Archive for the ‘Knowledge Map’ Category

Knowledge Map At The Washington Post (Rediscovery of HyperText)

Saturday, August 1st, 2015

How The Washington Post built — and will be building on — its “Knowledge Map” feature by Shan Wang.

From the post:

The Post is looking to create a database of “supplements” — categorized pieces of text and graphics that help give context around complicated news topics — and add it as a contextual layer across lots of different Post stories.

The Washington Post’s Knowledge Map aims to diminish that frustration by embedding context and background directly in a story. (We wrote about it briefly when it debuted earlier this month.) Highlighted links and buttons within the story, allowing readers to click on and then read brief overviews — called “supplements” — on the right hand side of the same page, without having to leave the page (currently the text and supplements are not tethered, so if you scroll away in the main story, there’s no easy way to jump back to the phrase or name you clicked on initially).

Knowledge Map sprouted a few months ago out of a design sprint (based on a five-day brainstorming method outlined by Google Ventures) that included the Post’s New York-based design and development team WPNYC and members of the data science team in the D.C. office, as well as engineers, designers, and other product people. After narrowing down a list of other promising projects, the team presented to the Post newsroom and to its engineering team an idea for providing readers with better summaries and context for the most complicated, long-evolving stories.

That idea of having context built into a story “really resonated” with colleagues, Sampsel said, so her team quickly created a proof-of-concept using an existing Post story, recruiting their first round of testers for the prototype via Craigslist. Because they had no prior data on what sort of key phrases or figures readers might want explained for any given story, the team relied on trial and error to settle on the right level of detail.

Not to take anything away from the Washington Post but doesn’t that scenario sounds a lot like HTML, <a> links with Javascript “hover” content? Perhaps the content is a bit long for hover, perhaps a pop-up window on mouseOver? Hold the context data locally for response time reasons.

Has the potential of hypertext been so muted by advertising, graphics, interactivity and > 1 MB pages that it takes a “design sprint” to bring some of that potential back to the fore?

I’m very glad that:

That idea of having context built into a story “really resonated” with colleagues,

but it isn’t a new idea.

Perhaps the best way to move the Web forward at this point would be to re-read (or read) some of the early web conference proceedings.

Rediscover what the web was like before being Google-driven was an accurate description of the web.

Other suggestions?

Groups: knowledge spreadsheets for symbolic biocomputing [Semantic Objects]

Tuesday, September 17th, 2013

Groups: knowledge spreadsheets for symbolic biocomputing by Michael Travers, Suzanne M. Paley, Jeff Shrager, Timothy A. Holland and Peter D. Karp.


Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. ‘Groups’ is an implementation of KSs within the Pathway Tools system. Groups allows Pathway Tools users to define a group of objects (e.g. groups of genes or metabolites) from a Pathway/Genome Database. Groups can be transformed (e.g. by transforming a metabolite group to the group of pathways in which those metabolites are substrates); combined through set operations; analysed (e.g. through enrichment analysis); and visualized (e.g. by painting onto a metabolic map diagram). Users of the Pathway Tools-based website have made extensive use of Groups, and an informal survey of Groups users suggests that Groups has achieved the goal of allowing biologists themselves to perform some data manipulations that previously would have required the assistance of a programmer.

Database URL:

Not my area so a biologist would have to comment on the substantive aspects of using these particular knowledge spreadsheets.

But there is much in this article that could be applied more broadly.

From the introduction:

A long-standing problem in computing is that of providing non-programmers with intuitive, yet powerful tools for manipulating and analysing sets of entities. For example, a number of bioinformatics database websites provide users with powerful tools for composing database queries, but once a user obtains the query results, they are largely on their own. What if a user wants to store the query results for future reference, or combine them with other query results, or transform the results, or share them with a colleague? Sets of entities of interest arise in other contexts for life scientists, such as the entities that are identified as significantly perturbed in a high-throughput experiment (e.g. a set of differentially occurring metabolites), or a set of genes of interest that emerge from an experimental investigation.

We observe that spreadsheets have become a dominant form of end-user programming and data analysis for scientists. Although traditional spreadsheets provide a compelling interaction model, and are excellent tools for the manipulation of the tables of numbers that are typical of accounting and data analysis problems, they are less easily used with the complex symbolic computations typical of symbolic biocomputing. For example, they cannot perform semantic transformations such as converting a gene list to the list of pathways the genes act in.

We coined the term knowledge spreadsheet (KS) to describe spreadsheets that are characterized by their ability to manipulate semantic objects and relationships instead of just numbers and strings. Both traditional spreadsheets and KSs represent data in tabular structures, but in a KS the contents of a cell will typically be an object from a knowledge base (KB) [such as a MetaCyc (1) frame or a URI entity from an RDF store]. Given that a column in a KS will typically contain objects of the same ontological type, a KS can offer high-level semantically knowledgeable operations on the data. For example, given a group with a column of metabolites, a semantic operation could create a parallel column in which each cell contained the reactions that produced that metabolite. Another difference between our implementation of KSs and traditional spreadsheets is that cells in our KSs can contain multiple values.

Can you think of any domain that would not benefit from better handling of “semantic objects?”

As you read the article closely, any number of ideas or techniques for manipulating “semantic objects” will come to mind.

Interpreting the knowledge map of digital library research (1990–2010)

Tuesday, May 14th, 2013

Interpreting the knowledge map of digital library research (1990–2010) by Son Hoang Nguyen and Gobinda Chowdhury. (Nguyen, S. H. and Chowdhury, G. (2013), Interpreting the knowledge map of digital library research (1990–2010). J. Am. Soc. Inf. Sci., 64: 1235–1258. doi: 10.1002/asi.22830)


A knowledge map of digital library (DL) research shows the semantic organization of DL research topics and also the evolution of the field. The research reported in this article aims to find the core topics and subtopics of DL research in order to build a knowledge map of the DL domain. The methodology is comprised of a four-step research process, and two knowledge organization methods (classification and thesaurus building) were used. A knowledge map covering 21 core topics and 1,015 subtopics of DL research was created and provides a systematic overview of DL research during the last two decades (1990–2010). We argue that the map can work as a knowledge platform to guide, evaluate, and improve the activities of DL research, education, and practices. Moreover, it can be transformed into a DL ontology for various applications. The research methodology can be used to map any human knowledge domain; it is a novel and scientific method for producing comprehensive and systematic knowledge maps based on literary warrant.

This is a an impressive piece of work and likely to be read by librarians, particularly digital librarians.

That restricted readership is unfortunate because anyone building a knowledge (topic) map will benefit from the research methodology detailed in this article.