Analysis and news
computers ‘knowing’ and ‘understanding’, there is a reason we put those words in quotes”
the relationship between Home Alone 2 and New York is that Home Alone 2 takes place in New York City. While discovery services capable
mentioned, to figure out which ones were truly needed. And even if the user or a subject matter expert noted that acne was a side effect of prednisone, there would be no way to ‘teach’ it to the discovery service. Essentially, any future user who needed the same information would have to go through the same tedious process. Semantic enrichment can also help a
discovery service recognise implications. For example, if it ‘sees’ articles about over- the-air updates of autonomous vehicles and recognises article content about autonomous vehicles being hacked, the discovery service can ‘know’ to bring it up in searches for cybersecurity, even if the word ‘cybersecurity’ is not mentioned in the article. A resource that relies on brute force keyword searches would miss these articles entirely, since it would not see the word ‘cybersecurity’ in them.
www.researchinformation.info | @researchinfo Now, while we talk about computers
‘knowing’ and ‘understanding’, there is a reason we put those words in quotes. While they are good at calculations and tracking patterns, it is important that the ideal discovery service also has an active human team monitoring and refining it, continuously ‘teaching’ what connections are significant, which aren’t, and so on. For example, a discovery service
capable of semantic search might think that because reviews of Home Alone 2 mention ‘New York’ that there is some significant connection between the film and New York State. Essentially, the discovery service might not realise that when people say ‘New York’ they often mean New York City, and a human would have to correct this, making it clear that the New York mentioned in these movie reviews is indeed New York City, and that
of perfect semantic search aren’t here yet, features like the EBSCO Discovery Service (EDS) Concept Maps are a great step forward. Concept maps are the visual representation of knowledge graphs, which leverage subject indexing to deliver precise results from across all records of a discovery service. In such systems, subject indexes are mapped across vocabularies and natural language terms, constituting an extensive semantic network of related terms and topics. Since the maps essentially show users knowledge graphs, they make it easier for users to make connections across topics. Most significantly, users can find hidden relationships between and among concepts, and discover links across fields of study, which has the benefit of making things like interdisciplinary research easier. Since, as mentioned above, concept maps add a semantic layer to subject queries, this facilitates the use of more natural language in users’ searches, which means that users can potentially learn and discover more, as they are not constricted to the use of a rigid, inflexible ‘library speak’ that they might not be proficient in, and can instead search using their own words. While the science fiction computers of
the Starship Enterprise and Jurassic Park had limitations when it came to semantic enrichment and semantic search, EDS is already trekking into the semantic frontier. Granted, it isn’t confronting potential interstellar conflict or dinosaur attacks, but it is making significant changes and advances to how research is done. The pace in which it is developing may not be quite at warp speed, but the leaps forward are coming faster and faster, and the benefit to researchers is that with each breakthrough in semantic search, researchers can go ever more boldly into their research in ways they have never gone before.
Jonathan Bresman is innovation editor and market outreach lead at EBSCO Information Services
“While we talk about
August/September 2021 Research Information
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