Returning to the subject of personal knowledge graphs Kyle Scheer has an interesting repository of Markdown files that describe academic disciplines at https://github.com/kyletscheer/academic-disciplines (see his blog post for more background).
\n\nIf you add these files to Obsidian you get a nice visualisation of a taxonomy of academic disciplines. The applications of this to biological taxonomy seem obvious, especially as a tool like Obsidian enables all sorts of interesting links to be added (e.g., we could add links to the taxonomic research behind each node in the taxonomic tree, the people doing that research, etc. - although that would mean we''d no longer have a simple tree).
\n\nThe more I look at these sort of simple Markdown-based tools the more I wonder whether we could make more use of them to create simple but persistent databases. Text files seem the most stable, long-lived digital format around, maybe this would be a way to minimise the inevitable obsolescence of database and server software. Time for some experiments I feel... can we take a taxonomic group, such as mammals, and create a richly connected database purely in Markdown?
\n\n","tags":["markdown","obsidian"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/m48f7-c2128","uuid":"8aea47e4-f227-45f4-b37b-0454a8a7a3ff","url":"https://iphylo.blogspot.com/2023/04/chatgpt-semantic-search-and-knowledge.html","title":"ChatGPT, semantic search, and knowledge graphs","summary":"One thing about ChatGPT is it has opened my eyes to some concepts I was dimly aware of but am only now beginning to fully appreciate. ChatGPT enables you ask it questions, but the answers depend on what ChatGPT “knows”. As several people have noted, what would be even better is to be able to run ChatGPT on your own content. Indeed, ChatGPT itself now supports this using plugins. Paul Graham GPT However, it’s still useful to see how to add ChatGPT functionality to your own content from...","date_published":"2023-04-03T15:30:00Z","date_modified":"2023-04-03T15:32:04Z","date_indexed":"1909-06-16T09:02:34+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"One thing about ChatGPT is it has opened my eyes to some concepts I was dimly aware of but am only now beginning to fully appreciate. ChatGPT enables you ask it questions, but the answers depend on what ChatGPT “knows”. As several people have noted, what would be even better is to be able to run ChatGPT on your own content. Indeed, ChatGPT itself now supports this using plugins.
\nHowever, it’s still useful to see how to add ChatGPT functionality to your own content from scratch. A nice example of this is Paul Graham GPT by Mckay Wrigley. Mckay Wrigley took essays by Paul Graham (a well known venture capitalist) and built a question and answer tool very like ChatGPT.
\n\nBecause you can send a block of text to ChatGPT (as part of the prompt) you can get ChatGPT to summarise or transform that information, or answer questions based on that information. But there is a limit to how much information you can pack into a prompt. You can’t put all of Paul Graham’s essays into a prompt for example. So a solution is to do some preprocessing. For example, given a question such as “How do I start a startup?” we could first find the essays that are most relevant to this question, then use them to create a prompt for ChatGPT. A quick and dirty way to do this is simply do a text search over the essays and take the top hits. But we aren’t searching for words, we are searching for answers to a question. The essay with the best answer might not include the phrase “How do I start a startup?”.
\nEnter Semantic search. The key concept behind semantic search is that we are looking for documents with similar meaning, not just similarity of text. One approach to this is to represent documents by “embeddings”, that is, a vector of numbers that encapsulate features of the document. Documents with similar vectors are potentially related. In semantic search we take the query (e.g., “How do I start a startup?”), compute its embedding, then search among the documents for those with similar embeddings.
\nTo create Paul Graham GPT Mckay Wrigley did the following. First he sent each essay to the OpenAI API underlying ChatGPT, and in return he got the embedding for that essay (a vector of 1536 numbers). Each embedding was stored in a database (Mckay uses Postgres with pgvector). When a user enters a query such as “How do I start a startup?” that query is also sent to the OpenAI API to retrieve its embedding vector. Then we query the database of embeddings for Paul Graham’s essays and take the top five hits. These hits are, one hopes, the most likely to contain relevant answers. The original question and the most similar essays are then bundled up and sent to ChatGPT which then synthesises an answer. See his GitHub repo for more details. Note that we are still using ChatGPT, but on a set of documents it doesn’t already have.
\nI’m a fan of knowledge graphs, but they are not terribly easy to use. For example, I built a knowledge graph of Australian animals Ozymandias that contains a wealth of information on taxa, publications, and people, wrapped up in a web site. If you want to learn more you need to figure out how to write queries in SPARQL, which is not fun. Maybe we could use ChatGPT to write the SPARQL queries for us, but it would be much more fun to be simply ask natural language queries (e.g., “who are the experts on Australian ants?”). I made some naïve notes on these ideas Possible project: natural language queries, or answering “how many species are there?” and Ozymandias meets Wikipedia, with notes on natural language generation.
\nOf course, this is a well known problem. Tools such as RDF2vec can take RDF from a knowledge graph and create embeddings which could in tern be used to support semantic search. But it seems to me that we could simply this process a bit by making use of ChatGPT.
\nFirstly we would generate natural language statements from the knowledge graph (e.g., “species x belongs to genus y and was described in z”, “this paper on ants was authored by x”, etc.) that cover the basic questions we expect people to ask. We then get embeddings for these (e.g., using OpenAI). We then have an interface where people can ask a question (“is species x a valid species?”, “who has published on ants”, etc.), we get the embedding for that question, retrieve natural language statements that the closest in embedding “space”, package everything up and ask ChatGPT to summarise the answer.
\nThe trick, of course, is to figure out how t generate natural language statements from the knowledge graph (which amounts to deciding what paths to traverse in the knowledge graph, and how to write those paths is something approximating English). We also want to know something about the sorts of questions people are likely to ask so that we have a reasonable chance of having the answers (for example, are people going to ask about individual species, or questions about summary statistics such as numbers of species in a genus, etc.).
\nWhat makes this attractive is that it seems a straightforward way to go from a largely academic exercise (build a knowledge graph) to something potentially useful (a question and answer machine). Imagine if something like the defunct BBC wildlife site (see Blue Planet II, the BBC, and the Semantic Web: a tale of lessons forgotten and opportunities lost) revived here had a question and answer interface where we could ask questions rather than passively browse.
\nI have so much more to learn, and need to think about ways to incorporate semantic search and ChatGPT-like tools into knowledge graphs.
\n\n","tags":[],"language":"en","references":[]},{"id":"https://doi.org/10.59350/zc4qc-77616","uuid":"30c78d9d-2e50-49db-9f4f-b3baa060387b","url":"https://iphylo.blogspot.com/2022/09/does-anyone-cite-taxonomic-treatments.html","title":"Does anyone cite taxonomic treatments?","summary":"Taxonomic treatments have come up in various discussions I''m involved in, and I''m curious as to whether they are actually being used, in particular, whether they are actually being cited. Consider the following quote: The taxa are described in taxonomic treatments, well defined sections of scientific publications (Catapano 2019). They include a nomenclatural section and one or more sections including descriptions, material citations referring to studied specimens, or notes ecology and...","date_published":"2022-09-01T16:49:00Z","date_modified":"2022-09-01T16:49:51Z","date_indexed":"1909-06-16T09:31:50+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"Written with StackEdit.
\n
\nThe taxa are described in taxonomic treatments, well defined sections of scientific publications (Catapano 2019). They include a nomenclatural section and one or more sections including descriptions, material citations referring to studied specimens, or notes ecology and behavior. In case the treatment does not describe a new discovered taxon, previous treatments are cited in the form of treatment citations. This citation can refer to a previous treatment and add additional data, or it can be a statement synonymizing the taxon with another taxon. This allows building a citation network, and ultimately is a constituent part of the catalogue of life. - Taxonomic Treatments as Open FAIR Digital Objects https://doi.org/10.3897/rio.8.e93709\n\n\n
\n \"Traditional\" academic citation is from article to article. For example, consider these two papers:\n\n
\nLi Y, Li S, Lin Y (2021) Taxonomic study on fourteen symphytognathid species from Asia (Araneae, Symphytognathidae). ZooKeys 1072: 1-47. https://doi.org/10.3897/zookeys.1072.67935\n\n\n
\nMiller J, Griswold C, Yin C (2009) The symphytognathoid spiders of the Gaoligongshan, Yunnan, China (Araneae: Araneoidea): Systematics and diversity of micro-orbweavers. ZooKeys 11: 9-195. https://doi.org/10.3897/zookeys.11.160\n\n\n\n
Li et al. 2021 cites Miller et al. 2009 (although Pensoft seems to have broken the citation such that it does appear correctly either on their web page or in CrossRef).
\n\nSo, we have this link: [article]10.3897/zookeys.1072.67935 --cites--> [article]10.3897/zookeys.11.160. One article cites another.
\n\nIn their 2021 paper Li et al. discuss Patu jidanweishi Miller, Griswold & Yin, 2009:\n\n
![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEgPavMHXqQNX1ls_zXo9kIcMLHPxc7ZpV9NCSof5wLumrg3ovPoi6nYKzZsINuqFtYoEvW1QrerePD-MEf2DJaYUXlT37d81x3L6ILls7u229rg0_Nc0uUmgW-ICzr6MI_QCZfgQbYGTxuofu-fuPVoygbCnm3vQVYOhLDLtp1EtQ9jRZHDvw/s1040/Screenshot%202022-09-01%20at%2017.12.27.png\")
There is a treatment for the original description of Patu jidanweishi at https://doi.org/10.5281/zenodo.3792232, which was created by Plazi with a time stamp \"2020-05-06T04:59:53.278684+00:00\". The original publication date was 2009, the treatments are being added retrospectively.
\n\nIn an ideal world my expectation would be that Li et al. 2021 would have cited the treatment, instead of just providing the text string \"Patu jidanweishi Miller, Griswold & Yin, 2009: 64, figs 65A–E, 66A, B, 67A–D, 68A–F, 69A–F, 70A–F and 71A–F (♂♀).\" Isn''t the expectation under the treatment model that we would have seen this relationship:
\n\n[article]10.3897/zookeys.1072.67935 --cites--> [treatment]https://doi.org/10.5281/zenodo.3792232
\n\nFurthermore, if it is the case that \"[i]n case the treatment does not describe a new discovered taxon, previous treatments are cited in the form of treatment citations\" then we should also see a citation between treatments, in other words Li et al.''s 2021 treatment of Patu jidanweishi (which doesn''t seem to have a DOI but is available on Plazi'' web site as https://tb.plazi.org/GgServer/html/1CD9FEC313A35240938EC58ABB858E74) should also cite the original treatment? It doesn''t - but it does cite the Miller et al. paper.
\n\nSo in this example we don''t see articles citing treatments, nor do we see treatments citing treatments. Playing Devil''s advocate, why then do we have treatments? Does''t the lack of citations suggest that - despite some taxonomists saying this is the unit that matters - they actually don''t. If we pay attention to what people do rather than what they say they do, they cite articles.
\n\nNow, there are all sorts of reasons why we don''t see [article] -> [treatment] citations, or [treatment] -> [treatment] citations. Treatments are being added after the fact by Plazi, not by the authors of the original work. And in many cases the treatments that could be cited haven''t appeared until after that potentially citing work was published. In the example above the Miller et al. paper dates from 2009, but the treatment extracted only went online in 2020. And while there is a long standing culture of citing publications (ideally using DOIs) there isn''t an equivalent culture of citing treatments (beyond the simple text strings).
\n\nObviously this is but one example. I''d need to do some exploration of the citation graph to get a better sense of citations patterns, perhaps using CrossRef''s event data. But my sense is that taxonomists don''t cite treatments.
\n\nI''m guessing Plazi would respond by saying treatments are cited, for example (indirectly) in GBIF downloads. This is true, although arguably people aren''t citing the treatment, they''re citing specimen data in those treatments, and that specimen data could be extracted at the level of articles rather than treatments. In other words, it''s not the treatments themselves that people are citing.
\n\nTo be clear, I think there is value in being able to identify those \"well defined sections\" of a publication that deal with a given taxon (i.e., treatments), but it''s not clear to me that these are actually the citable units people might hope them to be. Likewise, journals such as ZooKeys have DOIs for individual figures. Does anyone actually cite those?
","tags":[],"language":"en","references":[]},{"id":"https://doi.org/10.59350/pmhat-5ky65","uuid":"5891c709-d139-440f-bacb-06244424587a","url":"https://iphylo.blogspot.com/2021/10/problems-with-plazi-parsing-how.html","title":"Problems with Plazi parsing: how reliable are automated methods for extracting specimens from the literature?","summary":"The Plazi project has become one of the major contributors to GBIF with some 36,000 datasets yielding some 500,000 occurrences (see Plazi''s GBIF page for details). These occurrences are extracted from taxonomic publication using automated methods. New data is published almost daily (see latest treatments). The map below shows the geographic distribution of material citations provided to GBIF by Plazi, which gives you a sense of the size of the dataset. By any metric Plazi represents a...","date_published":"2021-10-25T11:10:00Z","date_modified":"2021-10-28T16:08:18Z","date_indexed":"1970-01-01T00:00:00+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"![\"\"](\"https://1.bp.blogspot.com/-oiqSkA53FI4/YXaRAUpRgFI/AAAAAAAAgzY/PbPEOh_VlhIE0aaqqhVQPLAOE5pRULwJACLcBGAsYHQ/s240/Rf7UoXTw_400x400.jpg\")
The Plazi project has become one of the major contributors to GBIF with some 36,000 datasets yielding some 500,000 occurrences (see Plazi''s GBIF page for details). These occurrences are extracted from taxonomic publication using automated methods. New data is published almost daily (see latest treatments). The map below shows the geographic distribution of material citations provided to GBIF by Plazi, which gives you a sense of the size of the dataset.
\n\n![\"\"](\"https://1.bp.blogspot.com/-DCJ4HR8eej8/YXaRQnz22bI/AAAAAAAAgz4/AgRcree6jVgjtQL2ch7IXgtb_Xtx7fkngCPcBGAYYCw/s1030/Screenshot%2B2021-10-24%2Bat%2B20.43.23.png\")
By any metric Plazi represents a considerable achievement. But often when I browse individual records on Plazi I find records that seem clearly incorrect. Text mining the literature is a challenging problem, but at the moment Plazi seems something of a \"black box\". PDFs go in, the content is mined, and data comes up to be displayed on the Plazi web site and uploaded to GBIF. Nowhere does there seem to be an evaluation of how accurate this text mining actually is. Anecdotally it seems to work well in some cases, but in others it produces what can only be described as bogus records.
\n\nA treatment in Plazi is a block of text (and sometimes illustrations) that refers to a single taxon. Often that text will include a description of the taxon, and list one or more specimens that have been examined. These lists of specimens (\"material citations\") are one of the key bits of information that Plaza extracts from a treatment as these citations get fed into GBIF as occurrences.
\n\nTo help explore treatments I''ve constructed a simple web site that takes the Plazi identifier for a treatment and displays that treatment with the material citations highlighted. For example, for the Plazi treatment 03B5A943FFBB6F02FE27EC94FABEEAE7 you can view the marked up version at https://plazi-tester.herokuapp.com/?uri=622F7788-F0A4-449D-814A-5B49CD20B228. Below is an example of a material citation with its component parts tagged:
\n\n![\"\"](\"https://1.bp.blogspot.com/-NIGuo9BggdA/YXaRQQrv0QI/AAAAAAAAgz4/SZDcA1jZSN47JMRTWDwSMRpHUShrCeOdgCPcBGAYYCw/s693/Screenshot%2B2021-10-24%2Bat%2B20.59.56.png\")
This is an example where Plazi has successfully parsed the specimen. But I keep coming across cases where specimens have not been parsed correctly, resulting in issues such as single specimens being split into multiple records (e.g., https://plazi-tester.herokuapp.com/?uri=5244B05EFFC8E20F7BC32056C178F496), geographical coordinates being misinterpreted (e.g., https://plazi-tester.herokuapp.com/?uri=0D228E6AFFC2FFEFFF4DE8118C4EE6B9), or collector''s initials being confused with codes for natural history collections (e.g., https://plazi-tester.herokuapp.com/?uri=252C87918B362C05FF20F8C5BFCB3D4E).
\n\nParsing specimens is a hard problem so it''s not unexpected to find errors. But they do seem common enough to be easily found, which raises the question of just what percentage of these material citations are correct? How much of the data Plazi feeds to GBIF is correct? How would we know?
\n\nSome of the errors I''ve found concern the interpretation of the parsed data. For example, it is striking that despite including marine taxa no Plazi record has a value for depth below sea level (see GBIF search on depth range 0-9999 for Plazi). But many records do have an elevation, including records from marine environments. Any record that has a depth value is interpreted by Plazi as being elevation, so we have aerial crustacea and fish.
\n\n![\"\"](\"https://1.bp.blogspot.com/-GD4pPtPCxVc/YXaRQ9bdn1I/AAAAAAAAgz8/A9YsypSvHfwWKAjDxSdeFVUkou88LGItACPcBGAYYCw/s673/Screenshot%2B2021-10-25%2Bat%2B12.03.51.png\")
![\"\"](\"https://1.bp.blogspot.com/-BReSHtXTOkA/YXaRRKFW7dI/AAAAAAAAg0A/-FcBkMwyswIp0siWGVX3MNMANs7UkZFtwCPcBGAYYCw/s675/Screenshot%2B2021-10-25%2Bat%2B12.04.06.png\")
Anecdotally I''ve also noticed that Plazi seems to do well on zoological data, especially journals like Zootaxa, but it often struggles with botanical specimens. Botanists tend to cite specimens rather differently to zoologists (botanists emphasise collector numbers rather than specimen codes). Hence data quality in Plazi is likely to taxonomic biased.
\n\nPlazi is using GitHub to track issues with treatments so feedback on erroneous records is possible, but this seems inadequate to the task. There are tens of thousands of data sets, with more being released daily, and hundreds of thousands of occurrences, and relying on GitHub issues devolves the responsibility for error checking onto the data users. I don''t have a measure of how many records in Plazi have problems, but because I suspect it is a significant fraction because for any given day''s output I can typically find errors.
\n\nFaced with a process that generates noisy data there are several of things we could do:
\n\nI think a comparison with the problem of parsing bibliographic references might be instructive here. There is a long history of people developing tools to parse references (I''ve even had a go). State-of-the art tools such as AnyStyle feature machine learning, and are tested against human curated datasets of tagged bibliographic records. This means we can evaluate the performance of a method (how well does it retrieve the same results as human experts?) and also improve the method by expanding the corpus of training data. Some of these tools can provide a measures of how confident they are when classifying a string as, say, a person''s name, which means we could flag potential issues for anyone wanting to use that record.
\n\nWe don''t have equivalent tools for parsing specimens in the literature, and hence have no easy way to quantify how good existing methods are, nor do we have a public corpus of material citations that we can use as training data. I blogged about this a few months ago and was considering using Plazi as a source of marked up specimen data to use for training. However based on what I''ve looked at so far Plazi''s data would need to be carefully scrutinised before it could be used as training data.
\n\nGoing forward, I think it would be desirable to have a set of records that can be used to benchmark specimen parsers, and ideally have the parsers themselves available as web services so that anyone can evaluate them. Even better would be a way to contribute to the training data so that these tools improve over time.
\n\nPlazi''s data extraction tools are mostly desktop-based, that is, you need to download software to use their methods. However, there are experimental web services available as well. I''ve created a simple wrapper around the material citation parser, you can try it at https://plazi-tester.herokuapp.com/parser.php. It takes a single material citation and returns a version with elements such as specimen code and collector name tagged in different colours.
\n\nText mining the taxonomic literature is clearly a gold mine of data, but at the same time it is potentially fraught as we try and extract structured data from semi-structured text. Plazi has demonstrated that it is possible to extract a lot of data from the literature, but at the same time the quality of that data seems highly variable. Even minor issues in parsing text can have big implications for data quality (e.g., marine organisms apparently living above sea level). Historically in biodiversity informatics we have favoured data quantity over data quality. Quantity has an obvious metric, and has milestones we can celebrate (e.g., one billion specimens). There aren''t really any equivalent metrics for data quality.
\n\nAdding new types of data can sometimes initially result in a new set of quality issues (e.g., GBIF metagenomics and metacrap) that take time to resolve. In the case of Plazi, I think it would be worthwhile to quantify just how many records have errors, and develop benchmarks that we can use to test methods for extracting specimen data from text. If we don''t do this then there will remain uncertainty as to how much trust we can place in data mined from the taxonomic literature.
\n\n![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEi5ZkbnumEWKZpf0isei_hUJucFlOOK08-SKnJknD8B4qhAX36u-vMQRnZdhRJK5rPb7HNYcnqB7qE4agbeStqyzMkWHrzUj14gPkz2ohmbOVg8P_nHo0hM94PD1wH3SPJsaLAumN8vih3ch9pjH2RaVWZBLwwhGhNu0FS1m5z6j5xt2NeZ4w/s2140/linking%20to%20names144.jpg\")
In the diagram above I''ve tried to capture the different situatios I encounter. Much of the work I''ve done on this has focussed on case 1 in the diagram: I want to link a taxonomic name to an identifier for the work in which that name was published. In practise this means linking names to DOIs. This has the advantage of linking to a citable indentifier, raising questions such as whether citations of taxonmic papers by taxonomic databases could become part of a taxonomist''s Google Scholar profile.
\n\nIn many taxonomic databases full work-level citations are not the norm, instead taxonomists cite one or more pages within a work that are relevant to a taxonomic name. These \"microcitations\" (what the U.S. legal profession refer to as \"point citations\" or \"pincites\", see What are pincites, pinpoints, or jump legal references?) require some work to map to the work itself (which is typically the thing that has a citatble identifier such as a DOI).
\n\nMicrocitations (case 2 in the diagram above) can be quite
complex. Some might simply mention a single page, but others might list a
series of (not necessarily contiguous) pages, as well as figures, plates etc.
Converting these to citable identifiers can be tricky, especially as in most
cases we don''t have page-level identifiers. The Biodiversity Heritage Library
(BHL) does have URLs for each scanned page, and we have a standard for referring
to pages in a PDF (page=<pageNum>, see RFC
8118). But how do we refer to a set of pages? Do we pick the first page?
Do we try and represent a set of pages, and if so, how?
Another issue with page-level identifiers is that not everything on a given page may be relevant to the taxonomic name. In case 2 above I''ve shaded in the parts of the pages and figure that refer to the taxonomic name. An example where this can be problematic is the recent test case I created for BHL where a page image was included for the taxonomic name Aphrophora impressa. The image includes the species description and a illustration, as well as text that relates to other species.
\n\n![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEg1SAv1XiVBPHXeMLPfdsnTh4Sj4m0AQyqjXM0faXyvbNhBXFDl7SawjFIkMo3qz4RQhDEyZXkKAh5nF4gtfHbVXA6cK3NJ46CuIWECC6HmJKtTjoZ0M3QQXvLY_X-2-RecjBqEy68M0cEr0ph3l6KY51kA9BvGt9d4id314P71PBitpWMATg/s3467/https---www.biodiversitylibrary.org-pageimage-29138463.jpeg\")
Given that not everything on a page need be relevant, we could extract just the relevant blocks of text and illustrations (e.g., paragraphs of text, panels within a figure, etc.) and treat that set of elements as the thing to cite. This is, of course, what Plazi are doing. The set of extracted blocks is glued together as a \"treatment\", assigned an identifier (often a DOI), and treated as a citable unit. It would be interesting to see to what extent these treatments are actually cited, for example, do subsequent revisions that cite work that include treatments cite those treatments, or just the work itself? Put another way, are we creating \"threads\" between taxonomic revisions?
\n\nOne reason for these notes is that I''m exploring uploading taxonomic name - literature links to ChecklistBank and case 1 above is easy, as is case 3 (if we have treatment-level identifiers). But case 2 is problematic because we are linking to a set of things that may not have an identifier, which means a decision has to be made about which page to link to, and how to refer to that page.
","tags":[],"language":"en","references":[]},{"id":"https://doi.org/10.59350/ymc6x-rx659","uuid":"0807f515-f31d-4e2c-9e6f-78c3a9668b9d","url":"https://iphylo.blogspot.com/2022/09/dna-barcoding-as-intergenerational.html","title":"DNA barcoding as intergenerational transfer of taxonomic knowledge","summary":"I tweeted about this but want to bookmark it for later as well. The paper “A molecular-based identification resource for the arthropods of Finland” doi:10.1111/1755-0998.13510 contains the following: …the annotated barcode records assembled by FinBOL participants represent a tremendous intergenerational transfer of taxonomic knowledge … the time contributed by current taxonomists in identifying and contributing voucher specimens represents a great gift to future generations who will benefit...","date_published":"2022-09-14T10:12:00Z","date_modified":"2022-09-29T13:57:30Z","date_indexed":"1909-06-16T11:02:21+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"I tweeted about this but want to bookmark it for later as well. The paper “A molecular-based identification resource for the arthropods of Finland” doi:10.1111/1755-0998.13510 contains the following:
\n\n\n…the annotated barcode records assembled by FinBOL participants represent a tremendous intergenerational transfer of taxonomic knowledge … the time contributed by current taxonomists in identifying and contributing voucher specimens represents a great gift to future generations who will benefit from their expertise when they are no longer able to process new material.
\n
I think this is a very clever way to characterise the project. In an age of machine learning this may be commonest way to share knowledge , namely as expert-labelled training data used to build tools for others. Of course, this means the expertise itself may be lost, which has implications for updating the models if the data isn’t complete. But it speaks to Charles Godfrey’s theme of “Taxonomy as information science”.
\nNote that the knowledge is also transformed in the sense that the underlying expertise of interpreting morphology, ecology, behaviour, genomics, and the past literature is not what is being passed on. Instead it is probabilities that a DNA sequence belongs to a particular taxon.
\nThis feels is different to, say iNaturalist, where there is a machine learning model to identify images. In that case, the model is built on something the community itself has created, and continues to create. Yes, the underlying idea is that same: “experts” have labelled the data, a model is trained, the model is used. But the benefits of the iNaturalist model are immediately applicable to the people whose data built the model. In the case of barcoding, because the technology itself is still not in the hands of many (relative to, say, digital imaging), the benefits are perhaps less tangible. Obviously researchers working with environmental DNA will find it very useful, but broader impact may await the arrival of citizen science DNA barcoding.
\nThe other consideration is whether the barcoding helps taxonomists. Is it to be used to help prioritise future work (“we are getting lots of unknown sequences in these taxa, lets do some taxonomy there”), or is it simply capturing the knowledge of a generation that won’t be replaced:
\n\n\nThe need to capture such knowledge is essential because there are, for example, no young Finnish taxonomists who can critically identify species in many key groups of ar- thropods (e.g., aphids, chewing lice, chalcid wasps, gall midges, most mite lineages).
\n
The cycle of collect data, test and refine model, collect more data, rinse and repeat that happens with iNaturalist creates a feedback loop. It’s not clear that a similar cycle exists for DNA barcoding.
\n\n","tags":[],"language":"en","references":[]},{"id":"https://doi.org/10.59350/d3dc0-7an69","uuid":"545c177f-cea5-4b79-b554-3ccae9c789d7","url":"https://iphylo.blogspot.com/2021/10/reflections-on-macroscope-tool-for-21st.html","title":"Reflections on \"The Macroscope\" - a tool for the 21st Century?","summary":"This is a guest post by Tony Rees. It would be difficult to encounter a scientist, or anyone interested in science, who is not familiar with the microscope, a tool for making objects visible that are otherwise too small to be properly seen by the unaided eye, or to reveal otherwise invisible fine detail in larger objects. A select few with a particular interest in microscopy may also have encountered the Wild-Leica \"Macroscope\", a specialised type of benchtop microscope optimised for...","date_published":"2021-10-07T12:38:00Z","date_modified":"2021-10-08T10:26:22Z","date_indexed":"1909-06-16T10:02:25+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"Written with StackEdit.
\n
![\"YtNkVT2U\"](\"https://lh3.googleusercontent.com/-A99btr6ERMs/Vl1Wvjp2OtI/AAAAAAAAEFI/7bKdRjNG5w0/ytNkVT2U.jpg?imgmax=800\" "\\"ytNkVT2U.jpg\\"")
This is a guest post by Tony
Rees.
It would be difficult to encounter a scientist, or anyone interested in science, who is not familiar with the microscope, a tool for making objects visible that are otherwise too small to be properly seen by the unaided eye, or to reveal otherwise invisible fine detail in larger objects. A select few with a particular interest in microscopy may also have encountered the Wild-Leica \"Macroscope\", a specialised type of benchtop microscope optimised for low-power macro-photography. However in this overview I discuss the \"Macroscope\" in a different sense, which is that of the antithesis to the microscope: namely a method for visualizing subjects too large to be encompassed by a single field of vision, such as the Earth or some subset of its phenomena (the biosphere, for example), or conceptually, the universe.
\n\n![\"\"](\"https://1.bp.blogspot.com/-xXxH9aJn1tY/YV7vnKGQWPI/AAAAAAAAgyQ/7CSJJ663Ry4IXtav54nLcLiI0kda5_L7ACLcBGAsYHQ/s500/2020045672.jpg\")
From the standpoint of the 21st century, it seems that we are closer to achieving a \"macroscope\" (or possibly, multiple such tools) than ever before, based on the availability of existing and continuing new data streams, improved technology for data assembly and storage, and advanced ways to query and combine these large streams of data to produce new visualizations, data products, and analytical findings. I devote the remainder of this article to examples where either particular workers have employed \"macroscope\" terminology to describe their activities, or where potentially equivalent actions are taking place without the explicit \"macroscope\" association, but are equally worthy of consideration. To save space here, references cited here (most or all) can be found via a Wikipedia article entitled \"Macroscope (science concept)\" that I authored on the subject around a year ago, and have continued to add to on occasion as new thoughts or information come to hand (see edit history for the article).
\n\nFirst, one can ask, what constitutes a macroscope, in the present context? In the Wikipedia article I point to a book \"Big Data - Related Technologies, Challenges and Future Prospects\" by Chen et al. (2014) (doi:10.1007/978-3-319-06245-7), in which the \"value chain of big data\" is characterised as divisible into four phases, namely data generation, data acquisition (aka data assembly), data storage, and data analysis. To my mind, data generation (which others may term acquisition, differently from the usage by Chen et al.) is obviously the first step, but does not in itself constitute the macroscope, except in rare cases - such as Landsat imagery, perhaps - where on its own, a single co-ordinated data stream is sufficient to meet the need for a particular type of \"global view\". A variant of this might be a coordinated data collection program - such as that of the ten year Census of Marine Life - which might produce the data required for the desired global view; but again, in reality, such data are collected in a series of discrete chunks, in many and often disparate data formats, and must be \"wrangled\" into a more coherent whole before any meaningful \"macroscope\" functionality becomes available.
\n\nHere we come to what, in my view, constitutes the heart of the \"macroscope\": an intelligently organized (i.e. indexable and searchable), coherent data store or repository (where \"data\" may include imagery and other non numeric data forms, but much else besides). Taking the Census of Marine Life example, the data repository for that project''s data (plus other available sources as inputs) is the Ocean Biodiversity Information System or OBIS (previously the Ocean Biogeographic Information System), which according to this view forms the \"macroscope\" for which the Census data is a feed. (For non habitat-specific biodiversity data, GBIF is an equivalent, and more extensive, operation). Other planetary scale \"macroscopes\", by this definition (which may or may not have an explicit geographic, i.e. spatial, component) would include inventories of biological taxa such as the Catalogue of Life and so on, all the way back to the pioneering compendia published by Linnaeus in the eighteenth century; while for cartography and topographic imagery, the current \"blockbuster\" of Google Earth and its predecessors also come well into public consciousness.
\n\nIn the view of some workers and/or operations, both of these phases are precursors to the real \"work\" of the macroscope which is to reveal previously unseen portions of the \"big picture\" by means either of the availability of large, synoptic datasets, or fusion between different data streams to produce novel insights. Companies such as IBM and Microsoft have used phraseology such as:
\n\nBy 2022 we will use machine-learning algorithms and software to help us organize information about the physical world, helping bring the vast and complex data gathered by billions of devices within the range of our vision and understanding. We call this a \"macroscope\" – but unlike the microscope to see the very small, or the telescope that can see far away, it is a system of software and algorithms to bring all of Earth''s complex data together to analyze it by space and time for meaning.\" (IBM)\n\n
As the Earth becomes increasingly instrumented with low-cost, high-bandwidth sensors, we will gain a better understanding of our environment via a virtual, distributed whole-Earth \"macroscope\"... Massive-scale data analytics will enable real-time tracking of disease and targeted responses to potential pandemics. Our virtual \"macroscope\" can now be used on ourselves, as well as on our planet.\" (Microsoft) (references available via the Wikipedia article cited above).\n\n
Whether or not the analytical capabilities described here are viewed as being an integral part of the \"macroscope\" concept, or are maybe an add-on, is ultimately a question of semantics and perhaps, personal opinion. Continuing the Census of Marine Life/OBIS example, OBIS offers some (arguably rather basic) visualization and summary tools, but also makes its data available for download to users wishing to analyse it further according to their own particular interests; using OBIS data in this manner, Mark Costello et al. in 2017 were able to demarcate a finite number of data-supported marine biogeographic realms for the first time (Costello et al. 2017: Nature Communications. 8: 1057. doi:10.1038/s41467-017-01121-2), a project which I was able to assist in a small way in an advisory capacity. In a case such as this, perhaps the final function of the macroscope, namely data visualization and analysis, was outsourced to the authors'' own research institution. Similarly at an earlier phase, \"data aggregation\" can also be virtual rather than actual, i.e. avoiding using a single physical system to hold all the data, enabled by open web mapping standards WMS (web map service) and WFS (web feature service) to access a set of distributed data stores, e.g. as implemented on the portal for the Australian Ocean Data Network.
\n\nSo, as we pass through the third decade of the twenty first century, what developments await us in the \"macroscope\" area\"? In the biodiversity space, one can reasonably presume that the existing \"macroscopic\" data assembly projects such as OBIS and GBIF will continue, and hopefully slowly fill current gaps in their coverage - although in the marine area, strategic new data collection exercises may be required (Census 2020, or 2025, anyone?), while (again hopefully), the Catalogue of Life will continue its progress towards a \"complete\" species inventory for the biosphere. The Landsat project, with imagery dating back to 1972, continues with the launch of its latest satellite Landsat 9 just this year (21 September 2021) with a planned mission duration for the next 5 years, so the \"macroscope\" functionality of that project seems set to continue for the medium term at least. Meanwhile the ongoing development of sensor networks, both on land and in the ocean, offers an exciting new method of \"instrumenting the earth\" to obtain much more real time data than has ever been available in the past, offering scope for many more, use case-specific \"macroscopes\" to be constructed that can fuse (e.g.) satellite imagery with much more that is happening at a local level.
\n\nSo, the \"macroscope\" concept appears to be alive and well, even though the nomenclature can change from time to time (IBM''s \"Macroscope\", foreshadowed in 2017, became the \"IBM Pairs Geoscope\" on implementation, and is now simply the \"Geospatial Analytics component within the IBM Environmental Intelligence Suite\" according to available IBM publicity materials). In reality this illustrates a new dichotomy: even if \"everyone\" in principle has access to huge quantities of publicly available data, maybe only a few well funded entities now have the computational ability to make sense of it, and can charge clients a good fee for their services...
\n\nI present this account partly to give a brief picture of \"macroscope\" concepts today and in the past, for those who may be interested, and partly to present a few personal views which would be out of scope in a \"neutral point of view\" article such as is required on Wikipedia; also to see if readers of this blog would like to contribute further to discussion of any of the concepts traversed herein.
","tags":["guest post","macroscope"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/gf1dw-n1v47","uuid":"a41163e0-9c9a-41e0-a141-f772663f2f32","url":"https://iphylo.blogspot.com/2023/03/dugald-stuart-page-1936-2022.html","title":"Dugald Stuart Page 1936-2022","summary":"My dad died last weekend. Below is a notice in today''s New Zealand Herald. I''m in New Zealand for his funeral. Don''t really have the words for this right now.","date_published":"2023-03-14T03:00:00Z","date_modified":"2023-03-22T07:25:56Z","date_indexed":"1909-06-16T10:41:55+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjZweukxntl7R5jnk3knVFVrqZ5RxC7mPZBV4gKeDIglbFzs2O442nbxqs8t8jV2tLqCU24K6gS32jW-Pe8q3O_5JR1Ms3qW1aQAZ877cKkFfcUydqUba9HsgNlX-zS9Ne92eLxRGS8F-lStTecJw2oalp3u58Yoc0oM7CUin5LKPeFIJ7Rzg/s3454/_DSC5106.jpg\")
![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiRUTOFF1VWHCl8dg3FQuaWy5LM7aX8IivdRpTtzgrdQTEymsA5bLTZE3cSQf1WQIP3XrC46JsLScP8BxTK9C5a-B1i51yg8WGSJD0heJVaoDLnerv0lD1o3qloDjqEuuyfX4wagHB5YYBmjWnGeVQvyYVngvDDf9eM6pmMtZ7x94Y4jSVrug/s3640/IMG_2870.jpeg\")
Some quick notes on interface ideas for digital libraries and/or knowledge graphs.
\nRecently there’s been something of an explosion in bibliographic tools to explore the literature. Examples include:
\nAs an aside, I think these (and similar tools) are a great example of how bibliographic data such as abstracts, the citation graph and - to a lesser extent - full text - have become commodities. That is, what was once proprietary information is now free to anyone, which in turns means a whole ecosystem of new tools can emerge. If I was clever I’d be building a Wardley map to explore this. Note that a decade or so ago reference managers like Zotero were made possible by publishers exposing basic bibliographic data on their articles. As we move to open citations we are seeing the next generation of tools.
\nBack to my main topic. As usual, rather than focus on what these tools do I’m more interested in how they look. I have history here, when the iPad came out I was intrigued by the possibilities it offered for displaying academic articles, as discussed here, here, here, here, and here. ResearchRabbit looks like this:
\n\nScispace’s “trace” view looks like this:
\n\nWhat is interesting about both is that they display content from left to right in vertical columns, rather than the more common horizontal rows. This sort of display is sometimes called Miller columns or a cascading list.
\n\n![\"\"](\"https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEjBPnV9fRBcvm-BX5PjzfG5Cff9PerCLsTW8d5ZbsL6b41t7ypD7ovmcgfTf3b4b34mbq8NM4sfwOHkgEq32FLYnD497RFQD4HQmYmh5Eveu1zWdDVyKyDtPyE98QoxTaOEnLA5kK0fnl3dOOEgUvtVKlTZ8bt1gj2v_8tDRWl9f50ybyei3A/s1024/GNUstep-liveCD.png\")
By Gürkan Sengün (talk) - Own work, Public Domain, https://commons.wikimedia.org/w/index.php?curid=594715
\nI’ve always found displaying a knowledge graph to be a challenge, as discussed elsewhere on this blog and in my paper on Ozymandias. Miller columns enable one to drill down in increasing depth, but it doesn’t need to be a tree, it can be a path within a network. What I like about ResearchRabbit and the original Scispace interface is that they present the current item together with a list of possible connections (e.g., authors, citations) that you can drill down on. Clicking on these will result in a new column being appended to the right, with a view (typically a list) of the next candidates to visit. In graph terms, these are adjacent nodes to the original item. The clickable badges on each item can be thought of as sets of edges that have the same label (e.g., “authored by”, “cites”, “funded”, “is about”, etc.). Each of these nodes itself becomes a starting point for further exploration. Note that the original starting point isn’t privileged, other than being the starting point. That is, each time we drill down we are seeing the same type of information displayed in the same way. Note also that the navigation can be though of as a card for a node, with buttons grouping the adjacent nodes. When we click on an individual button, it expands into a list in the next column. This can be thought of as a preview for each adjacent node. Clicking on an element in the list generates a new card (we are viewing a single node) and we get another set of buttons corresponding to the adjacent nodes.
\nOne important behaviour in a Miller column interface is that the current path can be pruned at any point. If we go back (i.e., scroll to the left) and click on another tab on an item, everything downstream of that item (i.e., to the right) gets deleted and replaced by a new set of nodes. This could make retrieving a particular history of browsing a bit tricky, but encourages exploration. Both Scispace and ResearchRabbit have the ability to add items to a collection, so you can keep track of things you discover.
\nLots of food for thought, I’m assuming that there is some user interface/experience research on Miller columns. One thing to remember is that Miller columns are most often associated with trees, but in this case we are exploring a network. That means that potentially there is no limit to the number of columns being generated as we wander through the graph. It will be interesting to think about what the average depth is likely to be, in other words, how deep down the rabbit hole will be go?
\n\nShould add link to David Regev''s explorations of Flow Browser.\n\n
\n","tags":["cards","flow","Knowledge Graph","Miller column","RabbitResearch"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/t6fb9-4fn44","uuid":"8bc3fea6-cb86-4344-8dad-f312fbf58041","url":"https://iphylo.blogspot.com/2021/12/the-business-of-extracting-knowledge.html","title":"The Business of Extracting Knowledge from Academic Publications","summary":"Markus Strasser (@mkstra write a fascinating article entitled \"The Business of Extracting Knowledge from Academic Publications\". I spent months working on domain-specific search engines and knowledge discovery apps for biomedicine and eventually figured that synthesizing \"insights\" or building knowledge graphs by machine-reading the academic literature (papers) is *barely useful* :https://t.co/eciOg30Odc— Markus Strasser (@mkstra) December 7, 2021 His TL;DR: TL;DR: I worked on biomedical...","date_published":"2021-12-11T00:01:00Z","date_modified":"2021-12-11T00:01:21Z","date_indexed":"1909-06-16T11:32:09+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"Written with StackEdit.
\n
Markus Strasser (@mkstra write a fascinating article entitled \"The Business of Extracting Knowledge from Academic Publications\".
\n\n\n\nI spent months working on domain-specific search engines and knowledge discovery apps for biomedicine and eventually figured that synthesizing "insights" or building knowledge graphs by machine-reading the academic literature (papers) is *barely useful* :https://t.co/eciOg30Odc
— Markus Strasser (@mkstra) December 7, 2021
His TL;DR:
\n\n\nTL;DR: I worked on biomedical literature search, discovery and recommender web applications for many months and concluded that extracting, structuring or synthesizing \"insights\" from academic publications (papers) or building knowledge bases from a domain corpus of literature has negligible value in industry.\n\n\n\nClose to nothing of what makes science actually work is published as text on the web.\n
After recounting the many problems of knowledge extraction - including a swipe at nanopubs which \"are ... dead in my view (without admitting it)\" - he concludes:
\n\n\nI’ve been flirting with this entire cluster of ideas including open source web annotation, semantic search and semantic web, public knowledge graphs, nano-publications, knowledge maps, interoperable protocols and structured data, serendipitous discovery apps, knowledge organization, communal sense making and academic literature/publishing toolchains for a few years on and off ... nothing of it will go anywhere.\n\n\n\nDon’t take that as a challenge. Take it as a red flag and run. Run towards better problems.\n
Well worth a read, and much food for thought.
","tags":["ai","business model","text mining"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/463yw-pbj26","uuid":"dc829ab3-f0f1-40a4-b16d-a36dc0e34166","url":"https://iphylo.blogspot.com/2022/12/david-remsen.html","title":"David Remsen","summary":"I heard yesterday from Martin Kalfatovic (BHL) that David Remsen has died. Very sad news. It''s starting to feel like iPhylo might end up being a list of obituaries of people working on biodiversity informatics (e.g., Scott Federhen). I spent several happy visits at MBL at Woods Hole talking to Dave at the height of the uBio project, which really kickstarted large scale indexing of taxonomic names, and the use of taxonomic name finding tools to index the literature. His work on uBio with David...","date_published":"2022-12-16T17:54:00Z","date_modified":"2022-12-17T08:12:23Z","date_indexed":"1909-06-16T11:41:39+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"I heard yesterday from Martin Kalfatovic (BHL) that David Remsen has died. Very sad news. It''s starting to feel like iPhylo might end up being a list of obituaries of people working on biodiversity informatics (e.g., Scott Federhen).
\n\nI spent several happy visits at MBL at Woods Hole talking to Dave at the height of the uBio project, which really kickstarted large scale indexing of taxonomic names, and the use of taxonomic name finding tools to index the literature. His work on uBio with David (\"Paddy\") Patterson led to the Encyclopedia of Life (EOL).
\n\nA number of the things I''m currently working on are things Dave started. For example, I recently uploaded a version of his dataset for Nomenclator Zoologicus[1] to ChecklistBank where I''m working on augmenting that original dataset by adding links to the taxonomic literature. My BioRSS project is essentially an attempt to revive uBioRSS[2] (see Revisiting RSS to monitor the latest taxonomic research).
\n\nI have fond memories of those visits to Woods Hole. A very sad day indeed.
\n\nUpdate: The David Remsen Memorial Fund has been set up on GoFundMe.
\n\n1. Remsen, D. P., Norton, C., & Patterson, D. J. (2006). Taxonomic Informatics Tools for the Electronic Nomenclator Zoologicus. The Biological Bulletin, 210(1), 18–24. https://doi.org/10.2307/4134533
\n\n2. Patrick R. Leary, David P. Remsen, Catherine N. Norton, David J. Patterson, Indra Neil Sarkar, uBioRSS: Tracking taxonomic literature using RSS, Bioinformatics, Volume 23, Issue 11, June 2007, Pages 1434–1436, https://doi.org/10.1093/bioinformatics/btm109
","tags":["David Remsen","obituary","uBio"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/3s376-6bm21","uuid":"62e7b438-67a3-44ac-a66d-3f5c278c949e","url":"https://iphylo.blogspot.com/2022/02/deduplicating-bibliographic-data.html","title":"Deduplicating bibliographic data","summary":"There are several instances where I have a collection of references that I want to deduplicate and merge. For example, in Zootaxa has no impact factor I describe a dataset of the literature cited by articles in the journal Zootaxa. This data is available on Figshare (https://doi.org/10.6084/m9.figshare.c.5054372.v4), as is the equivalent dataset for Phytotaxa (https://doi.org/10.6084/m9.figshare.c.5525901.v1). Given that the same articles may be cited many times, these datasets have lots of...","date_published":"2022-02-03T15:09:00Z","date_modified":"2022-02-03T15:11:29Z","date_indexed":"1909-06-16T10:22:30+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"There are several instances where I have a collection of references that I want to deduplicate and merge. For example, in Zootaxa has no impact factor I describe a dataset of the literature cited by articles in the journal Zootaxa. This data is available on Figshare (https://doi.org/10.6084/m9.figshare.c.5054372.v4), as is the equivalent dataset for Phytotaxa (https://doi.org/10.6084/m9.figshare.c.5525901.v1). Given that the same articles may be cited many times, these datasets have lots of duplicates. Similarly, articles in Wikispecies often have extensive lists of references cited, and the same reference may appear on multiple pages (for an initial attempt to extract these references see https://doi.org/10.5281/zenodo.5801661 and https://github.com/rdmpage/wikispecies-parser).
\n\nThere are several reasons I want to merge these references. If I want to build a citation graph for Zootaxa or Phytotaxa I need to merge references that are the same so that I can accurate count citations. I am also interested in harvesting the metadata to help find those articles in the Biodiversity Heritage Library (BHL), and the literature cited section of scientific articles is a potential goldmine of bibliographic metadata, as is Wikispecies.
\n\nAfter various experiments and false starts I''ve created a repository https://github.com/rdmpage/bib-dedup to host a series of PHP scripts to deduplicate bibliographics data. I''ve settled on using CSL-JSON as the format for bibliographic data. Because deduplication relies on comparing pairs of references, the standard format for most of the scripts is a JSON array containing a pair of CSL-JSON objects to compare. Below are the steps the code takes.
\n\nThe first step is to take a list of references and generate the pairs that will be compared. I started with this approach as I wanted to explore machine learning and wanted a simple format for training data, such as an array of two CSL-JSON objects and an integer flag representing whether the two references were the same of different.
\n\nThere are various ways to generate CSL-JSON for a reference. I use a tool I wrote (see Citation parsing tool released) that has a simple API where you parse one or more references and it returns that reference as structured data in CSL-JSON.
\n\nAttempting to do all possible pairwise comparisons rapidly gets impractical as the number of references increases, so we need some way to restrict the number of comparisons we make. One approach I''ve explored is the “sorted neighbourhood method” where we sort the references 9for example by their title) then move a sliding window down the list of references, comparing all references within that window. This greatly reduces the number of pairwise comparisons. So the first step is to sort the references, then run a sliding window over them, output all the pairs in each window (ignoring in pairwise comparisons already made in a previous window). Other methods of \"blocking\" could also be used, such as only including references in a particular year, or a particular journal.
\n\nSo, the output of this step is a set of JSON arrays, each with a pair of references in CSL-JSON format. Each array is stored on a single line in the same file in line-delimited JSON (JSONL).
\n\nThe next step is to compare each pair of references and decide whether they are a match or not. Initially I explored a machine learning approach used in the following paper:
\n\n\nWilson DR. 2011. Beyond probabilistic record linkage: Using neural networks and complex features to improve genealogical record linkage. In: The 2011 International Joint Conference on Neural Networks. 9–14. DOI: 10.1109/IJCNN.2011.6033192\n\n\n
Initial experiments using https://github.com/jtet/Perceptron were promising and I want to play with this further, but I deciding to skip this for now and just use simple string comparison. So for each CSL-JSON object I generate a citation string in the same format using CiteProc, then compute the Levenshtein distance between the two strings. By normalising this distance by the length of the two strings being compared I can use an arbitrary threshold to decide if the references are the same or not.
\n\nFor this step we read the JSONL file produced above and record whether the two references are a match or not. Assuming each reference has a unique identifier (needs only be unique within the file) then we can use those identifier to record the clusters each reference belongs to. I do this using a Disjoint-set data structure. For each reference start with a graph where each node represents a reference, and each node has a pointer to a parent node. Initially the reference is its own parent. A simple implementation is to have an array index by reference identifiers and where the value of each cell in the array is the node''s parent.
\n\nAs we discover pairs we update the parents of the nodes to reflect this, such that once all the comparisons are done we have a one or more sets of clusters corresponding to the references that we think are the same. Another way to think of this is that we are getting the components of a graph where each node is a reference and pair of references that match are connected by an edge.
\n\nIn the code I''m using I write this graph in Trivial Graph Format (TGF) which can be visualised using a tools such as yEd.
\n\nNow that we have a graph representing the sets of references that we think are the same we need to merge them. This is where things get interesting as the references are similar (by definition) but may differ in some details. The paper below describes a simple Bayesian approach for merging records:
\n\n\nCouncill IG, Li H, Zhuang Z, Debnath S, Bolelli L, Lee WC, Sivasubramaniam A, Giles CL. 2006. Learning Metadata from the Evidence in an On-line Citation Matching Scheme. In: Proceedings of the 6th ACM/IEEE-CS Joint Conference on Digital Libraries. JCDL ’06. New York, NY, USA: ACM, 276–285. DOI: 10.1145/1141753.1141817.\n\n\n
So the next step is to read the graph with the clusters, generate the sets of bibliographic references that correspond to each cluster, then use the method described in Councill et al. to produce a single bibliographic record for that cluster. These records could then be used to, say locate the corresponding article in BHL, or populate Wikidata with missing references.
\n\nObviously there is always the potential for errors, such as trying to merge references that are not the same. As a quick and dirty check I flag as dubious any cluster where the page numbers vary among members of the cluster. More sophisticated checks are possible, especially if I go down the ML route (i.e., I would have evidence for the probability that the same reference can disagree on some aspects of metadata).
\n\nAt this stage the code is working well enough for me to play with and explore some example datasets. The focus is on structured bibliographic metadata, but I may simplify things and have a version that handles simple string matching, for example to cluster together different abbreviations of the same journal name.
","tags":["data cleaning","deduplication","Phytotaxa","Wikispecies","Zootaxa"],"language":"en","references":[]},{"id":"https://doi.org/10.59350/c79vq-7rr11","uuid":"3cb94422-5506-4e24-a41c-a250bb521ee0","url":"https://iphylo.blogspot.com/2021/12/graphql-for-wikidata-wikicite.html","title":"GraphQL for WikiData (WikiCite)","summary":"I''ve released a very crude GraphQL endpoint for WikiData. More precisely, the endpoint is for a subset of the entities that are of interest to WikiCite, such as scholarly articles, people, and journals. There is a crude demo at https://wikicite-graphql.herokuapp.com. The endpoint itself is at https://wikicite-graphql.herokuapp.com/gql.php. There are various ways to interact with the endpoint, personally I like the Altair GraphQL Client by Samuel Imolorhe. As I''ve mentioned earlier it''s taken...","date_published":"2021-12-20T13:16:00Z","date_modified":"2021-12-20T13:20:05Z","date_indexed":"1909-06-16T10:52:00+00:00","authors":[{"url":null,"name":"Roderic Page"}],"image":null,"content_html":"![\"\"](\"https://blogger.googleusercontent.com/img/a/AVvXsEh7WW8TlfN2u3xe_E-sH5IK6AWYnAoWaKrP2b32UawUeqMpPlq6ZFk5BtJVZsMmCNh5j3QRsTj5H0Ee55RRGntnc9yjj_mNB8KHmH2dzocCgyLS2VFOxsBji6u4Ey6qxlDAnT-zrsBpnDcTchbhgt1x0Sf7RkmIMkS1y4-_3KCQian-SeIF-g=s200\"/)
I''ve released a very crude GraphQL endpoint for WikiData. More precisely, the endpoint is for a subset of the entities that are of interest to WikiCite, such as scholarly articles, people, and journals. There is a crude demo at https://wikicite-graphql.herokuapp.com. The endpoint itself is at https://wikicite-graphql.herokuapp.com/gql.php. There are various ways to interact with the endpoint, personally I like the Altair GraphQL Client by Samuel Imolorhe.
\n\nAs I''ve mentioned earlier it''s taken me a while to see the point of GraphQL. But it is clear it is gaining traction in the biodiversity world (see for example the GBIF Hosted Portals) so it''s worth exploring. My take on GraphQL is that it is a way to create a self-describing API that someone developing a web site can use without them having to bury themselves in the gory details of how data is internally modelled. For example, WikiData''s query interface uses SPARQL, a powerful language that has a steep learning curve (in part because of the administrative overhead brought by RDF namespaces, etc.). In my previous SPARQL-based projects such as Ozymandias and ALEC I have either returned SPARQL results directly (Ozymandias) or formatted SPARQL results as schema.org DataFeeds (equivalent to RSS feeds) (ALEC). Both approaches work, but they are project-specific and if anyone else tried to build based on these projects they might struggle for figure out what was going on. I certainly struggle, and I wrote them!
\n\nSo it seems worthwhile to explore this approach a little further and see if I can develop a GraphQL interface that can be used to build the sort of rich apps that I want to see. The demo I''ve created uses SPARQL under the hood to provide responses to the GraphQL queries. So in this sense it''s not replacing SPARQL, it''s simply providing a (hopefully) simpler overlay on top of SPARQL so that we can retrieve the data we want without having to learn the intricacies of SPARQL, nor how Wikidata models publications and people.
","tags":["GraphQL","SPARQL","WikiCite","Wikidata"],"language":"en","references":[]}]}' recorded_at: Sun, 18 Jun 2023 06:09:05 GMT recorded_with: VCR 6.1.0