Weakly Supervised Learning Algorithm to Eliminate Irrelevant Association Rules in Large Knowledge Bases

Abstract

The construction and population of large knowledge bases have been widely explored in the past few years. Many techniques were developed in order to accomplish this purpose. Association rule mining algorithms can also be used to help populate these knowledge bases. Nevertheless, analyzing the amount of association rules generated can be a challenge and time-consuming task. The technique described in this article aims to eliminate irrelevant association rules in order to facilitate the rules evaluation process. To achieve that, this article presents a weakly supervised learning technique to prune irrelevant association rules. The proposed method uses irrelevant rules already discovered in past iterations and prunes off those with the same pattern. Experiments showed that the new technique can reduce and eliminate the amount of rules by about 60%, decreasing the effort required to evaluate them.

The Curriculum Knowledge Analysis of Marxist Philosophy and System Theory Based on Association Rules

“…By analyzing the connections between various knowledge points, knowledge connections can be mined [ 5 , 6 ]. For example, literature [ 7 ] studies the application of association rules to the design of question answering systems; literature [ 8 ] studies some weakly supervised learning algorithms for association rules in large knowledge bases; literature [ 9 ] studies data mining technology and association of comprehensive application of rules; literature [ 10 ] studies methods to improve the quality of data association analysis. In addition to these studies on association analysis algorithms, more scholars have studied the application of association rule analysis in some specific fields, such as the application of association rules in English online mixed teaching [ 11 ], and the association rule model can be applied to students' thinking educational work [ 12 ]; Bi [ 13 ] studied the relationship between ideological and political teaching and mental health; Khan et al [ 14 ] studied the impact of teaching on student performance based on data mining analysis; Chen [ 15 ] proposed that association rules are used in teaching evaluation work; some scholars use association rules in the scheduling and distribution of educational resources [ 16 ].…”