${\cal R}$, RC attempts at linking entity pairs between two entity lists under the relation ${\cal R}$. To accomplish the RC goals, we propose to formulate search queries for each query entity $\alpha$ based on some auxiliary information, so that to detect its target entity $\beta$ from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC." /> ${\cal R}$, RC attempts at linking entity pairs between two entity lists under the relation ${\cal R}$. To accomplish the RC goals, we propose to formulate search queries for each query entity $\alpha$ based on some auxiliary information, so that to detect its target entity $\beta$ from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC." /> ${\cal R}$, RC attempts at linking entity pairs between two entity lists under the relation ${\cal R}$. To accomplish the RC goals, we propose to formulate search queries for each query entity $\alpha$ based on some auxiliary information, so that to detect its target entity $\beta$ from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC." /> CoRE: A Context-Aware RelationExtraction Method for Relation Completion
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Issue No.04 - April (2014 vol.26)
pp: 836-849
Xiaofang Zhou , Sch. of Inf. Technol. & Electr. Eng., Univ. of Queensland, Brisbane, QLD, Australia
ABSTRACT
We identify relation completion (RC) as one recurring problem that is central to the success of novel big data applications such as Entity Reconstruction and Data Enrichment. Given a semantic relation ℜ, RC attempts at linking entity pairs between two entity lists under the relation ℜ. To accomplish the RC goals, we propose to formulate search queries for each query entity α based on some auxiliary information, so that to detect its target entity β from the set of retrieved documents. For instance, a pattern-based method (PaRE) uses extracted patterns as the auxiliary information in formulating search queries. However, high-quality patterns may decrease the probability of finding suitable target entities. As an alternative, we propose CoRE method that uses context terms learned surrounding the expression of a relation as the auxiliary information in formulating queries. The experimental results based on several real-world web data collections demonstrate that CoRE reaches a much higher accuracy than PaRE for the purpose of RC.
INDEX TERMS
Educational institutions, Semantics, Context, Web search, Adaptation models, Web pages, Data mining,relation query expansion, Context-aware relation extraction, relation completion
CITATION
Xiaofang Zhou, "CoRE: A Context-Aware Relation Extraction Method for Relation Completion", IEEE Transactions on Knowledge & Data Engineering, vol.26, no. 4, pp. 836-849, April 2014, doi:10.1109/TKDE.2013.148
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