About
Experienced machine learning engineer across multiple domains, working from data-informed…
Experience
Education
-
The University of Western Australia
-
Researching knowledge graphs, reasoning and uncertainty (part-time)
-
-
BHP Billiton CEED Scholar
Masters Thesis Topic: Comparing Ontology and Expert System Models for Plant Control (First Class Honours) -
-
Software Engineering specialisation
Broadening units in Philosophy -
Licenses & Certifications
Volunteer Experience
-
Non Executive Director
WAUC
- 4 years
Non Executive Director: 2017 - 2018
Co-President: 2016
Project Leader: 2015
WAUC is a pro-bono, strategy consulting organisation that works collaboratively with charities and not-for-profits to address key business problems and deliver social impact within the WA community (e.g. MS Society WA, Telethon Kids Institute, Chamber of Commerce and Industry WA). Projects include feasibility studies for commercial markets, revenue strategies, costing models and sensitivity analysis. -
Publications
-
An ontology for reasoning over engineering textual data stored in FMEA spreadsheet tables
Computers in Industry
Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence, industry must find ways to extract the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make the classes and relations held in spreadsheet tables explicit. Ontologies offer a pathway because they use formal descriptions to define…
Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence, industry must find ways to extract the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make the classes and relations held in spreadsheet tables explicit. Ontologies offer a pathway because they use formal descriptions to define machine-interpretable definitions of shared concepts and relations between concepts. We illustrate this with two case studies on a failure modes and effects analysis (FMEA) table. Our examples demonstrate how the relationship between rows and columns in a table can be represented in logic for FMEA entries, thereby allowing the same ontology to ingest instance data from the IEC 60812:2006 FMEA Standard and a real industrial FMEA. We give relationships in the FMEA and asset hierarchy spreadsheets an explicit representation, so that OWL-DL reasoning can infer final failure effects at the system level from component failures. The prototype ontologies described in this paper are modular and aligned to a top level ontology, and hence can be applied to other use cases. Our contribution is showing that engineers can make data captured in commonly used spreadsheet tables machine readable using a FMEA ontology.
Other authorsSee publication -
Digitalization and reasoning over engineering textual data stored in spreadsheet tables
Advanced Maintenance Engineering, Services and Technologies (AMEST) 2020
Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence requires industry to find ways of extracting the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make explicit the classes and relations held in spreadsheet tables. Ontologies offer a pathway, as they are used to define machine-interpretable…
Much textual engineering knowledge is captured in tables, particularly in spreadsheets and in documents such as equipment manuals. To leverage the benefits of artificial intelligence requires industry to find ways of extracting the data and relationships captured in these tables. This paper demonstrates the application of an ontological approach to make explicit the classes and relations held in spreadsheet tables. Ontologies offer a pathway, as they are used to define machine-interpretable definitions of shared concepts, and relations between concepts, using formal descriptions. We illustrate this with a case study on a Failure Modes and Effects Analysis (FMEA) table, using an example from the IEC 60812 FMEA Standard. Our example demonstrates how the relationship between rows and columns in a table can be represented in logic. Further, we give relationships in the FMEA and asset hierarchy spreadsheets an explicit representation, so that OWL-DL reasoning can infer final failure effects at the system level from component failures. The prototype ontologies described in this paper are modular and aligned to a Top Level Ontology, and hence can be applied to other use cases. Our contribution is to show engineers needing to make data captured in spreadsheet tables machine readable, how ontologies can be applied using a real example.
Other authorsSee publication -
Benchmarking for keyword extraction methodologies in maintenance work orders
Proceedings of the Annual Conference of the PHM Society
Maintenance has largely remained a human-knowledge centered activity, with the primary records of activity being textbased maintenance work orders (MWOs). However, the bulk of maintenance research does not currently attempt to quantify human knowledge, though this knowledge can be rich with useful contextual and system-level information. The underlying quality of data in MWOs often suffers from misspellings, domain-specific (or even workforce specific) jargon, and abbreviations, that prevent…
Maintenance has largely remained a human-knowledge centered activity, with the primary records of activity being textbased maintenance work orders (MWOs). However, the bulk of maintenance research does not currently attempt to quantify human knowledge, though this knowledge can be rich with useful contextual and system-level information. The underlying quality of data in MWOs often suffers from misspellings, domain-specific (or even workforce specific) jargon, and abbreviations, that prevent its immediate use in computer analyses. Therefore, approaches to making this data computable must translate unstructured text into a formal schema or system; ie, perform a mapping from informal technical language to some computable format. Keyword spotting (or, extraction) has proven a valuable tool in reducing manual efforts while structuring data, by providing a systematic methodology to create computable knowledge. This technique searches for known vocabulary in a corpus and maps them to designed higher level concepts, shifting the primary effort away from structuring the MWOs themselves, toward creating a dictionary of domain specific terms and the knowledge that they represent. The presented work compares rules-based keyword extraction to data-driven tagging assistance, through quantitative and qualitative discussion of the key advantages and disadvantages. This will enable maintenance practitioners to select an appropriate approach to information encoding that provides needed functionality at minimal cost and effort.
Other authorsSee publication -
Applying cognitive computing to maintainer-collected data
2017 2nd International Conference on System Reliability and Safety (ICSRS)
Companies are investing heavily in predictive maintenance algorithms without considering how the predictions will be validated. When components are removed, the observations of the maintenance technicians about their state (failed or not) and the failure mode are crucial to this validation process and to developing accurate component reliability distributions. Despite years of effort to get maintenance technicians to collect data that is usable and useful to engineers, either by trying to…
Companies are investing heavily in predictive maintenance algorithms without considering how the predictions will be validated. When components are removed, the observations of the maintenance technicians about their state (failed or not) and the failure mode are crucial to this validation process and to developing accurate component reliability distributions. Despite years of effort to get maintenance technicians to collect data that is usable and useful to engineers, either by trying to enforce the use of codes or apply management controls, little progress has been made. Advances in cognitive computing processes such as text mining, natural language processing and knowledge representation hold the key to solving this problem. The purpose of this paper is to explain key concepts in text mining, knowledge representation and ontology development in a way that is accessible to reliability and maintenance engineers. We illustrate, using a conveyor system as an example, how these concepts can be applied. Our aim is to convince the reader of the value of investing time to understand and develop cognitive computing methods. Sooner, rather than later, these concepts will be used to translate manually entered maintenance work order data to support validation of condition based maintenance predictions and generation of near real time reliability distributions.
Other authorsSee publication
Honors & Awards
-
2019 ICDM/ICBK Knowledge Graph Contest - Winner
International Conference on Data Mining / Mininglamp Technology
Small part of a larger NLP research team, involving Dr Wei Liu, Michael Stewart, Majigsuren Enkhsaikhan and Morgan Lewis. Used SpaCy (chunked noun and verb phrases extracted according to predefined rules), a pre-trained attention-based Bi-LSTM model and NeuralCoref for entity resolution.
Work can be found here: https://arxiv.org/pdf/1909.01807.pdf -
Australian Government Research Training Program (AGRTP) Scholarship
Australian Commonwealth Government
Established by the Australian Commonwealth Government, this scholarship Is available to high achieving domestic and international students undertaking a Master by research degree or Doctoral by research degree.
-
Silicon Valley Youth Mission
Startup Catalyst
• Selected through competitive process as one of twenty promising future tech leaders in Australia
• Two week trip sponsored by River City Labs with visits including Facebook, Google and Stanford
• Several panels with startup founders / investors and participation in Startup Weekend Santa Cruz -
Startup Weekend Santa Cruz - Second Place
Techstars
• Attended as part of Startup Catalyst's Youth Mission
• Pitched Aha!, high quality mentor matching for practical skills
• Built an MVP using Meteor.js, Digital Ocean and MailChimp -
Unearthed Hackathon - First Place
Unearthed + Woodside
• Beat 26 other teams to win overall
• Over 54 hours developed a solution in Python and SQL in a team of 7 students
• Optimised a Woodside port with regard to scheduling, routing and packing -
UBS Investment Banking Challenge - National Runner-Up
UBS
• Acted as bankers for three different stages of M&A
• Presented to a team of five UBS bankers including MD's
• Skills included financial modelling and slide deck presentation -
Georgian Prize
St Georges College
Awarded for meritorious service to the college.
Organizations
-
St Georges College
Resident Advisor (2013), Treasurer (2014), Magazine Editor (2014)
- PresentThe St George's College Club is an organisation funded and run by residents of St George's College within the University of Western Australia. Its purpose is to foster a vibrant community through social and educational events.
Other similar profiles
Explore collaborative articles
We’re unlocking community knowledge in a new way. Experts add insights directly into each article, started with the help of AI.
Explore MoreOthers named Thomas Smoker
-
Tom Smoker
Retired Royal Navy Veteran
-
Tom Smoker
Broker at Coldwell Banker Residential Brokerage
-
Tom Smoker
Route Sales Representative at FRITO LAY INC
3 others named Thomas Smoker are on LinkedIn
See others named Thomas Smoker