Search Term Recommendations

Search query term recommendations for Scholarly and Enterprise Search: Google Scholar recently introduced recommended search terms based on specificity of the query for scholars, a topic I published on in 2014. I have reviewed the 'search serendipity' literature in past posts, so won't go over that again, rather focus specifically on a few methods in... Continue Reading →

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European Petroleum GIS Conference: Mapping Analogues

Looking forward to presenting at the end of this month at the ESRI Petroleum GIS PUG at St Paul's in London. Applying text analytics and machine learning to detect geoscience analogues and how that can be represented spatially. https://www.esri.com/en-us/about/events/euro-petroleum-conference/overview

Scholarly Publishing, Search & Discovery

Platform Strategies Conference, National Union Building, Washington DC September 26-27 2018 Hosted by Silverchair. Conference notes: Dr Paul H. Cleverley, Robert Gordon University, Aberdeen, UK Introduction Over 100 people gathered at the platform strategies conference this week, converging on the 1890’s Brownstone National Union Building in the Penn Quarter of Washington DC between the White... Continue Reading →

Machine Learning on Subsurface Data

20th September 2018, FORCE, Norwegian Petroleum Directorate (NPD), Stavanger Brief event write up: Dr Paul H Cleverley, Robert Gordon University Introduction Over 130 people attended the event held in Stavanger on the 20th September, mainly Oil & Gas Operators (Equinor, AkerBP, ConocoPhillips, Spirit Energy, Wintershall etc.). The event was excellently organized by Peter Bormann (ConocoPhillips),... Continue Reading →

Search is on the move…

Today, there are more search queries made on mobile devices than on desktop computers, which has probably been one of the major changes within information search during the past decade, along with the rise of voice search. I was invited to conduct a book review over the summer of ‘Mobile Search Behavior: An In-depth Analysis... Continue Reading →

Chemical Element Entity Extraction from Economic Geology Journals: Combining word frequency in text with measured numerical data

Finding trends and patterns in unstructured text can be possible without combining with other data sources. However, combining derived structured data from text analytics with measured real world data can also lead to differentiating insights. Figure 1 shows the frequency of occurrence of chemical elements in the text of 100 years of the Society of... Continue Reading →

Unsupervised Machine Learning: Clustering Geoscience Text Using Co-occurrence windows and Principal Component Analysis (PCA).

Unsupervised machine learning techniques exploit latent patterns in text (in layman's terms - normally some form of complex word co-occurrence) rather than rules driven by human labelled data. As this is essentially an inductive technique, it can be useful to stimulate ideas and questions that the information professional or geoscientist a priori, may not have... Continue Reading →

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