF)%關鍵詞查詢%RDF句子%語義網
자원묘술광가(RDF)%관건사사순%RDF구자%어의망
resource description framework(RDF)%keyword query%RDF sentence%semantic web
在建立关键词倒排索引和路径索引的基础上,提出一个利用量化均衡规则和等距规则的启发式查询算法,并按照查询结果的大小排序返回最相关的前k个结果.通过建模RDF数据为RDF句子图,将文本信息封装到句子节点,同时将查询结果建模为包括所有查询关键词并且叶节点是关键词节点的无根树,将关键词查询问题转化为斯坦纳树问题.假设RDF句子图包括n个节点,最坏情况下索引占用的空间是3n~2.假设关键词节点数为k,查询算法的时间复杂度为O(kn).该方法不需要依赖RDF数据的模式信息,支持对数据中的属性和关系名进行关键词查询.实验证明该方法能够快速而有效地实现RDF数据的关键词查询.
在建立關鍵詞倒排索引和路徑索引的基礎上,提齣一箇利用量化均衡規則和等距規則的啟髮式查詢算法,併按照查詢結果的大小排序返迴最相關的前k箇結果.通過建模RDF數據為RDF句子圖,將文本信息封裝到句子節點,同時將查詢結果建模為包括所有查詢關鍵詞併且葉節點是關鍵詞節點的無根樹,將關鍵詞查詢問題轉化為斯坦納樹問題.假設RDF句子圖包括n箇節點,最壞情況下索引佔用的空間是3n~2.假設關鍵詞節點數為k,查詢算法的時間複雜度為O(kn).該方法不需要依賴RDF數據的模式信息,支持對數據中的屬性和關繫名進行關鍵詞查詢.實驗證明該方法能夠快速而有效地實現RDF數據的關鍵詞查詢.
재건립관건사도배색인화로경색인적기출상,제출일개이용양화균형규칙화등거규칙적계발식사순산법,병안조사순결과적대소배서반회최상관적전k개결과.통과건모RDF수거위RDF구자도,장문본신식봉장도구자절점,동시장사순결과건모위포괄소유사순관건사병차협절점시관건사절점적무근수,장관건사사순문제전화위사탄납수문제.가설RDF구자도포괄n개절점,최배정황하색인점용적공간시3n~2.가설관건사절점수위k,사순산법적시간복잡도위O(kn).해방법불수요의뢰RDF수거적모식신식,지지대수거중적속성화관계명진행관건사사순.실험증명해방법능구쾌속이유효지실현RDF수거적관건사사순.
Based on the keyword inverted-list index and the path index,a heuristic searching algorithm is proposed.The algorithm uses the cost-balanced strategy and the equi-distance strategy to find the top-k answers.Resource description framework (RDF) data is modeled as an RDF sentence graph,and all text information is encapsulated by the sentence nodes.An answer to a keyword query is an RDF sentence tree which contains all the keywords,and all the leaf nodes are relevant to keywords.Therefore,to find a shortest answer tree is a Steiner tree problem.Supposing that there are n nodes in RDF sentence graph,the index space would be 3n~2 in the worst case.Supposing that there are k relevant nodes,the time complexity would be O(kn).The proposed approach supports keywords that match attributes and relation contained in the data,without the information of the RDF data schema.The experimental results show that the approach is feasible and effective.