Web 3 Expert System

Web 3.0 Reasoning Using a Semantic Network J. Brooke Aker CEO Expert System USA Web 3.0 Conference January 26th

Why Use a Semantic Network? ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Keyword-based technologies Disambiguation Entity extraction Categorization Natural lang. UI Semantic Search Discovery Sentiment

[object Object],[object Object],[object Object],[object Object],What is a Semantic Network? COGITO® English Semantic Network: - 350,000 words - 2.8m relationships

[object Object],What Does a Semantic Network Do? 4 Approaches Definition Example Morphological Analysis understand word forms dog , dogs , and dog-catcher are closely related Grammatical Analysis understand the parts of speech "There are 40 rows in the table" uses rows as a noun, vs. "She rows 5 times a week" uses rows as a verb Logical Analysis understand how words relate to other words "Jeffrey Skilling, represented by Attorney Daniel Petrocelli, is married to Rebecca Carter". Rebecca is married to Jeffrey not Daniel. Semantic Analysis (disambiguation) understand the context of key words "I used beef broth for my soup stock" uses stock in the context of food, vs. "The company keeps lots of stock on hand" uses stock in the context of inventory.

[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Steps to establish meaning Semantic Network Parse Eliminate Ambiguity Order & Priority 1 2 3 Linguistic Query Engine

Semantic Networks ,[object Object],[object Object],[object Object],“ San Jose is an American city” “ San Jose is a geographic part of California” Connections Concepts Terms Abbrev. Phrases Meanings Domains

How do the parts of a Semantic Network fit together?

Technology Stack Semantic Network Semantic Network Semantic Network Semantic Network Semantic Network Linguistic Query Engine Development Studio English Arabic Italian German Other Middle Eastern 1. Morphology 2. Grammatical 4. Disambiguation Develop & Add Custom Rules 3. Logic 80% Precision 90%+ Precision

Superior Performance ,[object Object],<10 -6 sec Software memory footprint (semantic net and engine) 50 MB 350,000 400,000+ 55,000 20 2,800,000 Virtually unlimited Semantic text analysis processing speed (one CPU) Scalability in number of CPUs Typical time of access to a concept in the semantic net Number of concepts in English semantic net Hyponyms and hypernyms Hypernyms and troponyms Average # of attributes for each concept Number of relations in semantic net (English)

Unique Feature #1 ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Keyword / Statistical and Shallow Semantic Tech Fails Here  treats “to fly off the handle” all as separate words not as a concept.

Unique Feature #2 ,[object Object],Keyword / Statistical and Shallow Semantic Tech Fails Here  treats “RIM sued Verizon” as the same thing as “Verizon sued RIM” ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Unique Feature #3 ,[object Object],Keyword / Statistical and Shallow Semantic Tech Fails Here  can’t tell you what portions of a document are related to categorically … e.g. only points to words not sections within a long document as a first cut. object animals plants people concepts places time natural phenomena states quantity groups

Unique Feature #4 ,[object Object],Keyword / Statistical and Shallow Semantic Tech Fails Here  can’t tell you what a simple object in the text is, rather treats words only as tokens with no understanding of their context. Anniversary Address Animals City Company Continent Country Currency Date Device Email Address Event Facility Fax Number Food Holiday Market Index Medical Condition Medical Treatment Month Measure Natural Disaster Natural Feature Operating System Organization Percent Person Phone Number Plants State SSN Time URL Vehicle Year

Expert System Unique Feature #5 ,[object Object],Keyword / Statistical and Shallow Semantic Tech Fails Here  can’t aid in the construction of metadata (information about information) for later logical storage, cache retrieval, maintenance, archiving etc. * aeronautics * breeding * mountaineering * archiving * art * craftwork * auction * astrology * automation * bank * biology * do it yourself * collecting * computer art * graphic * law * building industry * publishing * electronics * electrotechnics * energy * evolution * philosophy * physics * folklore * photography * artistic photography * geology * toys * game * computer science * engineering * education * needlework * work * literature * linguistics * knitting * mathematics * medicine * meteorology * military * fashion * design and engineering * music * jeweler's art * watch making * fishing * post * perfumery * kitchen utensils * public relations * worship * catering * health board * exact science * social science * social service * social services * sled dog * show * sport * statistics * musical instruments * scuba diver * technology * telecommunications * thermo hydraulics * transports * tourism * crochet work * city planning * veterinary science * windsurf * zootechnics * bureaucratic terms * scientific terms * technical terms * typewriting * shorthand * pornography

Thank you Brooke Aker CEO of Expert System US +1 860-614-2411 [email_address] www.expertsystem.net

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