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March 18th, 2013

BibleTech talk: Automatically Learning Topical Content

I’ve posted the slides from my BibleTech 2013 talk. Here’s the abstract:

Continued work on the Logos Controlled Vocabulary (BibleTech 2010, “A Controlled Vocabulary for Biblical Studies”) has produced a unique collection of topic-aligned content across more than 50 different Bible dictionaries, encyclopedias, and topical indexes in both English and Spanish. This presentation will describe the information we’re learning automatically from this content, including:

  • determining concept importance
  • associating concepts with Bible references
  • extracting and associating names and descriptive terms for concepts
  • relating concepts to each other

You can see the other talks at the BibleTech website. I’ve had a number of positive comments on the talk, which is always gratifying. Slowly but surely, we’re climbing up the data stack …


3/21/2013 update: I’ve used the Slidy framework for presentations for several years because i like the way it puts the whole content out on the web in HTML. However, @JohnRGentry pointed out that my slides don’t work on the iPad because moving forward and backward requires keys. There’s a newer version of Slidy which does support swiping to move through slides, though my experience with it on both Safari and Chrome on iOS hasn’t been great: it’s not easy to register a swipe, and title text sometimes gets lost. I assume these are issues with javascript support on iOS, though i’m not really sure. I’ll try to update my slides to the newest version of Slidy, which will help a little, but i’ll also look for another framework that’s more tablet friendly.

December 14th, 2012

Logos 5, Behind the Curtain: Curation

This post is part of Logos 5, Behind the Curtain, a series of blog posts looking at new data sets that are part of the latest Bible study application from Logos Bible Software.


At the heart of Logos’ approach to data is the practice of curation. If you’ve heard this term at all, it’s probably in the context of museums: but we mean something rather different. As usual, Scott Adams’ Dilbert has the pulse of corporate America:

The Official Dilbert Website featuring Scott Adams Dilbert strips, animations and more

For Logos, though, curation is not just trendy new jargon, but an essential practice that we’ve been pursing for quite a few years now (five under my direction, and more before my tenure). It’s a critical part of what makes Logos unique in its market. For example, we have not one but three Lexical Curators working on the Bible Sense Lexicon (one for Greek, two for Hebrew). To my knowledge (and Google’s), these appear to be the only people in the world with this job title. In fact, most people in the Content Innovation department at Logos are doing one kind of curation task or another.

So what is curation in the context of Bible study software, anyway? In the simplest terms, curation means organizing and maintaining a collection of things. In the museum context, that’s artifacts they display. Our kind of curation involves computer-readable data that captures knowledge relevant to biblical studies. You can summarize it briefly with three key practices:

  1. Collect
  2. Correct
  3. Connect

(“Describe” is probably a more apt term than “Correct”, but i just couldn’t resist the awesome alliteration.)

To “Collect” means imposing organization on a complicated and messy world of information, with an eye toward structuring it and making it useful in some way in the Logos system. Often this information is expressed in prose sentences in a reference book: for example, a Bible dictionary might describe a city, and include language about where the city was located, what larger region it was part of, where it’s mentioned in the Bible, etc. Fundamentally, a plan for collection requires deciding what information ought to be collected, and which things belong in the collection and which ones are excluded (in more technical terms, an ontology).

Capturing and formalizing knowledge typically involves some tradeoffs. First of all, we have to find categories and labels that balance and maximize utility and (formal) correctness. When it comes to categorization in particular, ultimately “everything is miscellaneous” (which is also the title of a really important book by David Weinberger), and if you push hard enough, each thing is its own unique category . But data sets are typically more useful if things are grouped in some way. So we categorize the following as “people” in our data set:

  • individuals (whether named or not)
  • groups, whether defined by residence (Greeks), common ancestry (Levites), belief systems (Pharisees and Sadducees) , etc.
  • supernatural entities (including those that most Bible readers would accept like the God of Israel, and those that biblical authors argue are not real at all, like Baal, Asherah, or Zeus)

To “Correct” or “Describe” means that we choose and populate particular attributes for the items we’re curating. In the case of people, that includes things like names, gender, and roles. In addition, we create three special attributes for nearly every data set:

  • a unique identifier: though you’ll probably know who i mean when i use a biblical name like “David”, you won’t for “John” because there are five different individuals known by that name. This kind of ambiguity, and other variations (“John”, “John the Baptist”, “John Baptizer”, etc.) means names are usually poor identifiers. Instead, we create a symbol like “John.4″ that uniquely identifies one particular item in our collection (in this case, a member of the Sanhedrin mentioned in Acts 4:6). Since we don’t show these to users, we don’t have to worry about people understanding them.
  • a label: since our data is for people to look at and understand, we need user-friendly ways to display an entity. Labels are typically brief (less than 20 -25 characters), and also unique, so that in a drop-down list showing names that match “John”, i can distinguish “John (the Baptist)” from “John (Ac 4:6)”. Since the label is also unique, we could use it as the identifier: but since data stability is a primary goal, we separate the two, so that we can change the label if necessary. Since identifiers (not labels) are the means by which we connect data, we (almost) never change them, since that risks breaking the integrity of the data set.
  • a description: labels are brief so they take up minimum space, but consequently, they can’t carry much information. So we often provide a longer prose description, perhaps a sentence or two, that helps identify the entity and its most basic information. You could compare this to the leading sentence in a Wikipedia article. In the case of John.4, that’s “a member of the family of high priests in Jerusalem following Jesus’ ascension.”, which is probably enough to help you decide whether this is a John you want to know more about or not.

To “Connect” means linking entities to other entities (or other data sets). For people, family relationships are an important ways that people connect to each other. We also label those relationship (father, mother, sister), and, for biblical information, capture the textual sources that support this relationship (more technically, the provenance of the data).

Connecting information is one of the most important aspects of curation for Logos. While it may be interesting to learn that King David was also a shepherd, that’s an isolated fact. But if you can get a list of other individuals in the Bible who were also shepherds (or kings, or musicians), now you’re discovering new information. You might not have started out looking for this, or known how to find it for yourself.

Question: which part of Logos’ curation process (Collect, Correct, Connect) do you find the most interesting or appealing? Please leave me a comment.

(Edit: saw a good piece today by John Chambers of Cisco about the power of connection. http://www.wired.com/insights/2012/12/the-internet-of-everything/)

December 23rd, 2011

Review: Beautiful Data by Toby Segaran and Jeff Hammerbacher

Segaran (author of the highly-recommended Collective Intelligence) and Hammerbacher have collected together a diverse set of essays on data collection, visualization, processing, and analysis. What interested me most was the wide variety of application areas in which data is the “secret sauce”. The essays range from broadly philosophical to deep in the technical details: so you’re likely to find something at your level of interest (though that also means that much of the book may not hit your level).

Jeff Hammerbacher’s chapter on Information Platforms and the Rise of the Data Scientist is a good example. It discusses Facebook’s history of scaling its data storage and analysis capabilities, starting with custom scripting based on SQL, moving to data warehousing and then beyond to Hadoop and related tools. “More data, simple models” is the processing style that characterizes many such Big Data enterprises today.

Other valuable chapters for me:

  • Data Finds Data (Jeff Jonas and Lisa Sokol)
  • Natural Language Corpus Data (Peter Norvig)
  • Connecting Data (Toby Segaran)

While you’re not likely to find a solution here to specific technical problems, there’s a good chance you’ll find something either to broaden your horizons or give you some new ideas. Definitely recommended.

(Disclosure: I received a free copy of this book through O’Reilly’s Blogger Review program.)

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