Hubain, R.; Wilde, M. De; Hooland, S. van: Automated SKOS vocabulary design for the biopharmaceutical industry (2016)
0.00
0.0023678814 = product of:
0.0047357627 = sum of:
0.0047357627 = product of:
0.009471525 = sum of:
0.009471525 = weight(_text_:a in 5132) [ClassicSimilarity], result of:
0.009471525 = score(doc=5132,freq=8.0), product of:
0.053105544 = queryWeight, product of:
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.046056706 = queryNorm
0.17835285 = fieldWeight in 5132, product of:
2.828427 = tf(freq=8.0), with freq of:
8.0 = termFreq=8.0
1.153047 = idf(docFreq=37942, maxDocs=44218)
0.0546875 = fieldNorm(doc=5132)
0.5 = coord(1/2)
0.5 = coord(1/2)
- Abstract
- Ensuring quick and consistent access to large collections of unstructured documents is one of the biggest challenges facing knowledge-intensive organizations. Designing specific vocabularies to index and retrieve documents is often deemed too expensive, full-text search being preferred despite its known limitations. However, the process of creating controlled vocabularies can be partly automated thanks to natural language processing and machine learning techniques. With a case study from the biopharmaceutical industry, we demonstrate how small organizations can use an automated workflow in order to create a controlled vocabulary to index unstructured documents in a semantically meaningful way.
- Type
- a