Towards Semantic Business Intelligence Semantic Technology 2010, San Francisco Paul Haley Automata, Inc. [email protected] (412) 716-6420.

Towards SemanticBusiness Intelligence

Semantic Technology 2010, San Francisco

Paul Haley

Automata, Inc.

[email protected]

(412) 716-6420

mailto:[email protected]

Knowledge is power

• rules are code

Copyright © 2010, Automata, Inc. 2SemTech 2010, San Franciso

Business needs more intelligence• Natural logic:

– Only full page color ads may run on the last page of the Times.

• Some business rules to enforce constraints:– If an ad that is not full page is to be run on the last page of the Times

then refuse the run.– If an ad that is not color is to be run on the last page of the Times then

refuse the run.

• Business rules for user interfaces:– If asking for the size of an ad that is to be run on the last page of the

Times then the only choice should be full page.– If asking for the type of an ad that is to be run on the last page of the

Times then full page should not be a choice.

• More general business rules (without if):– Ads run on the last page of the Times must be full page.– Ads run on the last page of the Times must be color.


Knowledge is power

• rules are too context sensitive

• too hard to conceive in context

• too hard to manage across contexts


Two convergent themes

• Semantics, IT and enterprises

• Knowledge management & acquisition


Knowledge “engineering”

• Metaphors – accessibility; sizzle

• Expressiveness – functionality, adequacy

• Utility – suitability, flexibility, reusability


Business “intelligence”

• business rules vs. logic

• behavior vs. inference

• action vs. truth

• PRR vs. RIF

• SBVR?


Business Processes and Rules

• highly conditional behavior

• many events & processes

• little semantics or inference

• little or no problem solving


Tactical and strategic

• Narrow perspectives– business rule or decision management– business process management– event processing

• Broader perspectives– business intelligence– performance management


Semantics chasm

• lack of semantics or knowledge– in BRMS / BPMS / CEP– in business intelligence / performance management

• what is driving the enterprise towards…?– W3C OWL, RIF– OMG SBVR, BMM

• Semantics of business: vocabulary & rules• Business motivation model

• if this is knowledge, how will it be managed?


BRMS / BPMS / CEP don’t understand

• Underwriting must precede approval.

• Marital status is the state of people with respect to their participation in marriage.

• A plane flies from when it takes off to when it lands.

• A plane taxis between landing and taking off except when it is parked.

• Call a customer who has not responded to a notice within the applicable period.

• If a validated application has been submitted forward it to originations (or underwriting).


Processes and events

• No [adequate] ontology exists

• Very commonly used in language

• Adjectives commonly reference state

• Tense and perfection reference process

• Imperative action and past or progressive occurrence is common

• Processes and events are not just “things”

Copyright © 2010, Automata, Inc.SemTech 2010, San Franciso 12

Events are primitive

• Events occur. – They happen.– They are temporal.– Processes are a kind of event.– Actions are processes.

• It’s all about the verbs.– Tense is context for BPM & CEP – De-verbal nouns are not just “objects”!

• A request is an action, process, and event.– see the blog for more details


Crossing the chasm

• Rules as knowledge

• Semantics of action

• Processes as knowledge

• Semantics of events (and time)

PRR.RIF.SBVR.BRMS.BPMS.CEPconvergence?!


Reach the enterprise majority

• Processes as means to objectives

• Pursuing goals and achieving objectives

• Semantics of business motivation

• Semantics of business performance

• Leverage inference– Active business intelligence– Active performance optimization


Concrete steps

• Increase focus on knowledge– unify BRMS, BPMS, CEP

• Mature OWL, RIF, SBVR, et al– events (including processes) and time– quantities, including time and money, …

• Increasingly dictate and govern decisions and behavior using knowledge– increasing knowledge-driven process engines– increasing automated decision management

• Knowledge-driven BI & performance mgmt


Semantic Business Intelligence

• Process semantics includes causality– good or bad events, activity, and outcomes

• Process semantics will include motivation– goals, purposes, & expectations

• Static BI vs. automated discovery– systems will learn to predict performance– w/o understanding learning is too hard

• Semantics enables learning & prediction


Two convergent themes

• Semantics, IT and enterprises

• Knowledge management & acquisition


Knowledge “engineering”

• Metaphors – accessibility; sizzle

• Expressiveness – functionality, adequacy

• Utility – suitability, flexibility, reusability


Utility vs. Usability

Rule Metaphor Analysis

0

2

4

6

8

10

0 2 4 6 8 10

Intuitive / Ease of Use

Po

we

r / E

xpre

ss

ive

ne

ss

Natural Logical Rules

If / Then Rules

Tabular Rules

Decision Trees

Lookup Tables

Copyright © 2010, Automata, Inc.

Graphical metaphors less usable than tabular rules

Logics may be slightly more or less expressive than rules but are not more accessible than tabular rules

SemTech 2010, San Franciso 20

Lookup tables

• Determine one of the arguments to a predicate (typically binary or ternary)

• Min/max cardinality may be 0 or >1


Other tabular metaphors

• Primitive

• Spreadsheet rules– one rule per row or column– multiple antecedents or consequents– variable scoping (givens) per sheet– binary predicate limitation

• Tabular organization of statements


Primitive metaphor

• Tabular metaphor– rules correspond to 3 column tables

• leverage properties’ maximum arity of 2• predicate, domain, range (prefix or infix feasible)• support for literals, variables (amounts feasible)• negated, disjunctive, and inequality restrictions feasible• a 4th [tree] column supporting more than conjunction is feasible

– first or last row(s) are consequent(s)– other rows are antecedents

• Textual metaphor– wordings of properties as 5 column tables


text before optionaldomain or range

text between optionalrange or domain

text after


Spreadsheet rules


Statements within tables


Textual metaphors

• Formal syntax– technical rule languages– FOL, SILK, etc.– SBVR’s “formalized” English

• Structured textual metaphors– Top-down structure editing– Left to right authoring or parsing

• Grammars ranging from pseudo-code to “business language” to “natural language”


Textual versus linguistic

• SBVR defines wordings as structured text, not linguistically– vocabulary and wordings are (generally) “just”

text (e.g., without parts of speech)– SBVR does not manage its vocabulary in

dictionary with parts of speech, conjugations, or plurals nor with numbered definitions (i.e., word senses) corresponding to wordings

• SBVR does provide for ontology


Top-Down Structure Editing

• IBM w/in Word• Clauses

correspond to relations with noun phrases for roles

• Structured editing and pick lists avoid ambiguity

• Simple and effective


Existing structured “language”• No grammar or vocabulary required

– Very little natural language processing involved– Easily internationalized using text

• Generally limited to “if” formulations– Reference is by choice not inference– No ambiguity, the parse is implicit (or explicit)

• Expressiveness limited by small equivalent grammar– limited use of adjectives– relative clauses limited (e.g., “that”/”which”/”who”)– passive voice and possessive or prepositional forms limited– limited (if any) support for plurals (e.g., sets and aggregates)– such limitations result in logic limitations and wordiness


“Left to right” English


Document metaphors

• Microsoft Word– Microsoft acquired SBVR from Unisys– Oracle supports Microsoft Word via Haley

• Wiki metaphor– supporting collaborative KA from Semantic MediaWiki

• Typical functionality and feedback loop:– ambiguous or clearly understood fragments readily

distinguished from draft content– rendering of draft content drives vocabulary acquisition– defining vocabulary or phrasings fleshes out ontology– content becomes increasingly understood as linguistic

ontology is acquired


VocaWiki


Linguistic feedback improves authoring as in Simplified English

Encourages authors to contribute more formal knowledge

Facilitates knowledge acquisition from content thru gardening

Facilitates community / collaborative KA

Simplifies and improves query


Logical interpretation

• Only 6x21” ads can run on the back page of any section in the newspaper.

• For color reservations, pick-ups and multiple appearance ads are not allowed.

• When Vulcan runs a 2x7” on pgs 2-3 they should only be charged for a 2x5.25” at their contract rate.

• Book ads shall receive a lower per column inch rate for a 6x21” than for other sizes.

• Ad schedules using the same material with the same reservation number must always be the same size.


Clarification or Disambiguation

• only full page color ads run on back pages– only ads run on back pages– ads run on back pages must be color– ads run on back pages must be full page

• show plausible implications of interpretations

SemTech 2010, San Franciso 34Copyright © 2010, Automata, Inc.

NLP and Linguistic Ontology


dictatea sentence

analyze lexemes used

in the sentence

hypothesize concepts

referenced by noun phrases

hypothesize relationships referenced by

phrases / clauses

determine plausible

interpretationsof sentence

identify ambiguities or grammatical

or lexical issues

restate or editthe sentence

updaterepository with

formal logicfor sentence

updaterepository of sentences


Towards SemanticBusiness Intelligence

Semantic Technology 2010, San Francisco

Paul Haley

Automata, Inc.

[email protected]

(412) 716-6420

mailto:[email protected]

Towards Semantic Business Intelligence Semantic Technology 2010, San Francisco Paul Haley Automata, Inc. [email protected] (412) 716-6420.

Documents

san francisocopyright

page color ads

bmmsemantics of business

business processes

general business rules

contexttoo hard

plane flies

plane taxis