Semantic Map application sounds familiar to you?

Semantic web not just reading cloud tags, and sentences, maps are now reading location and giving a meaning.

Before & After: The current web is a decentralized platform for distributed presentations, while the SemWeb is a decentralized platform for distributed knowledge

The key word is semantic context “meaningfully tracking behavior across the web”. A web in which machines get the meaning of information and use that understanding to transform organize synthesize data intelligently on our behalf.

Definition: Web 3.0 Homehost.com is “the combination of Web 2.0 mass collaboration with structured databases” Web 3.0 enables the transition from “structure upfront” to “structure on the fly”.

Summary: Engine that automatically create structure from unstructured content. Under the hood, homehost.com will harvest content from every social media site worldwide, index, test and mine both content through homehost.com sophisticated “Aggregation Engine”.

Competitive Analysis: Unlike Most major search engines, including Google, yahoo and MSN, HH is not limited to analyzing keywords but full sentences. HH utilizes Semantic Rank Algorithm a technology that is capable of analyzing the concept of a search query, in particular by doing sentence analysis.

How it works: HH automatically learns about the publisher and their interests as content populated through Usage of frequent phrases extracted via HH crawler engine which become tags;

1. Content Feed: in addition to tags added by users. A “Semantic Graph”. Is generated as users add listings – content or publish articles.
2. HH semantic DB picks out & tags certain content with semantic tags – e.g. publishers name.
3. HH app will store information make suggestions about content and share with users – Then creates new semantic and rich data searchable by key words instead of filters –
4. Search filters are narrowed down to Companies, Countries, Industry Terms, Organizations, People, Products and Technologies.
5. Search Results: Meaning and knowledge gets extracted automatically from the semantic database.
6. Content is matched with listings, and tied to product and services provided to searchers.
7. Contextual information to certain types of listings is now indexed, stored and accumulated with searches – Contextual information can include books, movies, music, stocks, and so forth – Stored Contextual information continues recognizing and augmenting links to those listings.

Conclusion: Seek out knowledge distributed throughout the Web, mesh it, and then take action based on it.

General method to decompose information into pieces – In a world with a working Semantic Web, I should not only be able to know without launching a full web expedition, which middle eastern restaurant in a 5 /km radius carries Fresh Falafel! but also:

To aggregate and filter information from various subprime real estate lenders by region and map that against mortgage default rates and lenders’ pools of debt by risk level in a snap. That type of easy data transformation could help avoid a financial crisis of gigantic proportions, which, some would argue, is a handy benefit worth its weight in trillions of dollar

Have I known any better, I would have least saved my 20 year career in mortgage backed securities!

Tags: aggregation, behalf definition, competitive analysis, content feed, definition web, fly summary, google, hh, index test, key word, major search engines, reading location, search filters, search query, semantic context, semantic map, semantic tags, semantic web, sentence analysis, worldwide index