Semantic Web researcher at TIB working on ontology alignment and learning, with fast-scaling LLM-era output
3
papers here
0
reviewed by Evolve
3
first-author
0
corresponding
—
works · OpenAlex
8
h-index · OpenAlex
Bibliometric figures come from OpenAlex (2026-10-17) and cover the author's entire public record, not just the papers listed here. Reproducibility signal comes from Evolve's own reviews.
Research profile
Output—
An output score is not yet computed in this snapshot. This axis would measure cumulative publication volume and its growth against field baselines; only raw work counts and an h-index of 8 are on record.
Impact—
An impact score is not yet available. This axis would measure citation-based influence — citations normalized against field and cohort — which recent 2026 preprints have not had time to accrue; only a raw h-index of 8 is recorded.
Focus—
A focus score is not yet computed. This axis would measure how concentrated output is across topics and venues; OpenAlex topic counts (topic modeling 44, semantic web 28, biomedical text mining 21) exist, but no normalized focus metric is available.
Collaboration32/100
The collaboration axis scores 32, reflecting a compact network: the OpenAlex record lists only 4 distinct co-authors in the recent window. Work is anchored in a small TIB-centered circle — Sören Auer, Jennifer D'Souza, Peio Popov, and Mahsa Sanaei — and all three Evolve-reviewed 2026 papers are first-authored without corresponding-author credit. This is a tightly concentrated collaboration pattern rather than a broad, distributed one.
Activity100/100
The activity axis scores 100, the maximum in this snapshot. OpenAlex countsByYear shows output climbing from 4 works in 2023 to 11 in 2024 and 14 in 2025, with 10 already recorded in 2026 (a partial year); recent3yWorks totals 35. All three Evolve-reviewed papers are 2026 arXiv preprints. This trajectory marks a steep, ongoing acceleration rather than a plateau.
Reproducibility—
A reproducibility score is not yet computed for this profile. The axis would measure release of code, datasets, and benchmark artifacts; Evolve's per-paper reviews tag all three 2026 papers as medium repro-readiness, but no aggregate axis score exists.
Research summary
Hamed Babaei Giglou is a researcher at the Technische Informationsbibliothek (TIB) in Germany whose work sits at the junction of the Semantic Web and natural language processing. His OpenAlex-recorded profile (h-index 8, i10-index 6) is small but sharply accelerating: recorded works rise from 4 in 2023 to 11 in 2024, 14 in 2025, and 10 already in 2026, with 35 works in the last three years. Topic modeling (44 works), Semantic Web and ontologies (28), and biomedical text mining and ontologies (21) dominate his OpenAlex topic profile, with smaller clusters in NLP techniques (7) and hate-speech detection (5). The three Evolve-reviewed papers, all first-author 2026 arXiv preprints, define a coherent agenda: stress-testing whether general sentence embeddings capture ontological reasoning (the AVA benchmark), fusing string-, KGE-, and LLM-based ontology aligners through voting ensembles (OntoAligner-Ensemble), and a controlled 13-model study of when LLM scale helps ontology learning. Evolve's verdicts repeatedly note solid, honest engineering constrained by evaluation limits — small test sets, a single fusion strategy, and synthetic labels lacking human validation. His co-author network is compact (4 distinct co-authors recorded), anchored by Sören Auer, Jennifer D'Souza, Peio Popov, and Mahsa Sanaei.
Collaboration pattern
Hamed Babaei Giglou's recorded network is compact: 4 distinct co-authors in the recent OpenAlex window, scoring 32 on the collaboration axis. His recurring partners — Sören Auer, Jennifer D'Souza, Peio Popov, and Mahsa Sanaei — point to a TIB-centered working group. All three Evolve-reviewed 2026 papers are first-authored (none corresponding), consistent with a hands-on contributor operating inside a small, stable team rather than a large distributed consortium.
Research keywords
Highlights
AVA benchmark: general embeddings largely fail ontological reasoning
OntoAligner-Ensemble: voting fusion across heterogeneous aligners
Controlled 13-model study of LLM scale in ontology learning
Timeline
- 2018Earliest works recorded in the OpenAlex snapshot (1 work recorded this year).
- 2021Output ticks up to 4 recorded works, an early sign of steady production.
- 20234 recorded works — the last year before the sharp acceleration.
- 2024Recorded works jump to 11, nearly triple the prior year.
- 2025Most productive year on record: 14 works recorded in OpenAlex.
- 202610 works recorded year-to-date; three first-author arXiv preprints (embeddings vs. ontological reasoning, OntoAligner-Ensemble, LLM scale for ontology learning) enter the Evolve catalog.
Papers in the Evolve catalog
Frequent co-authors
About this profile
This profile is AI-structured by Evolve.Pub from public bibliographic data (OpenAlex) and Evolve's own paper reviews. It is not written or endorsed by the researcher unless the profile is claimed. Radar dimensions are computed from real bibliometrics with fixed formulas — raw figures are shown alongside; missing dimensions mean insufficient data, never zero. Errors? Claim the profile to correct or hide it.
- All bibliometrics are OpenAlex-derived (identity: openalex); citation coverage for very recent 2026 arXiv preprints is likely incomplete.
- 2026 work counts are partial-year figures recorded to date and will likely change.
- OpenAlex topic counts (e.g., topic modeling 44) span the full OpenAlex record and exceed the three papers reviewed by Evolve; qualitative verdicts apply only to those three.
- Identity is resolved from public bibliographic data (OpenAlex profile tied to ORCID 0000-0003-3758-1454); no biographical information beyond institutional affiliation is included.