Titles, Not Tokens: Making Job Matching Explainable with STR + KGs
The big idea Job titles are messy: “Managing Director” and “CEO” share zero tokens yet often mean the same thing, while “Director of Sales” and “VP Marketing” are different but related. Traditional semantic similarity (STS) rewards look‑alikes; real hiring needs relatedness (STR)—associations that capture hierarchy, function, and context. A recent study proposes a hybrid pipeline that pairs fine‑tuned Sentence‑BERT embeddings with a skill‑level Knowledge Graph (KG), then evaluates models by region of relatedness (low/medium/high) instead of only global averages. The punchline: this KG‑augmented approach is both more accurate where it matters (high‑STR) and explainable—it can show which skills link two titles. ...
