We introduced Subject-centric RSS feeds some time ago on Ontopedia PSI server . With RSS feeds in place, we can subscribe and monitor information about subjects that we are interested in using RSS (including mobile) aggregators. As Ontopedia user, I can submit an assertion, for example, that I am thinking about Blogging Vocabulary . Everyone with RSS subscription to Blogging Vocabulary or to my PSI will be notified about this new assertion.

But, of course, existing RSS aggregators and pull model do not allow to realize full potential of Subject-centric micro-blogging. Services like iPhone Push Notification Service are game changers. I wrote this blog post many years ago about Subject-centric real-time messaging. Now is the right time to implement it. And with new Apple iPhone SDK it should be fun.

Every assertion created in Ontopedia’s knowledge map is automatically reified as a ‘subject’. Starting from the moment of ‘creation’, assertion-based PSIs have a regular ‘life cycle’. Users can change PSI default name, description. It is also possible to deprecate PSIs and introduce new PSIs for the same subject. Of course, users can make assertions about other assertions. This feature is quite helpful for modeling changes in time (combined with time interval scoping), for example.

Speaking about modeling resources on the web, we continue to support URI-based properties/occurrences, but main modeling practice moving forward is based on creation of explicit subjects for web resources and using associations for connecting resources and other subjects. Ontopedia’s generic user interface will be optimized in next releases to support dual nature of web resources (as “subjects” and “links”).

Next feature is related to improving ‘findability’. I think that in many cases we are looking for ‘facts’, not documents, so we try to do the first step in providing direct access to ‘facts’ collected in Ontopedia’s knowledge map. We use basic faceted search/navigation with three main facets: ‘Concepts’, ‘Web Resources’, and ‘Assertions’. For example, if we type ‘apple’ in Ontopedia’s search box, we can find some information items in all three tabs on the front search page. The most interesting tab is probably ‘Assertions’. This tab provides direct access to facts which include reference to ‘apple’. Future versions of ‘Assertions’ tab will include additional facets which will allow to ‘slice and dice’ assertions.

With this new feature, our goal is to demonstrate that Subject-centric computing can change ‘search paradigm’ by providing direct, reliable access to ‘facts’. Of course, it will take lots of efforts to make this approach scalable. But recent enhancements in commercial and open source faceted search engines, achievements in creating “knowledge maps”/”smart indices ” make me believe that we are not that far from ability to directly find ‘facts’ that we are interested in.

“Master data” (PSIs for people, places, companies, products etc.) is a conceptual frame/”endoskeleton” of Ontopedia’s knowledge map. For example, http://psi.ontopedia.net/Apple_Inc is a core, “master” entity.

Assertions Apple Inc is a Company , Apple’s product line includes Mac Mini, iPhone, ... are also part of this core knowledge map.

But Ontopedia’s knowledge map is not limited by this relatively static information. Ontopedia’s knowledge map also has PSIs for events, such as http://psi.ontopedia.net/Apple_reports_financial_results_Q4_2008 and http://psi.ontopedia.net/Apple_Event_October_14th_2008

“Master Data” combined with “Events” create amazingly powerful conceptual framework for mapping of our knowledge.

Ontopedia’s knowledge map has explicit concept of time and has focus on “current moment on Earth at human size level of (real) world” with recording of history and results of forecasting. History does not disappear in the knowledge map. For example, Ontopedia can “remember” that Apple Inc was called “Apple Computer Inc” at some point and that eMac was in Apple’s product line. History is available for referencing and continues to play an essential role in organizing information.

Explicit modeling of time helped us to introduce even more intriguing features such as Subject-centric micro-blogging. We are experimenting with “dynamic” associations and properties such as Currently Reading [Person, Book], Currently Located At [Person, City], “Currently Thinking About [Person, Subject]”, “My favorite link of the day” etc.

To support this “dynamic” perspective on Ontopedia’s knowledge map, we recently added subject-centric RSS feeds. Each subject page in Ontopedia’s knowledge map has own RSS feed which provides quick access to all assertions about specific subject. Each assertion has associated time stamps which allow to track changes in the knowledge map and report them in RSS feeds.

In addition to traditional “source-centric” RSS feeds in my RSS aggregator, I have now folders like People, Companies, etc. with subject-centric RSS feeds from Ontopedia’s knowledge map. These feeds are available on my laptop, but I also have a synchronized RSS aggregator on my mobile phone. Mobile RSS aggregator and mobile browser allow me to work with Ontopedia’s knowledge map when I need it. It makes me feel like Subject-centric computing is (almost) here :)

Carl Hewitt’s work has been an inspiration to me for more than 20 years. Knowledge inconsistency is a fundamental reality of our life. When we build computer systems, we can ignore it, we can try to create artificial boundaries, artificial worlds with “guaranteed” knowledge consistency. Alternative approach is to accept from the beginning that we have to deal with inconsistency and create systems that can represent inconsistent knowledge, reason within inconsistent knowledge bases and utilize mechanisms which help to keep inconsistency “under control”.

I made the choice many years ago in favor of this alternative approach and used it in building many computer systems over the years. Our recent project – Ontopedia PSI server is not an exception. Ontopedia PSI server allows to represent opinions from various sources, including contradictory opinions. Ontopedia’s reasoning engine is justification based (as everything in Ontopedia – work in progress :) which means that decision about each assertion is based on comparison between various opinions and their justifications. Reasoning inside of Ontopedia PSI server is paraconsistent. Inference engine can find contradictory assertions in some areas of Ontopedia’s knowledge base. Local contradictions do not prevent reasoning engine from inferring reasonable assertions in other areas of knowledge base and there is no ‘explosion of assertions’.

Reasoning in Ontopedia PSI server is also ‘adaptive’. We anticipate that when various sources ‘see’ results of comparison between various opinions and ‘see’ consequences of their statements in several ‘steps ahead’, then sources can change their original opinions.

Ontopedia PSI server actually ‘likes’ contradictions. Contradictions are starting points of identifying errors, negotiations, improving knowledge models and as a result – knowledge evolution.

Resources:

• Interdependent Message-Passing ORGs, interview on ITConversations

Topic Maps can play the role of “knowledge middleware” that helps to integrate various components of “smart search puzzle”. A topic map-based index allows to represent and connect subjects and resources. Explicit representation of relatively small number of relationships (“facts”,”assertions”) between resources and subjects can dramatically change the world of smart search.

Topic Maps based-knowledge middleware is a disruptive technology because it replaces proprietary knowledge organization schemas and modules and it allows multiple players to build various solutions that help to create or use smart index.

Topic Maps-based Ontopedia PSI server, for example, can represent assertions that are manually created by users or generated by some algorithms. We do not have our own text analysis infrastructure, but I hope that in the future we can leverage some services on the web (such as OpenCalais) which can perform text analysis on “as needed” basis. The core ability of Ontopedia PSI server is maintaining explicit representations of subjects that are important for people and ability to maintain assertions about these subjects.

The new version of Ontopedia PSI server can play a role of an aggregator that can extract assertions from existing topic maps/fragements hosted on other websites. Assertions from multiple sources are aggregated into one assertion set/information map/semantic index. Ontopedia PSI server keeps track of information provenance and supports multiple truth values. The server, for example, can handle a situation when one source on the web asserts that Person X did a Presentation P and someone else makes the opposite assertion.

I think that natural language processing can play a huge role in improving search. Ideal text analysis tool should allow to provide ‘clues’ about subjects in a text. I am looking for equivalent of some kind of ‘binding’ that is used in programming quite often these days. I would love to have the ability to provide list of main subjects in a form of PSIs to text analysis tool (using embedded markup or attached external assertions). If I do so, I expect much more precise results. If I do not have an initial list of subjects I expect some kind of suggestions from text analysis tools that I can check against existing information map.

Ontopedia (as many other Topic Maps-based projects) promotes usage of Public Subject Identifiers (PSIs) for “all thinkable” subjects. For example, there is an identifier for TMRA 2008 conference – http://psi.ontopedia.net/TMRA_2008 . There are identifiers for each presenter and presentation. Basic relationships between various subjects are also “mapped”/explicitly represented. Each basic resource, such as a blog post can have a small assertion set that describes metadata (using Dublin Core metadata vocabulary, for example) and maybe some main assertions. Traditional websites can provide combined assertion sets in XTM or RDF which can be consumed by semantic aggregators such as Ontopedia PSI server. Text analysis is great (when it is good enough). But even simple (semi-)manual “mapping” of subjects, resources and relationships can change the search game.

When we manually try to “map” an existing resource such as a conference website for the first time, it can look as a complicated and time consuming task. Mapping a website for another conference will take much less time. And, of course, in many cases it is possible to reverse traditional website building/assertion extraction paradigm.

It is possible to build nice looking and functional web sites based on “assertion sets”. Topicmaps.com is a great example of this approach. It is driven by a topic map. Humans can enjoy HTML-based representation of this site and aggregators like Ontopedia PSI Server can consume raw XTM-based representation and aggregate it with other assertion sets such as TMRA 2008 conference assertion set.

References

• Interview link on InfoQ