Optimization scheme for academic semantic search system
For academic research scenarios, Vespa.ai provides the following semantic search optimization strategies:
- multivector characterization: Supports a single paper containing title vectors, abstract vectors and full text vectors at the same time, capturing semantics at different levels of granularity
- Hybrid Search Architecture: Combining traditional BM25 keyword search with the latest vector similarity calculation
- Optimization of resultant fine-tuning: Structured features such as number of citations, year of publication, etc. can be added to enhance the relevance of the results.
Specific implementation programs:
- The data processing phase of the paper uses specialized models such as SciBERT to generate domain-related vectors
- Set multivector fields when configuring schema, for example:
"fields": [
{ "name": "title_embedding", "type": "tensor(d[768])" },
{ "name": "abstract_embedding", "type": "tensor(d[768])" }
] - Designing hybrid queries YQL:
"yql": "select * from papers where (userQuery() OR nearestNeighbor(title_embedding, query_embedding)) AND year > 2018"
Effectiveness validation: in the COVID-19 research dataset test, this scheme improves the recall of relevant papers by 45%, which is particularly suitable for literature research in emerging fields.
This answer comes from the articleVespa.ai: an open source platform for building efficient AI search and recommendation systemsThe
May not be reproduced without permission:AI productivity tools " How to Optimize Academic Paper Retrieval Systems for Accurate Semantic Search?
