Links:

• Deeper understanding with Metaweb

• Metaweb joins Google

• Google Buys Metaweb to Boost Semantic Search

Let’s take a look at TIBCO’s entry into the world of enterprise social software – tibbr. It is a microblogging platform: it allows people to submit/receive short messages. But it is subject-centric in its core. tibbr allows users to define subjects with name and description. Users can submit (“tib”) messages into “subjects” and subscribe to subjects that they are interested in. User experience is amazing!

It is very close to ideas described in my previous posts related to Subject-centric microblogging

• iPhone OS 3.0 – ready for Subject-centric computing, Mar 21, 2009

• Subject-centric microblogging and Ontopedia’s knowledge map, Feb 21, 2009

• INSTANT MESSAGING, SUBJECT CENTRIC GROUP CHATS, TOPIC MAPS AND… GOODBYE EMAIL … ALMOST, Dec 2, 2004

• JABBER, PUBLISH & SUBSCRIBE ARCITECTURE AND … TOPIC MAPS, Oct 12, 2004

Another interesting product is TIBCO BusinessEvents. It is a complex events processing platform. Under the hood there is a powerful domain modeling infrastructure that allows to define “concepts”, “events” and “business rules”. “Concepts” help to define what I like to call Enterprise Knowledge Map. “Events” define what can happen outside and inside of an enterprise. “Business rules” allow to connect events and concepts. As a result, we can create dynamic enterprise models which facilitate decision making in real-time.

TIBCO’s founder and CEO Vivek Ranadivé in his presentation “Two-second Advantage” mentioned future “triple store”. But it is not just static triple store that we typically mean in relation to Linked Data. It is a triple store that is integrated and updated by stream of events with ability to reason about concepts and events in time. TIBCO BusinessEvents 4.0 is a great introduction to these ideas.

Within traditional logic from inconsistency anything can be inferred. If we have a contradiction about a proposition P then we can infer any proposition Q

    P, ⌉P ├ Q    

This is not very helpful if we want to talk about inconsistency tolerant reasoning…

In Carl Hewitt’s Direct Logic [1] situation is different. Formula

    P, ⌉P

describes totally normal situation, but the meaning of this situation is different from traditional logic.

“Direct Logic supports direct inference ( ├τ ) for an inconsistency theory T. ├τ does not support either general proof by contradiction or disjunction introduction. However, ├τ does support all other rules of natural deduction…” [1]

In traditional logic if we have

    P ├τ Q, ├τ ⌉Q

then we can infer ⌉P in theory T. In Direct Logic we cannot do this kind of inference.

“Since truth is out the window for inconsistent theories, we have the following reformulation: Inference in a theory T (├τ) carries argument from antecedents to consequents in chains of inference…” [1]

So if we have

    P, ⌉P

it does not mean that P is true and not true at the same time. It means that we have arguments both for and against the proposition P. Direct inference ├τ does not “propagate truth”, it propagates arguments.

Even if we have

    P, ⌉P

in Direct Logic we still can continue doing inferences based on these propositions (of course, we can generate new contradictory propositions).

So if we have, for example,

    P, ⌉P, P ├ Q, ⌉P ├ ⌉Q

we can infer

    Q, ⌉Q

which means that we have arguments both for and against the proposition Q. Because we are talking here not about “truth” but about argumentation it makes total sense.

How is it related to inference in Ontopedia (described here)?

If we have foundational inconsistency tolerant logic we can implement reasoning engines that support various inference heuristics on top of this logic. We can think about Ontopedia inference engine as a specific heuristic layer on top of inconsistency tolerant logic such as Direct Logic.

We do have a concept of “truth” in Ontopedia, but it exists only at the heuristic layer as a convenient metaphor. At the foundation layer inference engine just generates propositions with argumentation. In Ontopedia we try to assign truth value to each proposition, but this attempt can fail if we have contradictory arguments with the same strength. We also use contradiction detection for managing/controlling inference. In Ontopedia we do not want to infer lots of assertions based on contradictory propositions. We suppress this kind of inference. Ontopedia is built to be a tool that helps Ontopedia’s user community to create/evolve/improve Ontopedia’s community knowledge map. Identification and resolution of knowledge conflicts is a fundamental activity.

It is important to mention that we do not make an assumption that one found contradiction refutes entire Ontopedia’s knowledge map. We assume that Ontopedia’s knowledge map can have multiple contradictions at any time. Ontopedia is an open system. We can have new users and we can collect new information about new subjects. Openness naturally introduces new (also helps to resolve some) inconsistencies . But we do not just stay and watch growing number of knowledge conflicts. We embed system mechanisms that allow us to monitor level of Ontopedia’s inconsistency. Our assumption is that Ontopedia’s user community will try to resolve some of existing knowledge conflicts and can improve knowledge quality and support reasonable level of inconsistency.

Ontopedia’s inference engine allows to ignore contradictory arguments and select one of the options as a “decision”. It means that engine does inferences only based on a “decision” and suppresses inferences based on alternative. Probably not all users can agree with decisions recorded in Ontopedia’s knowledge map. We consider possibility of “forking” knowledge map into separate maps with own user communities. Same assertions can be assimilated by some communities and ignored/rejected by others. In general, inconsistency tolerant reasoning allows various communities to exchange and utilize information even when they disagree with it.

I am looking forward to explore more in the areas of inconsistency tolerant logic and inconsistency tolerant reasoning.

References:

• 1. Common sense for concurrency and inconsistency tolerance using Direct Logic, Carl Hewitt, 2010

On the surface it looks like iPad (and iPhone) application-centric model (with thousands various Apps) is in contradiction with application-less model of Subject-centric computing. In reality, applications developed for iPad and iPhone are often can be considered as “functions” which can be combined to provide subject-centric experience.

What is missing? Integration with global knowledge map that can be used from various apps and a mechanism to pass subject context that allows to launch/continue applications in specific subject context.

Apple provides support for embedding geo maps into any application using MapKit framework. Let’s assume for a minute that Apple creates SubjectKit. This (imaginary) framework provides access to information collected in global sources such as Freebase, Wikipedia the same way MapKit provides access to Google Maps. In this case iPad and iPhone applications can leverage information collected in global knowledge map. SubjectKit also can allow applications to record current subject context in some shared storage available for all apps. When a user launches an app, this app can read current subject context and use it to provide subject-centric experience.

Let’s take a look at iTunes, for example. It simplifies “buying experience”, but it is not currently integrated with global knowledge map. We often need to launch a browser and search to get additional info about subjects that we are interested in (movies, actors, directors, tracks, groups, ...). iTunes has some reference data but it is quite minimal in comparison with what we can get in global knowledge map, and this reference data is limited to iTunes app.

With (imaginary) SubjectKit iTunes (and any other app) on iPad can leverage information available in global knowledge map directly without manual search.

What about leveraging subject context between various applications? Let’s say I open my Weather app and check weather in New York City. Weather app can record that one of my currently active subjects has identifier http://en.wikipedia.org/wiki/New_York_City. Let’s assume that as a next step I launch iTunes. iTunes (in my imaginary scenario) can retrieve this subject identifier from shared context storage. If I click on “Movies” tab then iTunes can suggest, for example, “Sleepless in Seattle” movie in the new “Related to your active subjects” section. iTunes can do it because (in my imaginary scenario) it can leverage current subject context, internal iTunes database and global knowledge map.

With sharing subject context between apps comes an issue of protecting user privacy. SubjectKit framework can maintain “white list” of apps which are allowed to save into and restore from current subject context. SubjectKit can block applications that try to access subject context if they are not allowed to see it. SubjectKit also can prevent applications to save active subjects into shared subject context if they are not allowed to do so.

Of course, this approach is not limited to OS X and iPad. It’s just combination of iPad design, powerful multi-touch interface and strength of OS X creates a winning platform for Subject-centric computing.

There are several fundamental principles that we try to follow developing inference capabilities in Ontopedia.

Paraconsistency

We assume that Ontopedia’s knowledge base can have contradictions at any time. We try to develop a system that can do “reasonable” inferences within inconsistent knowledge base.

Non-monotonic, Adaptive Knowledge Base

People change opinions, modify existing and create new theories (formal and informal). People learn new things, they can be convinced and taught (sometimes). There is evolution in general and personal knowledge. We would like to support these “subject-centric” features.

In simple cases, Ontopedia users can change their factual assertions. This can trigger truth maintenance processes and revision of other assertions. Ontopedia also keeps history of assertions – “Who asserted What and When”

Minimization of Inconsistency

We try to create a system that can operate/reason within inconsistent knowledge base. But it is not enough. We embed mechanisms that allow to identify inconsistencies and help to resolve them. Knowledge conflicts are “first class objects” in Ontopedia and are organized based on conflict types. Each knowledge conflict type has conditions that describe how conflicts of this type can be identified. There are also some recommendations for resolving conflicts. In general, Ontopedia’s user community tries to minimize knowledge inconsistency and Ontopedia system “tries” to help users to achieve this goal.

Inference Transparency and Information Provenance

Inference is not an easy process, contradictions are tough, knowledge evolution is challenging. We do not try to create an illusion that these things are easy and that a user can just “click a button” and magically get “all” inferred assertions, we do not try to “virtualize” inference and hide it behind a query language, for example. We think about inference as a process that can be time and resource consuming and can include multiple steps. Ontopedia provides facilities to record various steps of inference process. Inference tracing is an important part of Information Provenance in Ontopedia. We keep track of “Who asserted What and When” and we also keep track of “What was Inferred based on What and Why”.

Multiple Inference Modules and Decision Procedure

RDFS inference rules are useful, RDFS+ adds some new tricks. OWL 1.0 inference looks interesting in many contexts and OWL 2.0 looks even better. What about Common Logic? What about Cyc-like inference?... Ontopedia’s system architecture supports various inference modules. Each module can generate proposals based on the current state of Ontopedia’s knowledge base. These proposals are recorded in the knowledge base but they do not automatically become Ontopedia’s “visible” assertions. Different inference modules can produce controversial proposals, it’s OK. Various proposals are considered by Ontopedia’s decision procedure that calculates “final” assertion that becomes “visible” on Ontopedia’s web site. Decision procedure can be invoked on any assertion at any time. Ontopedia’s knowledge base is not limited by “true” only statements. We utilize multi-valued truth including “unknown.”

Loosely Coupled Inference, Decision Making, and Truth Maintenance

Ontopedia is a system that can “infer a little bit something here”, “find some knowledge conflicts there”, “make some new decisions”, “infer a little bit more”, “review some decisions” etc. All these activities can be performed in any order by various components. All activities are recorded in the “activity log” (it is available as RSS/ATOM feed). Various modules can explore activity log and use it for managing own activities and cooperation between modules. In general, modules can run in parallel in various areas of Ontopedia’s knowledge base. These activities can be directed buy user community through user interface or can be directed by “controller” software components.

I developed this approach and related system architecture in late 80s and used it successfully in various projects for relatively small data sets during last 20 years. What is exciting about 2010? It’s availability of huge data sets on the Web. There is also experience in building Social Web and much better understanding of Collective Intelligence. I could only envision in 1990 that it would be possible to build large stable evolving paraconsistent open knowledge based systems. In 2010, I am pretty sure about this.