Incremental Entity Summarization With Formal Concept Analysis

Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing descriptions of lead to information overload and further cause people confused. this backdrop, automatic summarization received much attention recent years, aiming select most concise typical facts that depict an brief from lengthy data. As new are continually coming, creating a compact summary quickly knowledge is challenging. To address problem, article first formulates problem proposes novel approach Incremental Entity Summarization leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove rationality our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate proposed IES-FCA can save about 8.7 percent consumption for all than non-incremental KAFCA at best. effectiveness, outperforms state-of-the-art algorithms terms $F1-measure$ , notation="LaTeX">$MAP$ notation="LaTeX">$NDCG$ .