Implementing Semantic Search in the Enterprise

Paul WlodarczykDirector of Consulting Services

Earley & Associates

Amber Swope

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Questions we will answer today

• What is Semantic Search?• How is Enterprise Search different from Internet

Search?• Why Semantic Enterprise Search?• How do you implement enterprise semantic search?

Examine people, process, technology, and content.• How do I prepare my content to enable semantic

search?• What technologies are there and how do they

differ?• What can I search?

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What is Semantic Search?

semantic adj. Of or relating to meaning in language or communications.•Semantic search uses language processing to assess the “meaning” of content (documents or web pages) and the “meaning” of search queries to return more relevant results (better matches in meaning)

Key concepts: – Taxonomy, Named Entity, Ontology, Tag

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Key concept: Taxonomy

taxonomy n. A categorization scheme for content, often hierarchical. Example: the animal kingdom

•Most often, taxonomies show “is a” relationshipsExample:

• A mammal is a vertebrate• A rodent is a mammal• A rabbit is a rodent

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Key concept: Named Entity

named entity n. A person, organization, place, thing, or event identified in a body of text Entities are distinct from terms in that they are unambiguous.

– e.g. “Washington” is a term that is ambiguous to an entity (the first President, the city, the state, the US Government, the monument).

– A tagged named entity is unambiguous

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Example: Named Entities

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Key concept: Ontology

ontology n. A set of relationships between entities. •Often these are in subject-predicate-object [triple] format. •Often ontologies relate entities that exist in multiple taxonomies.

Example: A food chain is a set of relationships (predator/prey) between entities (animals, plants) that exist in different taxonomies (kingdoms). The relationships are triples:

– Rodents eat seeds of grasses. – Fox eats rodents. – Kangaroo rat is a rodent. – Rye is a grass. Etc.

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How does semantic search work?

• Assess meaning of documents – Identify named entities and

relationships (triples) OR– Categorize documents to

taxonomies OR– Score each document with a

“signature” or “graph”• “Tag” documents for meaning

(categories, entities, triples, semantic signatures, graphs, etc.)

• Index the documents• Assess meaning of search

terms• Match documents to search

terms via common meaning

MeaningMeaning

[search term]

MeaningMeaning

MeaningMeaning

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Enterprise Search vs. Web Search

Web Search Enterprise Search

Search corpus

Every public webpage – the whole internet

Public documents in the enterprise, departmental docs, plus local docs (My Documents)

Context Generic : Shopping or seeking news and information

Company-specific: Executing a role in a business process

Taxonomies /

categories

Generic – Open Directory Project, Wikipedia, News, etc.

Domain Specific (customers, organization, products, technologies, processes)

Info Security

Information is public Information is secure with role-based access controls

Search algorithms

• Keyword and Link-based• Links = relevancy• Popularity = relevancy• Professionally tagged

• Keyword & tag-based• No links! • No traffic! • Inconsistent metadata tags!

Perfect result

Most popular content Highest quality content

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Why Semantic Enterprise Search?

• Semantic analysis can provide the context, relevancy, and consistency that is lacking in enterprise content creation and search – Enterprise content lacks the

connectedness that internet search exploits

– “Traffic” is not a clue to relevancy in enterprise search

– Enterprise users do not consistently tag content with metadata

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Another key difference in Enterprise Search: Social Context

In “enterprise search” is that we know a lot more about “who” is searching and “who” has authored “what”We understand the community a lot better in the enterprise

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Roadmap for implementing semantic search

1. Implement Enterprise Content Management2. Implement Enterprise Search3. Layer-in semantic analysis to improve search

relevancy

Semantic search isn’t a replacement to Semantic search isn’t a replacement to ECM and enterprise search. It’s a ECM and enterprise search. It’s a “sweetener.”“sweetener.”

ImplementImplementECMECM

ImplementImplementEnterpriseEnterprise

SearchSearch

ExploitExploitSemanticSemanticSearchSearch

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ECM and Enterprise Search Roll-out

Strategy & Plan

Implement Deploy Maintain

People Use cases and User Experience

Job Redesign, Communities

Training Incentives for participation

Process Content Lifecycle Analysis

Workflow, bus. rules, process redesign

Governance Evergreen process for maintaining IA

Technology

Business & system req’ts, technical architecture

ECM and Search Implementation

Desktop integration (classification, search)

Social tech (ratings, tags, bookmarks)

Content Content Analysis, Information Architecture, Taxonomy dev’t

Content migration

Content classification tools, search tools

Taxonomy maintenance, folksonomy

Strategy & PlanStrategy & Plan ImplementImplement DeployDeploy MaintainMaintain

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Layer-in Semantic Enterprise Search

Strategy & Plan

Implement Deploy Maintain

People Use cases and User Experience

Job Redesign Training Incentives for participation

Process Content Lifecycle Analysis

Workflow, bus. rules, process redesign

Governance Evergreen process for maintaining IA

Technology

Business & system req’ts, technical architecture

ECM and Search Implementation, Semantic search implementation

Desktop integration (classification, search)

Social tech (ratings, tags, bookmarks), machine learning

Content Content Analysis, Information Architecture, Taxonomy dev’t

Content migration, build triple stores, semantic training sets

Content classification tools, search tools

Taxonomy maintenance, folksonomy

Strategy & PlanStrategy & Plan ImplementImplement DeployDeploy MaintainMaintain

Semantic technologies play a role in content classification – from defining taxonomies and ontologies, to tagging documents, to improving search terms and hits – as well as in search and discovery

Semantic technologies play a role in content classification – from defining taxonomies and ontologies, to tagging documents, to improving search terms and hits – as well as in search and discovery

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Classify, Navigate, Search, Retrieve Content within the Enterprise

Content Content AuthorAuthor

Check-in & Classify Document or

Content Object

Retrieve Documentor Content

Object

RetrieveUnformatt

edContent

EndEndUserUser

Retrieve Formatted Content

Retrieve Documen

t

EndEndUserUser

EndEndUserUser

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Strategy and Plan: Key Activities

• Business Objectives: Understand the key business problems that must be solved

• People: Understand actors, roles, and use cases (who creates, who files, who searches, etc.)

• Process: Understand content lifecycle: how you create, maintain, reuse, and publish content

• Technology: Understand existing technology and new requirements for all use cases

• Content: Understand existing content, classification, policies, reuse, multichannel, etc.

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Strategy and Plan: Deliverables

• Business Objectives: Define the ROI in terms of the key metrics and how they will trend

• People: Actors, roles, and Use Cases elaborated into System And Business Requirements

• Process: Desired state Content Lifecycle defined • Technology: Systems Architecture completed

and new technology modules defined, integration points with existing technology defined

• Content: Information Architecture: How content will be structured, classified, managed, reused, and searched

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Strategy and Plan: Semantic Search Considerations: Technology

Semantic technologies need to be considered and evaluated as part of the technical architecture, including:

– Categorizers (for auto-tagging, clustering)

– Entity extraction– Triple stores and inference engine– Tag servers– Desktop integration (expose UX into

authoring and search tools)

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Strategy and Plan: Semantic Search Considerations: Content

• Semantic tools can aid content analysis activities including taxonomy, ontology, and name directory development

• Knowing which semantic approaches will be used for navigation, search, and retrieval (taxonomy, named entity, ontology) will inform the information architecture analysis and content classification

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Preparing Content for Semantic Search

Strategy & PlanStrategy & Plan ImplementImplement DeployDeploy MaintainMaintain

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Analyze existing content

• Know what you have– Number of retrievable units?– Size of each retrievable unit?– Current retrieval method?

• Understand its use– Who retrieves it?– When they need it?– How they find it?– How often need it?

• Determine the relationships between retrievable units

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Key Considerations

• Search Objectives – Who is searching for what? How do they search? How

do they expect to see results? How do they rank quality and relevance?

• Content– Where is it? Federation? What types of documents?

Security issues? Is XML or other special content types involved? Component documents or content reuse?

• User Experience (UX)– What is a balance between user expectations and an

effective UI design? Are you involving users in the design? How can you embed the UX into daily tools (mail, desktop, browser, CMS)?

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Define content structure

• Define authoring units– Size?– File format?

• Define storage units– Size?– Relationships between

units?

• Define retrieval units– Documents– Components– Topics/chunks

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Classify content

• Define terms and thesauri• Develop taxonomies

– How many?– Relationship between them?– Where/how stored?

• Apply taxonomy values to content– When are values applied?– Who is responsible for

applying/reviewing?– What can be automated?

• Develop ontologies (if using triples)

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Define metadata

• Identify what data is needed • Define the values

– How used?– Where/how stored?

• Apply metadata values to content– When are values applied?– Who is responsible for applying/reviewing?– What can be automated?

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Control content

• Identify relationship between Identify relationship between storage, retrieval and display storage, retrieval and display mechanismsmechanisms– Same?Same?– Different?Different?– Relationship between them?Relationship between them?

• Define storage strategyDefine storage strategy– Where is content stored?Where is content stored?– Where is metadata stored?Where is metadata stored?– Where are deliverables stored (if Where are deliverables stored (if

generated)?generated)?– How many repositories?How many repositories?– Who needs access to each one?Who needs access to each one?

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Information Architecture for Semantic Search

• Information Architecture

• Structure content for retrieval

• Apply retrieval support at appropriate level

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What technology does semantic search implementation require?

• Semantic Tagging Technology– “Train” a system to auto-categorize documents; taxonomy server– Named entity extraction; directory server– Analyze against “triples”; triple stores plus inference engines– Augment automatic tags with user tags and refinements

• Semantic Search Technology– Disambiguate search terms to their meaning– Map “meaning” of search term to “meaning” of document– Refine “meaning” of search terms (clustering / similarity: “more like

this”)

• Integration Technology– User experience for check-in, classification and NS&R– Desktop integration with browsers, email, and authoring tools– Integration frameworks to tie semantic services with existing

enterprise search and content management

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What can I search?

• Content in ECM– By using semantic tags in

ECM metadata

• Content on your desktop– By semantically tagging

and indexing

• Content on the web– By searching semantic

metadata (e.g. RDF, linked data URIs)

• Databases– By using XML Data Stores

to make relational data available as a “document” that can be tagged

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Standards

• Resource Description Framework (RDF)– Make statements about

resources in triples format

• W3C Semantic Web Standards (“linked data”)– Use URIs to point to

data in the web– Turn web pages into

databases

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Recap

• Semantic search improves search relevance by matching meaning of search terms to meaning of documents

• Semantic technologies include categorizers, entity extractors, and linguistic analysis of relationships between entities (triplets)

• Semantic technologies are available as plug-ins to enterprise systems, or “baked in” to enterprise systems

• Semantic search requires extra steps along the way in implementing ECM and enterprise search

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