發(fā)布時間：2018-05-27 11:35

  本文選題：馬爾可夫聚類 + 知識庫　； 參考：《計算機(jī)科學(xué)與探索》2017年04期

【摘要】：隨著面向網(wǎng)絡(luò)大數(shù)據(jù)的知識庫的不斷出現(xiàn),它們各自都包含海量的實體以及實體間的關(guān)系。然而許多有相同含義的關(guān)系并沒有統(tǒng)一名稱,針對這種情況,提出了一種基于馬爾可夫聚類(Markov cluster algorithm,MCL)的實體間關(guān)系融合方法。該方法首先計算關(guān)系間的語義相似度,然后利用關(guān)系間的語義相似度作為有邊的權(quán)重,構(gòu)建無向圖,并利用馬爾可夫聚類算法進(jìn)行聚類。實驗表明,該方法相比層次聚類和k-means聚類方法在聚類純度上有一定提高,并且更加方便使用。
[Abstract]:With the emergence of the knowledge base for network big data, each of them contains a large number of entities and their relationships. However, many relationships with the same meaning do not have a uniform name. In order to solve this problem, a new method of inter-entity relationship fusion based on Markov cluster algorithm is proposed. The method first calculates the semantic similarity between relationships, then uses the semantic similarity of relationships as the weights of edges to construct undirected graphs, and uses Markov clustering algorithm to cluster. Experiments show that compared with hierarchical clustering and k-means clustering, the proposed method can improve the purity of clustering and is more convenient to use.
【作者單位】： 中國科學(xué)院計算技術(shù)研究所網(wǎng)絡(luò)數(shù)據(jù)科學(xué)與技術(shù)重點實驗室;中國科學(xué)院大學(xué);國家計算機(jī)網(wǎng)絡(luò)應(yīng)急技術(shù)處理協(xié)調(diào)中心;
【基金】：國家自然科學(xué)基金Nos.61173008,61232010,60933005,61402442,61402022,61303244 國家重點基礎(chǔ)研究發(fā)展計劃(973計劃)Nos.2013CB329602,2014CB340405 北京市科技新星計劃項目No.Z121101002512063 北京市自然科學(xué)基金青年基金項目No.4154086 中科院醫(yī)學(xué)影像項目No.KGZD-EW-T03-2 山東省自主創(chuàng)新及成果轉(zhuǎn)化專項No.2014CGZH1103~~
【分類號】：TP391.1
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本文編號：1941903