Semantic vs Keyword Search in Web Archives: Which One Actually Works?

Web archive search has been stuck in keyword mode for decades. You type exact terms, apply boolean operators, maybe filter by date or filetype — and hope you guessed the right vocabulary. When you miss, you get nothing. When the archive's metadata is messy, you get noise. Semantic search promises a better way: search by meaning, not just matching strings. But does it actually work for historical web content?

Keyword search excels when you know exactly what you are looking for: a specific phrase, a document title, a person's name. It is fast, predictable, and deterministic. The challenge comes when terminology shifts, when you are exploring broadly, or when metadata is inconsistent. A keyword search for "climate adaptation policy 2015" only finds documents that use those exact terms — it misses synonyms, related concepts, and documents that discuss the topic without that phrasing.

Semantic search uses embeddings — mathematical representations of meaning — to match queries with conceptually similar content, even when keywords do not overlap. In theory, searching "how cities prepare for flooding" could surface documents about stormwater management, resilience planning, and disaster mitigation, regardless of exact wording. For web archives, this could be transformative — if the underlying metadata and content quality support it.

The reality: semantic search works well when document text is clean, complete, and contextually rich. It struggles when metadata is sparse, when snapshots are fragmented, or when the corpus mixes languages, formats, and quality levels. Web archives present all of these challenges simultaneously. A PDF from 2008 might have excellent OCR; a snapshot from 2012 might be broken HTML with missing CSS; a scanned image might have no text at all.

Hybrid approaches show the most promise. Start with semantic search to identify conceptually relevant candidates, then apply keyword filters to tighten precision. Or use keyword search to retrieve a rough set, then re-rank semantically to surface the best matches. This combines the reliability of exact matching with the flexibility of conceptual similarity.

Arkibber is exploring semantic layers as a complement to traditional search, particularly for clustering related documents, surfacing conceptually similar items, and helping researchers discover adjacent material they would not have thought to query. The goal is not to replace keyword search but to augment it — giving users another lens when exploration matters more than precision.

Practical advice: use keyword search when you have strong priors about terminology, known document titles, or specific entities. Use semantic search (when available) for exploratory research, cross-domain discovery, or when standard queries return too few results. And always verify: semantic systems can confidently return plausible-but-wrong results, especially on noisy data.

Looking ahead, expect more archives to layer semantic retrieval on top of traditional indexes. Embedding models are getting better at handling messy, historical text. Metadata normalization helps by giving semantic systems cleaner inputs. And as LLM-based agents become common in research workflows, they will rely on semantic search to power smarter question-answering over archival corpora.

The key limitation: semantic search does not fix bad data. If the underlying archive has missing dates, broken links, or mis-labeled media types, semantic ranking just surfaces a better-sorted version of the same mess. Metadata quality remains foundational — semantic search is a multiplier on good infrastructure, not a substitute for it.

For teams building on web archives, invest in both: maintain fast, reliable keyword indexes for precision tasks, and experiment with semantic layers for discovery and clustering. Offer users clear controls so they understand which mode they are in and can switch as their task evolves.

Bottom line: keyword search is not going away. It is too fast, too controllable, and too well-understood. But semantic search is becoming a valuable complement, especially for large-scale exploration and conceptual discovery. Use both, understand their trade-offs, and design workflows that let researchers toggle between precision and serendipity as needed.