Meta Data, Ontologies, Vocabularies, Taxonomies

Making the World Computable – An Ambitious Goal

I like it when people think at this level.

Wolfram wants to make the world computable, so that our computers can answer questions like “where is the International Space Station right now.” That requires a level of machine intelligence that knows what the ISS is, that it’s in space, that it is orbiting the Earth, what its speed is, and where in its orbit it is right now.

That’s not static data; that’s a combination of computation with knowledge.

As data explodes we need tools to make sense of the world. It is an exciting field of Natural Language Processing, Data with Context and Deep Analysis. There is so much to learn and so much to do.

Dwell Rank – A different Page Ranking Algorithm

Yesterday, I came across Bipplex, a search engine. Instead ranking pages based on something similar to Google’s PageRank, it uses an algorithm called Dwell Rank. Dwell rank is explained here.

We learned that the biggest commitment a user can make to a web page and content, is the time the user spends on it. We can safely assume that a web page where users only spend a few seconds on is not as important as a page where they spend several minutes. That’s a much stronger commitment than hitting a like button can ever be. And it’s a stronger indication for quality content than links between machines. We call this new way of ranking pages, Dwell Rank.

So I tried it out with a few searches. Here is one, I would like to share:

A few observations:

You can control the Dwell factor with a slider. As you reduce the Dwell Factor, the results change dynamically.
You can control how recently Bipplex has seen the link (currency of information control)
I noticed at least one problem with indexing. It seems to pick up search phrases across sentence boundaries which causes some erroneous results.
The effectiveness of this search engine really depends on user participation (you can download a browser extension that tracks the pages and the amount of time you spend on each page).

Overall, I think, it is a cool idea and has some potential. It has an API (have not tried it yet). I can see several custom versions that may be more useful in communities of interest. I especially like their philosophy of transparency, especially this part:

Search Parameters should be transparent

Every parameter of the search should be available for the people who are using it. That said, it’s a work in progress, especially with the scoring of search terms, but it is possible to control how many days the search should go back and influence the final scoring in terms of search term frequency (how often the search term is mentioned on the page) versus Dwell Rank.

With the slow death of organic search on other search engines, it will be nice to have a really transparent product that can be reused. Here are a few of my wish list items for search (will expand and explain this in a future post).

It will be great to have a customizable Search as a Service.
It will be great if we can tune some of the search parameters (give your own weights to different aspects of search)
For developers it will be great to get some contextual meta data along with search results so that they can build better interfaces.

Ontologies and Knowledge Sharing

On Ontologies and Knowledge Sharing from the (free) book on Artifical Intelligence – Foundations of Computational Agents:

a vocabulary of the categories of the things (both classes and properties) that a knowledge base may want to represent;

an organization of the categories, for example into an inheritance hierarchy using subClassOf or subPropertyOf, or using Aristotelian definitions; and

a set of axioms restricting the meanings of some of the symbols to better reflect their meaning – for example, that some property is transitive, or that the domain and range are restricted, or that there are some restriction on the number of values a property can take for each individual. Sometimes relationships are defined in terms of more primitive relationships but, ultimately, the relationships are grounded out into primitive relationships that are not actually defined

Data Cube Vocabulary

From RDF Data Cube Vocabulary. This document was published by the Government Linked Data Working Group as a Candidate Recommendation.

There are many situations where it would be useful to be able to publish multi-dimensional data, such as statistics, on the web in such a way that it can be linked to related data sets and concepts. The Data Cube vocabulary provides a means to do this using the W3CRDF (Resource Description Framework) standard. The model underpinning the Data Cube vocabulary is compatible with the cube model that underlies SDMX (Statistical Data and Metadata eXchange), an ISO standard for exchanging and sharing statistical data and metadata among organizations. The Data Cube vocabulary is a core foundation which supports extension vocabularies to enable publication of other aspects of statistical data flows or other multi-dimensional data sets.

This proposed standard leverages other well known standars like SKOS, Dublin Core , FOAF, admingeo etc.

A list of statistical datasets using the Data Cube Vocabulary.

The Organization Ontology

From The Organization Ontology

This ontology is designed to enable publication of information on organizations and organizational structures including governmental organizations. It is intended to provide a generic, reusable core ontology that can be extended or specialized for use in particular situations.

Will be interesting to watch the adoption rate. Can certainly help improve the semantics of search for and within organizations.

LinkLog: Ontology Summit 2013 Communique

From OntologySummit2013 Communique

Ontologies are human-intelligible and machine-interpretable representations of some portions and aspects of a domain. Since an ontology contains terms and their definitions, it enables the standardization of a terminology across a community or enterprise; thus, ontologies can be used as a type of glossary. Since ontologies can capture key concepts and their relationships in a machine-interpretable form, they are similar to domain models in system and software engineering. And since ontologies can be populated with or linked to instance data to create knowledge bases, and deployed as parts of information systems for query answering, ontologies resemble databases from an operational perspective.

You need a common vocabulary for a community to share knowledge. Ontologies are meant to provide that vocabulary.

Currently, there is no agreed on methodology for development of ontologies, and there is no consensus on how ontologies should be evaluated. Consequently, evaluation techniques and tools are not widely utilized in the development of ontologies. This can lead to ontologies of poor quality and is an obstacle to the successful deployment of ontologies as a technology.

This document focuses on the evaluation of five aspects of the quality of ontologies: intelligibility, fidelity, craftsmanship, fitness, and deployability.

To determine the quality of an ontology, we need to evaluate the ontology as a domain model for human consumption, the ontology as a domain model for machine consumption, and the ontology as deployed software that is part of a larger system. In this document, we focus on five high-level characteristics:[1]

1. Can humans understand the ontology correctly? (Intelligibility)

2. Does the ontology accurately represent its domain? (Fidelity)

3. Is the ontology well-built and are design decisions followed consistently? (Craftsmanship)

4. Does the representation of the domain fit the requirements for its intended use? (Fitness)

5. Does the deployed ontology meet the requirements of the information system of which it is part? (Deployability)

This document also contains the Ontology Life Cycle Model and is certainly worth a read.

So why is this document important?

Ontologies play different roles in information systems, natural language based analytics and knowledge discovery.

Having a consistent view of Ontologies and the process of building one can immensely impact the shared knowledge in any organization. They improve communication, ability to contribute to the shared knowledge, better searching. They provide great tools for efficiently organize information and provide better context for automated agents.

Links

Ontology Bundle

This is something I wanted to do for a while. Here is the first attempt – an Ontology Bundle. It turns out that I did not blog as much about Ontology as I thought. But here are the Ontology blog posts in the chronological order.

What is an Ontology?

Satine Project

Emotion Markup Language

Ontology – Papers and Lists of Ontologies

An Ontology for Innovation

Ontology Summit 2013 Communique

I will continue to do this with other topics. If you are interested in anything specific, please let me know.

An Ontology for Innovation

An Innovation Improves:

The Quality of a “thing”
The Efficiency of a “thing”
The Value of a “thing”
The Effectiveness of a “thing”

An Innovation has:

A benefit
Fulfills a need
Solves a problem

And to this may I add that an Innovation is

The implementation of one or more ideas
May have a business benefit
May have a social benefit
May have the effect to disrupt an existing process or product or service
May render some jobs homeless and give birth to new jobs
May require new skills

Someone created an ontology as a starting point so that we can discuss about the “thing” (which currently seems points to some resource in dbpedia).

And here is a copy of the overview

Here is a link to the Ontology of Innovation (a bit geeky).

Ontology – Papers and Lists of Ontologies

They are published in the areas that include aviation, cancer, chemistry, computer science, food processing, food safety, genealogy, general medicine, geography, healthcare, manufacturing, mathematics, transportation, and weather. There are, in fact, hundreds of published ontologies.

via technorati.com

This directory from Google on Ontologies are a good starting point.

Here are a couple of papers worth reading:

What is an Ontology by Tom Gruber

Guided Tour of Ontology by John F.Sowa

Organizational Intelligence? (Updated Oct 2013)

Every organization needs to keep track of information about a set of entities. For a business, these may be customers, partners or competitors. It may also be items related to the marketplace, industry sector(s) and related industries. Here is a simple mind map of a few things I can think about. This is just a starting point. (if you need a copy of this mindmap in pdf format you can get it here).

Do you have a strategy to gather information and use it? How do you do it today?

Update 5th Aug 2013

I found a formal Ontology Specification for Organization and Registered Organization (attempts at standardizing organization meta data). Here is a picture from the Organization Ontology.

Updated (Oct 2013)

A schema for an Organization is available here. If you are an Ontologist, modeler or software developer, you may also be interested in a more generic discussion of the data model.