Feature g
unique in a way, with its legal framework for pre-competitive collaboration bringing together a cross-industry project team, and it takes quite a while to build up that trust. But once you have done it, and the sharing begins, it’s like a eureka moment. You can make so much of a difference together. The value of that is just huge. Semantic enrichment was initially brought to Pistoia as an idea by Pfizer. They were already doing semantic enrichment, but they recognised it would be fantastic if this was done across pharma, and if we worked together on this problem, rather than trying to solve it individually.’ The project has focused on the
development of new assay ontologies for ADME, PD and drug safety. The first phase worked on ensuring everyone was using the same terms, and then the second phase built relationship maps on those terms that had been agreed upon and published in the BioAssay Ontology. The project then developed an exemplar, which showed what it would look like as part of an ELN workflow: starting with an unstructured text, using semantic enrichment and an API to connect with the standards used across the industry, and leaving at the end an enriched text. The next is looking at breadth rather than depth, working on the data model for an experiment. Companies are already reporting the
realisation of value from the project – and, as Whittick explained: ‘Any company who thinks they are going to do it on their own is very short sighted. They can’t reap the same value doing it on their own. They are spending resources trying to solve a problem they don’t need to, because we’re solving it together.’
Drawing on different perspectives While collaboration within an industry is important for developing, sharing, and building standards, in other situations vocabularies will want to include as wide a range of perspectives as possible. This can be an important part of making things more equitable, ensuring different groups and points of view are represented, and their associated data and documents are findable. While this is increasingly recognised for many historical collections, it can nonetheless be a divisive issue when it comes to subjects that cross political divides. As Hlava pointed out, choices in
terminology can lead to semantic censorship. While it may not be a conscious choice, in can lead to a bias in the terms that are taken and the directions subsequent analysis takes: ‘Semantic censorship is pervasive, wherein we want
8 Research Information Spring 2022
to make sure that whatever term is applied could be used by all the communities that generate or need to find or discover information. For example, when the Covid pandemic hit I decided it would be really helpful and a good service to our clients – and anybody who wanted it – to build a taxonomy of Covid terms, and I very quickly came up with about 20 synonyms for what we now generally call Covid-19, as well as related terms for drugs and treatments. ‘I presented the list to one of my editorial
staff, and the list included ‘Wuhan Virus’ and ‘CCP virus’, and he exploded, and said you can’t include those terms in the thesaurus because they are derogatory. But if we don’t include them in the thesaurus we are missing all kinds of publications and discussions that would probably be necessary for researchers to read. We want them to get all that information. We have to be inclusive. Our job is not to decide which is the proper term, or which group has the right information. Our job is to record it all.
“The difficulties can grow exponentially when working on multilingual taxonomies”
‘It leads to the whole question of equitable discovery, because if group A holds one opinion and group B hold another and group C holds yet another point of view, we want to be sure all those parties are working from the same body of information. We can’t decide what their conclusions are going to be, but you want people to have all the information available. I don’t care what decision they make, that’s not my job. My job is to make sure they have all the information. It can be accumulated on the topic and then they can make up their own mind.’ The difficulties can grow exponentially when working on multilingual taxonomies, or trying to align concepts from differing world views but, as Hlava explained, work can potentially have far reaching consequences in the political sphere: ‘We aren’t usually aware of those mappings, but I frequently think part of the reason we don’t quite understand our enemies is we don’t really get inside of their outlines of knowledge, their knowledge organisation for the country and for the school of thought they follow as opposed to our own. That causes a lot of unnecessary misunderstandings, we are coming at the problem from another direction of thought.’
The need for semantic enrichment While there is an increased recognition of the need for semantic enrichment, it is not the only potential tool being promoted as a solution to the data wastage problem. Artificial intelligence is often seen as the exciting solution. As Whittick pointed out, however, It’s not a case of one or the other, but often a case of one building upon the other: ‘We need to build standards, and no one’s very excited about that in many ways, but unless we have those building blocks, you can’t really go ahead with your AI and machine learning.’ As Hlava explained, the failure to
understand the role of semantic enrichment is often coupled with a popular misunderstanding of what artificial intelligence means, and the amount of guidance the technology still needs: ‘Part of the challenge is that artificial intelligence is a bit of a misnomer. A lot of what people think of as artificial intelligence are algorithms which automate a repetitive process that humans do. Automating a repetitive process is very well advanced, and very sensible, but to me that’s still not artificial intelligence. ‘Artificial intelligence is where people add the machine learning algorithms, and machine learning learns and improves the algorithms consistently through all kinds of vectors and statistics, neural maps and lots of other techniques. But the machine keeps on learning, and unless people keep looking at it they don’t have a chance to augment the algorithms, and the machine keeps on going in a straight trajectory and it can learn the wrong things. It might learn things we don’t think are morally or intellectually quite appropriate. It becomes a little dangerous if we let them run unsupervised.’ Artificial intelligence doesn’t offer a
panacea to our data wastage, rather it should be recognised as a tool that can run in harmony with semantic enrichment rather than in competition. There is a continuing need for the insights and judgements only a person can bring. Semantic enrichment is an important
part of tackling many of the big problems facing the world today. The sorts of problems that are encapsulated in the UN’s Sustainable Development Goals requires us to start making far better use of the data that is already available, whether we are talking about tackling scientific or social and political problems, whether we are capturing data in the lab or perspectives across divisive issues. For semantic enrichment to be most beneficial, however, it must be collaborative and inclusive.
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