Taxonomies and thesauri are only truly useful if their terms are appropriately indexed or tagged to content. My path to taxonomist had been as an indexer, so I always value the importance of human indexers. Nevertheless, I must acknowledge that automated indexing, also called auto-categorization, is becoming increasingly common and important.
At the most recent Taxonomy Boot Camp conference (November 6-7, in Washington, DC), a trend I discerned was the increasingly commonplace use of auto-categorization (or at least machine-aided indexing) with taxonomies. Conference presentations didn’t state auto-categorization as something new but rather sometime more matter of-the-fact, and by the way, the software vendor used in this case is so-and-so. There were also sessions on artificial intelligence and taxonomy and on leveraging taxonomy management with machine learning. There is also a lot of interest in text analytics, a field broader than auto-categorization, which justified the first Text Analytics Forum conference co-located with and immediately following Taxonomy Boot Camp (which I, unfortunately, did not have time for).
When conference speakers and others state that automated indexing has been proven repeatedly in test comparisons to be more “reliable” and more “consistent” than human/manual indexing, while true, that does not mean it is better. Human indexing is certainly not as consistent, as two trained indexers will not index exactly the same way, but the way they differ is rarely so substantial. One indexer may add an additional index term. Another indexer may index with a slightly different, but related, term. Automated indexing, on the other hand, while consistent, is not as correct. Depending on the method, it can be approximately 20% inaccurate, indexing with completely wrong terms or completely missing the most appropriate terms. That’s where “machine-aided indexing” comes in, where indexing is initially automated, but a human quickly reviews the suggested terms, adding or deleting terms as appropriate.
The primary reason for implementing automated indexing is not so much to achieve consistentindexing, but rather to achieve efficientindexing. This is because the amount of content to be indexed in many organizations is growing too fast to be kept up with by manual indexing. Publishers of external content for subscribers have also transitioned to partial automated indexes or machine-aided indexing.
While enterprise search engines do not utilize taxonomies by default (but can be configured to make use of them), auto-categorization software generally uses some form of taxonomies. Search engines can function out-of-the-box without any taxonomies or controlled vocabularies, although a search thesaurus (a.k.a synonym ring) can significantly improve search precision and recall. Auto-categorization software, on the other hand, relies on “categories,” which can be simple controlled vocabularies or hierarchical or faceted taxonomies. Thus, as auto-categorization is gaining wider adoption, the need for taxonomies to support them is also growing.
Automated indexing technologies have not advanced significantly in recent years, but there have been improvements in auto-categorization software by effectively combining more than one technology method within the same software product. The main technology methods are (1) rules-based and (2) machine-learning. Regardless of the method, automated indexing is still not fully automated. Humans are required to put in time and effort beforehand to either write or edit rules for each taxonomy term, or to provide and test training sets of sample documents to index for machine learning. These could be dedicated roles or additional tasks to be performed by the taxonomist.
Auto-categorization is also becoming more common, because software products that effectively combine taxonomy management with auto-categorization have become more established and better integrated. Although there are many organizations which continue to use distinctly separate software for each of taxonomy management and auto-categorization, organizations newer to taxonomy adoption prefer to have a single solution. Synaptica is the one major taxonomy management vendor which does not yet include fully integrated auto-categorization, and they are very actively working on incorporating the technology. I have separate chapters in my book, The Accidental Taxonomist for software for taxonomy management and software for auto-categorization, but in my second edition I ended up repeating more vendors in both sections.