Introducing Knowledge.Bio

I just prepared the following poster abstract for the upcoming Big Data 2 Knowledge all-hands meeting at NIH.  Please play with the tool it describes and let us know what you think (it is a work in progress!).  Also, if you have a chance, please stop by the poster and say hello!

Knowledge.Bio: an Interactive Tool for Literature-based Discovery 

Personal knowledge graph showing literature-derived connections
 between Sepiapterin Reductase (SPR) and 5-Hydroxytryptophan
(a treatment for patients with deleterious mutations in SPR.

Benjamin M. Good, Ph.D.1; Richard M. Bruskiewich, Ph.D. 2; Kenneth C. Huellas-Bruskiewicz2; Farzin Ahmed2; Andrew I. Su, Ph.D.1

1 The Scripps Research Institute, La Jolla, CA, USA. 2 STAR Informatics / Delphinai Corporation, Port Moody, BC, Canada

PubMed now indexes roughly 25 million articles and is growing by more than a million per year.  The scale of this “Big Knowledge” repository renders traditional, article-based modes of user interaction unsatisfactory, demanding new interfaces for integrating and summarizing widely distributed knowledge.  Natural language processing (NLP) techniques coupled with rich user interfaces can help meet this demand, providing end-users with enhanced views into public knowledge, stimulating their ability to form new hypotheses.

Knowledge.Bio provides a Web interface for exploring the results from text-mining PubMed.  It works with subject, predicate, object assertions (triples) extracted from individual abstracts and with predicted statistical associations between pairs of concepts.  While agnostic to the NLP technology employed, the current implementation is loaded with triples from the SemRep-generated SemmedDB database and putative gene-disease pairs obtained using Leiden University Medical Center’s ‘Implicitome’ technology.  

Users of Knowledge.Bio begin by identifying a concept of interest using text search.  Once a concept is identified, associated triples and concept-pairs are displayed in tables.  These tables have text-based and semantic filters to help refine the list of triples to relations of interest.  The user then selects relations for insertion into a personal knowledge graph implemented using cytoscape.js.  The graph is used as a note-taking or ‘mind-mapping’ structure that can be saved offline and then later reloaded into the application.  Clicking on edges within a graph or on the ‘evidence’ element of a triple displays the abstracts where that relation was detected, thus allowing the user to judge the veracity of the statement and to read the underlying articles.

Knowledge.Bio is a free, open-source application that can provide, deep, personal, concise, shareable views into the “Big Knowledge” scattered across the biomedical literature.  It is available at http://knowledge.bio, with source code at https://bitbucket.org/starinformatics/gbk. 

boundaries

Of networks, classes, hierarchy, groups, and the major transitions in evolution.
(warning, not-very-well-thought-out stream of consciousness to follow)

Like a lot of people, I'm currently reading Clay Shirky's book 'Here Comes Everybody'. One of the dominant themes of the book is the formation of groups of people, in particular groups of people that appear to rapidly self-assemble on the Web and produce some interesting product or behavior. This idea of the auto-genesis of complex systems is taking me back to about 9 years ago (yikes) to a time when I was thinking a lot about evolution. Way back then, I was trying to think up better ways to intentionally evolve teams of independent units (e.g. robot control systems) that had to interoperate with one another to achieve some group purpose (e.g. win robocup). My thinking (and a great paper about chickens) lead me in the direction of multi-level selection theory, but that is another story. Among the other people around me at the time that were also thinking a lot about evolution, was one Pietro Speroni.

Pietro was working on a new Artificial Chemistry (a subject I know next to nothing about) with which he was exploring biologically-inspired mechanisms for automatically generating increasingly complex systems. The key aspect of his work that has been trying to creep up to the surface of my consciousness since I started with this Shirky book was a focus on defining his simulated molecules such that they could link together to auto-generate boundaries akin to cell membranes. This was considered the key simulation behavior of interest because the genesis of each new boundary, e.g. the boundary that defines an organelle in a cell, might be considered another step up the ladder of complexity. With each new boundary, a new kind of thing emerges in the system with which the other things can begin to interact with in new ways and thus complexity can increase. Systems like this are interesting because one such system appears to have generated the most fascinating thing imaginable, us. The fuzzy connection dangling around here is between the evolution of complexity in the abstract as Pietro was studying, and the very real evolution of complex groups within human society that is being made possible by the social Web.

Each new user-generated node in the network, each blog posted, each tag applied, each photo shared - each captured communication - can be seen to either implicitly or explicitly define a new group of people. Such groups could contain the people that used the same tag in Del.icio.us, who tagged the same website, who 'liked' the same photo etc. Groups could also contain 'friends' of people who tagged a particular photo or people who tagged a website that was tagged by other people who tagged another website ad infitum. Within the increasingly large and multifaceted network we are (most often accidentally) creating via our contributions to the Web, human groups of seemingly limitless complexity already exist and this trend seems only to be increasing. Do these groups have any relationship to the groups of molecules defined via physical relationships that Pietro's model was emulating? Is there anything fundamental going on here?

I don't know. Just had to get that out of my head.

Introducing Worio

Worio is a hot-off-the-presses social search engine that just sprouted up out of Vancouver, Canada.  They just opened things up for the public a few days ago and I was lucky enough to be introduced to it by Ben, (yet another one..) one of their core developers last night.  Here is a screenshot of the search page.

What you get on the left (orange) side:

• Normal search engine results (you can use other engine's from their search bar if you like) and the opportunity to tag the results and to give them thumbs up or thumbs down votes

What you get on the right (blue) side:

• "Social search engine" results in the form of Web pages they think you might be interested in based what you and your "friends" have tagged and have given the thumbs up to

What you get on the top right:

• A tag cloud extracted from the search results and from your own tag library which can be used to filter the results of the search

Aside from the search engine aspect, Worio is also a social bookmarking service, complete with bookmarklet and all.  Its a perfect demonstration of why I'm interested in social tagging.  When I search for e.g. "Mark Wilkinson" I usually am not interested in the English zoologist, but rather a Canadian bioinformatician that happens to be my advisor.  By learning about who I am and what I'm interested in, social search can much more effectively help me find what I am looking for.

Good show, Worio !  Now if I could just get them to start using the Entity Describer in their tagging... ;)

Muir on Links

"When one tugs at a single thing in nature, he finds it attached to the rest of the world."

John Muir points out a fundamental truth of the physical world that seems to me to describe what the semantic web should be striving to achieve for the virtual.

Reminds me of what is becoming my mantra..
No more dead ends...

WWW conference

Along with three trusty geek companions (Dr. Mark, Dr. Cartik and Mr. Byron), I depart on Monday for the 16th Int. World Wide Web conference in beautiful Banff, Alberta, Canada. Aside from taking in the fantastic scenery and learning what I can, I'll be presenting a poster describing experiences with volunteer knowledge engineers.

Following the conference, we will take a quick trip to dinosaur valley in Drumheller Alberta Should be lots of fun! (Then back to ontology evaluation via inductive learning).