Expired50% confidenceFactExact time
Semantic matching is based on the semantic vector representation of text and uses metrics such as cosine similarity to determine the degree of semantic proximity.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
7/2/2026
First Seen
This claim has expired. It is kept for the record and no longer describes the current state.
Valid until: 9/30/2026(expired)
Sources
Related Claims
UnverifiedSemantic retrieval is based on mathematical metrics such as cosine similarity rather than simple keyword matching.83% similarUnverifiedSemantic search converts text into high-dimensional vectors and finds the most relevant conclusions by computing cosine similarity, allowing queries phrased in different ways to match semantically equivalent results.77% similarUnverifiedSynonym normalization can be achieved by using sentence embedding models such as Sentence-BERT to map words into dense vectors, and then computing semantic proximity via cosine similarity.75% similarVerified余弦相似度是衡量两个向量方向接近程度的指标，值域在-1到1之间，越接近1表示语义越相似74% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/43482API
curl https://kongchang.com/api/v1/knowledge/claims/43482MCP
get_claim(id=43482)