Author: phcleverley

Extracting Knowledge from Text using AI

OGTC 4

Thoroughly enjoyed two days workshops with the Oil and Gas Technology Centre (OGTC) this week in Aberdeen. The OGTC’s goal is to maximise economic recovery from the UK Continental Shelf, supported by Government.

As well as participating in workshops, I also shared some of my research on predictive geoscience sentiment analysis and its role to stimulate new insights. Thanks to all the staff for coordinating the event and some great participation from Operators, Service Companies and Academia.

An exciting time to be involved in Geoscience and Data Science!

 

 

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Artificially Intelligent Sub-Surface

Delighted to be invited as a keynote speaker for the Oil and Gas Technology Centre (OGTC) workshop on artificially intelligent sub-surface this month, 19-20 June in Aberdeen, representing Robert Gordon University.

artificially-intelligent

Artificially intelligent sub-surface is one of the six themes the OGTC are working on for Digital Transformation in the oil and gas industry. More here:

https://theogtc.com/events/2018/open-event-artificially-intelligent-sub-surface/

 

Review of Enterprise Search: Journal of Information Science Paper

Martin White (Visiting Professor at the University of Sheffield and Managing Director of IntranetFocus) has written a review of a recent academic paper I authored Here with Professor Simon Burnett on enterprise search:

“Dr Paul Cleverley and Professor Simon Burnett (Robert Gordon University) have published in the Journal of Information Science what is without doubt a landmark research paper on the factors that influence user satisfaction with enterprise search applications”

“No matter how small or large your organization, if you have responsibility for search management you should be taking this remarkable paper, marking it up para by para, and then using it to benchmark your approach to achieving the levels of search satisfaction that your employees expect”

“This research will change the way that the enterprise search community (and that includes software vendors) consider the opportunities and challenges of effective enterprise search management”

http://intranetfocus.com/enterprise-search-satisfaction-the-impacts-of-technology-information-and-literacy-factors-part-1/

http://intranetfocus.com/enterprise-search-satisfaction-the-impacts-of-technology-information-and-literacy-factors-part-2/

First large scale empirical study of enterprise search

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First large scale empirical study of enterprise search & discovery capability published in the Journal of Information Science (JIS) this week. Here

Many organizations have deployed ‘Google-like’ enterprise search engines in order to improve access to their own information, a key part of the digital workplace. Despite significant investments, it has been reported that dissatisfaction with search in the enterprise is widespread and enduring. A study was undertaken in order to develop a deeper understanding of what may be occurring. 

Using a large oil & gas company as a case study on their fourth generation of enterprise search technology, over 1,000 feedback comments from the user interface over a 2 year period were triangulated with interviews conducted with a search service team and management. This was combined with an extensive literature review.

Well known structural and formal factors for user satisfaction such as ‘information quality’, ‘technology quality’ and ‘service quality’ were identified. The study finding that 62% of user dissatisfaction events were likely due to non-technological factors may provide the first empirical support for what some enterprise search practitioners have been saying for some time: effective search capability in the enterprise requires more than technology. For some search queries, improving knowledge organization practices for structuring content may be more useful than tuning the search technology. In addition, the criticality of informal behaviours and agency (information literacy) was clearly identified, which is often downplayed or ignored altogether in the practitioner literature.

 The ‘Google Habitus’ was identified as a generative mechanism influencing expectations and behaviours at all levels in the organization for search, often leading to sub-optimal outcomes. There are aspects of search in the enterprise that differ considerably from Internet consumer based search, which has been well documented. Cognitive biases were postulated as another generative mechanism, such as simplicity bias (technology solutionism), where a preference for simple explanations ‘we can fix search with better technology’ often wins out over more complex explanations.

Whilst general purpose search capability is undoubtedly useful as a utility, approaches which also focus on very specific work tasks may be more likely to gain executive support. Advancing enterprise search capability is therefore likely to lend itself to multi-modal approaches; a system of agency and structure rather than any single component; not a single technology or interface, or single media type (text documents/web pages) or single set of behaviours. It is probable that organizations adopting holistic approaches towards search capability will in the long run, out-perform those that have more reductionist approaches.

STEPS Distinguished Lecture on Big Data

Invited to give the Distinguished Lecture on Big Data next month for the Science and Technology Exploration and Production (STEPS) program run by Halliburton. The program aims to foster geoscience excellence through the facilitation of thematic research and offers the opportunity for academics to engage with Landmark (Halliburton) and the wider exploration and production community.

The lecture title is Big Data – Small Patterns: Applying Geoscience Sentiment Analysis to Unstructured Text.

Will be sharing recent results and findings of the Geoscience aware sentiment AnalyZER (GAZER) algorithm I developed in Python which has been applied to Geological elements in public domain texts. It is designed to surface interesting associative patterns relating to concepts such as ‘source rock’, ‘reservoir’, ‘trap’ and ‘seal’ that might be unknown to exploration geoscientists as they are buried in volumes of documents too large to ever be read and too subtle to be detected by traditional search engines.

The hypothesis is that if a geoscientist can be surprised by these patterns, and there is legitimate evidence for that ‘surprise’, it is likely to lead to a learning event and potentially a new play/model; changing what people know – or think they know.

More here:

http://www.ienergy.community/NewsDetails/dt/Detail/ItemID/361/Big-Data-–-Small-Patterns-