Semantic Web Architecture and Applications

Semantic Web Architecture and Applications

                Semantic Web architecture and applications are the next generation in information architecture.

First Generation - Keywords

                Keyword technologies were originally used in IBM’s free text retrieval system in the late 1960’s. These tools are based on a simple scan of a text document to find a key word or root stem of a key word. This approach can find key words in a document, and can list and rank documents containing key words. But, these tools have no ability to extract the meaning from the word or root stem, and no ability to understand the meaning of the sentence.

Advanced Search

                Most keyword systems now include some form of Boolean logic “AND” , “OR” functions to narrow searches. This is often called “advanced search”. But, using Boolean logic to exclude documents from a search is not “advanced” . It is an arbitrary and random means to reduce the size of the source database to reduce the number of documents retrieved. This “advanced search” significantly increases false negatives by missing many relevant source documents.

The most common examples of key word tools are web site “Search” tools and the Microsoft “Find” function (control “f” key) in Microsoft Office applications.

Second Generation - Statistical Forecasting

                Statistical forecasting first finds keywords; and then calculates the frequency and distance of these keywords. Statistical forecasting tools now include many techniques for predictive forecasting, most often using inference theory. The frequency and distribution of words has some general value in understanding content. But, cannot understand the meaning of words or sentences; or provide context. These tools are still limited by keyword constraints; and can only infer simplistic meaning from the frequency and distribution of words.

Third Generation - Natural Language Processing

                Natural language processors focus on the structure of language. These recognize that certain words in each sentence (nouns and verbs) play a different role (subject-verb-object) than others (adjectives, adverbs, articles). This understanding of grammar increases the understanding of key words and their relationships. (“man bites dog” is different from “dog bites man”). But, these tools cannot extract the understanding of the words or their logical relationship beyond their basic grammar. And, these cannot perform any information summary, analysis or integration functions.

Fourth Generation – Semantic Web Architecture and Applications

                Semantic web architecture and applications are a dramatic departure from earlier database and applications generations. Semantic processing includes these earlier statistical and natural langue techniques, and enhances these with semantic processing tools. First, Semantic Web architecture is the automated conversion and storage of unstructured text sources in a semantic web database. Second, Semantic Web applications automatically extract and process the concepts and context in the database in a range of highly flexible tools. 

a. Architecture; not only Application

                First, the Semantic web is a complete database architecture, not only an application program. Semantic web architecture combines a two-step process. First, a Semantic Web database is created from unstructured text documents. And, then Semantic Web applications run on the Semantic Web database; not the original source documents.

b. Structured and Unstructured Data

                Second, Semantic Web architecture and applications handle both structured and unstructured data. Structured data is stored in relational databases with static classification systems, and also in discrete documents. These databases and documents can be processed and converted to Semantic Web databases, and then processed with unstrctured data.

c. Dynamic and Automatic; not Static and Manual

                Third, Semantic Web database architecture is dynamic and automated. Each new document which is analyzed, extracted and stored in the Semantic Web expands the logical relationships in all earlier documents. These expanding logical relationships increase the understanding of content and context in each document, and the entire database. The Semantic Web conversion process is automated. No human action is required for maintaining a taxonomy, meta data tagging or classification. The semantic database is constantly updated and more accurate.

d. From Machine Readable to Machine Understandable

                Fourth, Semantic Web architecture and applications support both human and machine intelligence systems. Humans can use Semantic Web applications on a manual basis, and improve the efficiency of search, summary, analysis and reporting tasks. Machines can also use Semantic Web applications to perform tasks that humans cannot do; because of the cost, speed, accuracy, complexity and scale of the tasks.

e. Synthetic vs Artificial Intelligence:

                Semantic Web technology is NOT “Artificial Intelligence”. AI was a mythical marketing goal to create “thinking” machines. The Semantic Web supports a much more limited and realistic goal. This is “Synthetic Intelligence”. The concepts and relationships stored in the Semantic Web database are “synthesized”, or brought together and integrated, to automatically create a new summary, analysis, report, email, alert; or launch another machine application.  The goal of Synthetic Intelligence information systems is bringing together all information sources and user knowledge, and synthesizing these in global networks.

Future of Information Management: Network Spread Sheets for Ideas

                The future of information management will be based on Semantic Web architecture and applications. The most important issue is which technologies and firms take the immediate leadership to drive the migration, and therefore guide the information architecture of the future.

1) Tidal Wave of Information Shifts Power

2) Migration to XMLand RDF Standards

3) Universal Internet Web Portals

4) Parallel Legacy Database Integration

5) Global and Language Expansion

6) Network Access and Distribution

7) Network Transactions and Capacity