Web 3.0 Reasoning Using a Semantic Network

J. Brooke AkerCEO Expert System USA

Web 3.0 Conference

January 26th

Semantic Networks• Linguistic rules• Sentence analysis• Semantic Network

Shallow text analytics• Statistics• Heuristic rules• Morphological recognition

Keyword-basedtechnologies

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Why Use a Semantic Network?

• The heart of semantic technology;

• Quality of results derived from the complexity and richness of the network.

• Includes all definitions of all words.• Include relationships among all words.

COGITO® EnglishSemantic Network:

- 350,000 words- 2.8m relationships

What is a Semantic Network?

COGITO® : deep analysis

4 Approaches Definition Example

Morphological Analysis understand word formsdog, 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 Analysisunderstand 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.

What Does a Semantic Network Do?

What are the parts of a Semantic Network?

Using human comprehension for machine understanding of text.

Machine understanding of text needs:

A semantic network

A parser to trace each text back to its basic elements

A linguistic engine to query the semantic network

A system to eliminate ambiguity

Steps to establish meaning

SemanticNetwork

ParseEliminateAmbiguity

Order &Priority

1 2 3

Linguistic Query Engine

Semantic Networks

Traditional technologies can only “guess” the meaning using; keywords, shallow linguistics, & statistics

Semantic Networks instead indentify;

Connections

Concepts

Terms

Abbrev.

Phrases Meanings

Domains

“San Jose is anAmerican city”

“San Jose is ageographic part of California”

How do the parts of a Semantic Network fit together?

SemanticNetwork

SemanticNetwork

SemanticNetwork

SemanticNetwork

Technology Stack

SemanticNetwork

LinguisticQueryEngine

DevelopmentStudio

English

Arabic

Italian

German

Other Middle Eastern

1. Morphology

2. Grammatical

4. Disambiguation

Develop & AddCustom Rules

3. Logic

80% Precision

90%+ Precision

60KB / secSemantic text analysis processing speed (one CPU)

<10-6 sec

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)

Software memory footprint (semantic net and engine) 50 MB

350,000

400,000+

55,000

20

2,800,000

Virtually unlimited

Superior Performance

Unique Feature #1

• Expanded Definition Sets - captures all possible ways of expressing a concept, beyond the use of a single word;

• Compound word – like “blackbird” or “cookbook”

• Collocation – like “overhead projector” or “landing field”

• Idiomatic expression – like “to fly off the handle” or “to weight anchor”

• Locutions – group of words that express simple concepts that cannot be expressed by a single word

• Verbal lemmas – such as a verb in the infinitive form, e.g. “to write”, or verbal collocations, e.g. “to sneak away”

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• Expanded Semantic Relations - expanded set (65) of

relations between concepts by looking at their use within the text. Answers questions like “Who did what to whom?”, often called a “triple” or a subject-action-object. WordNet for example contains only 5 relation types.

•Verb / Subject•Verb / Direct Object•Adjective / Class•Syncon / Class•Syncon / Corpus•Syncon / Geography•Fine Grain / Coarse Grain•Supernomen / Subnomen•Omninomen / Parsnomen

Keyword / Statistical and Shallow Semantic Tech Fails Here treats “RIM sued Verizon” as the same thing as “Verizon sued RIM”

Unique Feature #3

• Categories of Attributes – every concept in the semantic network also contains attributes which are organized into a hierarchy of categories. The attributes and categories are assigned to maximize similarities and differences between concepts as an aid in disambiguation.

objectanimals plantspeople concepts places

timenatural phenomena

statesquantity groups

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.

Unique Feature #4

• Deepest Entity Extraction Available – can identify 35+ unique entities in any text – that is roughly 3 times our nearest competitor, among these;

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 AddressAnimals

City Company ContinentCountry Currency

DateDevice

Email Address Event

FacilityFax Number

FoodHoliday

Market Index Medical Condition Medical Treatment

Month

MeasureNatural Disaster Natural Feature Operating System Organization PercentPerson Phone Number PlantsState

SSNTime URLVehicleYear

Expert System Unique Feature #5

• 600 Semantic Classifications – an ability to auto-classify content at a deep level, among these;

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

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Who Uses Semantic Networks?

Thank you

Brooke Aker

CEO of Expert System US

+1 860-614-2411

baker@expertsystem.net

www.expertsystem.net