ContentMining Conifers and Visualising Output: Extracting Semantic Triples

Last month I wrote about visualising ContentMine output. It was limited to showing and grouping ami results which — in this case — were simply words extracted from the text.

A few weeks back, I got to know the ContentMine fact dumps. These are not just extracted words: they are linked to Wikidata Entities, from which all sorts of identifiers and properties can be derived. This is visualised by factvis. For example, when you hover over a country’s name, the population appears.

Now, I’m experimenting with the next level of information: semantic triples. Because NLP a step too far at the moment, I resorted to extracting these from tables. To see how this went in detail, please take a look at my blogpost. A summary: norma — the ContentMine program that normalises articles — has an issue with tables, table layout is very inconsistent across papers (which makes it hard to parse), and I’m currently providing Wikidata IDs for (hopefully) all three parts of the triple.

About that last one: I’ve made a dictionary of species names paired with Wikidata Entity IDs with this query. I limited the number of results because the Web Client times out if I don’t. I run the query locally using the following command:

curl -i -H "Accept: text/csv" --data-urlencode query@<queryFile>.rq -G https://query.wikidata.org/bigdata/namespace/wdq/sparql -o <outputFile>.csv

Then I can match the species names to the values I get when extracting from the table. If I can do this for the property as well, we’ll have a functioning program that creates Wikidata-grade statements from hundreds of articles in a matter of minutes.

There are still plenty of issues. Mapping Wikidata property names to text from tables, for example, or the fact that there are several species whose name are, when shorted, O. bicolor. I can’t know which one just from the context of the table. And all the table layout issues, although probably easier to fix, are still here. That, and upscaling, is what I’m going to focus on for the next month.

But for now, there’s a demo similar to factvis, to preview the triples:

ctj-factvis

Job Opportunity: ContentMine Operations Manager

 

ContentMine was founded in 2016 as a UK non-profit company limited by guarantee. Our mission is to establish content mining for research and for education as widespread philosophy and practice through:

  • creating computer programs, protocols, practises, standards and educational materials that enable content mining,
  • training researchers and others in content mining,
  • encouraging research institutions and funders of research to support establishing freedom for anyone to engage in computational analysis of books, journals, databases and other knowledge sources for the purposes of education and research.

We develop open source software for mining the scientific literature and engage directly in supporting researchers to use mining, saving valuable time and opening up new research avenues.

For information, please visit http://contentmine.org/jobs

Position

We are seeking an Operations Manager to take overall operational responsibility for ContentMine’s development and execution of its mission, reporting to the Board of Directors and working closely with the ContentMine Founder, Dr Peter Murray-Rust. The successful candidate will develop deep knowledge of our core focus, operations, and business development opportunities and manage the transition of the organisation from a project to a sustainable non-profit with oversight of all major business areas from fundraising to communications and HR.

Salary

£40-45k pro rata, negotiable.

Time and Location

4 days per week, fixed term contract for four months in the first instance, with renewal subject to funding. The candidate should be a UK or EU national, remote working possible but candidates in easy travelling distance of Cambridge are preferred.

Responsibilities

Leadership and Management:

  • Ensure ongoing excellence in delivery of the ContentMine mission, including program evaluation, and consistent quality of finance and administration, Manage fundraising, communications, and systems; recommend timelines and resources needed to achieve the strategic goals.
  • Actively engage and energize ContentMine board members, contractors, collaborators, Fellows, volunteers and funders.
  • Ensure effective systems to track progress, evaluate program components and report to the Board and funders.

Fundraising and Communications:

  • Expand revenue generating and fundraising activities to support existing program operations and planned developments.
  • Oversee and refine all aspects of communications—from web presence to external relations, with the goal of creating a stronger brand based on a recent graphical design exercise.
  • Use external presence and relationships to garner new opportunities.

Planning and New Business:

  • Build partnerships with research-oriented organisations including groups and institutes, scholarly societies and NGOs.
  • Establish relationships with potential collaborators and philanthropic funders.
  • Write grant applications and tender for client contracts.
  • Manage relationships and work allocations with partner organisations and contractors who bring new skills and capabilities to projects.

Person Specification

The Operations Manager will be thoroughly committed to ContentMine’s mission. All candidates should have proven leadership and relationship management experience. Concrete demonstrable experience and other qualifications include:

  • At least 5 years of management experience; track record of effectively leading an outcomes-based organization.
  • Ability to point to specific examples of having developed and actioned strategies that have taken an organization to the next stage of growth.
  • Commitment to delivering quality programs and data-driven program evaluation.
  • Excellence in organisational management including developing high-performance teams, setting and achieving strategic objectives, and managing a budget.
  • Fundraising experience with the ability to engage a wide range of stakeholders, partiuclarly in the academic, non-profit, research and publishing sectors.
  • Strong written and verbal communication skills; a persuasive and passionate communicator with excellent interpersonal and multidisciplinary project skills.
  • Action-oriented, entrepreneurial, adaptable approach to business planning.
  • Ability to work effectively in collaboration with diverse groups of people.
  • Passion, integrity, positive attitude, mission-driven and self-directed focus are all desirable.

To apply

Please submit a cover letter and CV to admin@contentmine.org by 2 Dec 2016. Interviews will be held by the 9 Dec. Informal enquiries should be directed to Dr Peter Murray-Rust (peter@contentmine.org).

Digging into cell migration literature

It’s now been a few weeks since I have started working with the other fellows and people at ContentMine to dig into cell migration literature. I must admit it has been quite a challenge, because I have meantime submitted and successfully defended my PhD thesis!

Now, if you are wondering what my specific project was about, you can read about it here: the basic idea is to get a picture of (in)consistency in cell migration literature nomenclature, to build a set of minimal reporting requirements for the community.

So, I have started using more and more the ContentMine pipeline, and, as most of the other fellows, I have encountered a few problems here and there, and the team has been of a fantastic support to fix these issues. I have used the getpapers command so much that I can now run both basic and more advanced queries basically with my eyes closed (or a das keyboard ;)). For now, I have only used the default eupmc API, and, given a lot of papers available, I have decided to narrow down my search downloading papers published between 2000 and 2016, describing in vitro cell migration studies.

This results into a search space of about 700 open access papers.

Having the full XML text, I have then used norma to normalize this and obtain scholarly html files. First thing I wanted to check is the word frequencies, to get a rough idea of which words are used mostly, and in which sections of the papers. The ami-2word plugin seemed to be just perfect for this! However, when running the plugin with a stop-words file (a file containing some words that I would like to be ignored during the analysis, like the ones listed here), the file seems to get ignored (most likely because it cannot be parsed by the plugin). You can find this file here.

I am now discussing this with the fellows and the entire team, and in the process of figuring out if I did something stupid, or if this is an issue we need to correct for, to make the tools at ContentMine even better!

The entire set of commands and results developed so far are in my github repo.

And here is what I want to do next:

  • fix the issue with the stop-words file and visualize the word frequencies across the papers (most likely using a word cloud)
  • use the ami-regex plugin with a set of expressions (terms) that I would like to search for in the papers
  • use the pyCProject to parse my CProject structure and the related CTree and convert these into Python data structures. In this way, downstream manipulation (filtering, visualization etc.) can be done using Python (I will definitely use a jupyter notebook that I will then make available online).

Paola, 2016 fellow

ContentMine featured in Horizon magazine article “Copyright shift would put Europe ahead in ‘future of research’ data mining”

Horizon magazine featured an article on text and data mining and specifically the European Commission proposal for a copyright exception, currently covering “public or private organisations that are carrying out scientific research in the public interest”.

Read the full article >>

Dr Peter Murray-Rust is director of ContentMine, a not-for-profit organisation which has developed software that enables researchers to search through scientific papers on a particular subject. He gives the example of the Zika outbreak as an area where TDM can help to enhance knowledge.

‘We’re going to need to know a lot more about Zika, and much of it may already be in the scientific literature that’s been published but that we don’t read. We don’t read it because there’s so much, so we’ve built a machine, ContentMine, that will liberate the facts from the literature.’

ContentMining Conifers and Visualising Output: Creating a Page for ContentMine Output

In the past few weeks I have made a few pages to visualise ContentMine output of articles with several topics. For this to work, I needed to develop a program to convert ContentMine output to a single (JSON) file to access the facts more easily, and load this in a HTML page. These pages currently contain lists of articles, genus and species. Now, you can quickly glance at a lot of articles, and see common recurrences (although currently most “common recurrences” are typos in plant names on the author’s end).

Example of a results page
Example of a results page

For a more detailed description of my progress, see my blog.

Introducing Fellow Guanyang Zhang: Mining weevil-plant associations

guanyang-zhangI am a taxonomist, the kind of biologists who are charged with discovering, documenting and describing life on earth. I specialize on insects, the most diverse and successful form of life. My ContentMine Fellowship project will focus on mining weevil-plant associations from literature records. I will describe my project in the following.

Motivation. Comprising ~70,000 described and 220,000 estimated species, weevils (Curculionoidea) are one of the most diverse plant-feeding insect lineages and constitute nearly 5% of all known animals. Knowledge of host plant associations is critical for pest management, conservation, and comparative biological research. This knowledge is, however, scattered in 300 years of historical literature and difficult to access. This study will use ContentMine to extract, organize and synthesize knowledge of host plant associations of weevils from the literature. I have been doing literature mining manually and generated nearly 700 entries.

Continue reading Introducing Fellow Guanyang Zhang: Mining weevil-plant associations

ContentMining Preclinical Models of Depression: 1st Update

My depression project, starting with an animal model, depression-specific search carried out in Pubmed and Embase in May 2016, includes 70,363 unique records after deduplication using EndNote. I am currently evaluating the options as to what is the best solution to get my full search (or of much of it as possible) into the ContentMine pipeline.

Here is an outline of the options I am considering and my thoughts:

  • Wait until the screening section of my systematic review is complete and import only the included studies.
    • Pros: Less papers to deal with, which has benefits computationally. Only the most relevant papers would go through the ContentMine pipeline.
    • Cons: Do not get information from the annotation carried out with ‘ami’ tool, in order to more thoroughly test out machine learning options for screening in systematic reviews.

 

  • Use the ‘quickscrape’ function on a list of manually collated URLs.
    • Pros: Records are smoothly imported into the ContentMine pipeline and in the correct formats.
    • Cons: Time. It takes a long time to manually collect the correct URLs and group them into categories for different scrapers. There will likely be issues in that some records will not have a URL. These records would slow the process as a more thorough library search would need to be conducted to find these records or the authors of the record would need to be contacted for full text. A third possibility is that the record does not have an electronic copy. These records could then not be processed by the ContentMine pipeline.

 

  • Run a search using EuPMC with the ‘getpapers’ function. I am currently retrieving about 20,000 records using the search string “(“depressive disorder” OR “depression” OR “depressive behavior” OR “depressive behaviour” OR “dysthymia” OR “dysthymic” AND “animal”)” which has been developed with a librarian at the University of Edinburgh to roughly correspond with the more complex original PubMed & Embase searches.
    • Pros: Records are quickly and easily imported into the ContentMine pipeline.
    • Cons: Would need to later reconcile the records downloaded with EuPMC search with the included papers from screening in order to translate these forward in the systematic review pipeline, to the data extraction phase.

 

  • Retrieve pdfs for the records of my search using EndNote and run ‘norma’ to convert pdfs to text.
    • Pros: The process of downloading full text pdfs from EndNote will be carried out in any case, in order to import full text references into the systematic review database that we have at CAMARADES for further data extraction and meta-analysis.
    • Cons: Possible issues with different pdf formats and therefore the possible output from the PDF conversion. Will the reader be able to deal with the typical journal format of columns (particularly double columns)? Whether and how well it deals with tables and figures? Can legends be retrieved from figures?

 

  • Run my PubMed search with the Open Access Filter and download the xml versions of the articles using the FTP service. Running my PubMed search using the open access filter retrieves 20,381 records which is comparable to the records retrieved using the amended EuPMC search with ‘getpapers’.
    • Considerations: Seeing as the records retrieved are comparable, I do not see much added advantage of using this facility over that of ContentMine EuPMC API.

 

  • Use CrossRef to retrieve full text documents, using a list of DOIs. This method can download up to 10,000 full text records at a time.
    • Pros: Another method of retrieving full text documents in a machine readable format. Could be faster method compared to ‘quickscrape’ as there are DOIs for roughly a third of records.
    • Cons: This method has similar issues to the ‘quickscrape’ tool, namely the time it takes to manually collect the correct DOIs, as only about a third of records have corresponding DOIs that have been located thus far.

 

If anyone has any input or possible solutions I am unaware of or have not yet considered, please leave a comment down below.

 

As with the other fellows, I am also experiencing issues using the ‘getpapers’ function and getting time-out errors. This is an issue that lies with EuPMC and its API and this issue will be rectified shortly.

 

In the meantime, while I weigh the advantages and disadvantages of the above options, I am putting together dictionaries to aide annotation of my documents. The dictionaries I am putting together are; animal models of depression, molecular and cellular pathways, outcome measures (in particular behavioural, neurobiological and anatomical), and risk of bias terms in the animal modelling literature.