SemSearch: A Search Engine for the Semantic Web Yuangui Lei, Victoria Uren, Enrico Motta Knowledge...

SemSearch: A Search Engine for the Semantic Web

Yuangui Lei, Victoria Uren, Enrico Motta

Knowledge Media Institute

The Open University

EKAW 2006

Presented by Jungyeon, Yang

Copyright 2008 by CEBT

Outline

Research background

SemSearch overview

Query interface

Search process

Implementation & examples

Conclusions


Research background

Semantic search: extending traditional search with the semantic web technology

Exploiting the explicit meaning of documents (i.e., ontology-based metadata)

Current semantic search tools

Form-based, e.g., SHOE, Magnet

QA-based, e.g., AquaLog, ORAKEL

Keyword-based, e.g., TAP, Squiggle, DOSE


Support for ordinary end users

Form-based tools Forms are intuitive Issues: knowledge overhead; scalability

QA-based tools Easy to use Issue: heavy NLP.

Keyword-based tools Easy to post queries; quick response Issue: typically one keyword only; general knowledge of

the problem domain required


The goal of our search engine

Hide the complexity of semantic search from end users:

Low barrier to access: easy to post queries

– Avoiding the form-based routine

Dealing with relatively complex queries

– Supporting multiple keywords

Precise and self-explanatory results:

– Results satisfy user queries

– Results are easy to understand

Quick response

– Avoiding linguistic processing


SemSearch Architecture

Google-like User Interface Layer

Semantic Query Layer

Formal Query Language Layer (SPARQL, SERQL, etc.)

Semantic Data Layer

End users

Semantic entity indexing en-gineSemantic entity search engine

Formal query construction en-gineQuery engineRanking en-

gine

Google-like query interface

Text Search Layer


The Google-like query interface

Extending the traditional keyword search languages by allow-ing the specification of: The queried subject (the type of expected search results) The combination of keywords

Three operations are used: Operator “:” captures the query subject “and”/”or” specifies the combination of keywords

Query formats: One keyword: finding entities that have relations with the keyword

match Multiple keywords: “subject:keyword1 and/or keyword2 and/or

keyword3”, e.g., “<news: phd students>”, <paper: john and en-rico>

Advantages: More flexible than form-based query interface More powerful than state-of-art keyword-based semantic search

interfaces


The search process

Step1: making sense of the user queries Step2: translating user queries into formal queries Step3: Querying the back-end semantic data repository Step4: Ranking the querying results


Making sense of user queries

Finding out the semantic meaning of keywords

Class, (e.g., the keyword “phd students”)

Relation, (e.g., “author”)

Instance, (e.g., “Enrico”, ”KMi director”)

Method: text search

labels (rdfs:label)

Short literals also used in the case of instances matching

– When searching for “KMi director”, the instances can be picked up.

Two components in the search engine

The semantic entity index engine

The semantic entity search engine


Translating user queries into formal queries

The search engine takes as input the semantic matches of user search terms

The search engine takes outputs an appropriate formal query according to the semantic meanings of keywords

One user query Each keyword

multiple matches SEARCH ENGINE

multiple formal queries.


Simple user queries

There are only two keywords involved: <subject : keyword>

Fixed number of combination types

Subject match Keyword match Example

Class Class <news: phd students>

Property <news: author>

Instance <news: chief scientist>

Instance Property <victoria:author>

Instance <victoria:yuangui>

Property Instance <member: x-media>

Property <member: author>

• The SeRQL query templates are defined


select {Is}, {R}, {Ik} from {Is} rdf:type {Cs}, {Ik} rdf:type {Ck}, {Is} R {Ik} unionselect {Is}, {R}, {Ik} from {Is} rdf:type {Cs}, {Ik} rdf:type {Ck}, {Ik} R {Is}

A template example

Pattern: Subject -> Class Cs; Keyword -> Class Ck Results: <Is,Relation,Ik> associated with exploratory

links. Example: news stories about phd students

<news “KMi success”, mentions-person, Tom-Heath>

A simplified template in Sesame SeRQL:


Complex user queries

< subject: keyword1 and/or keyword2 and/or… >

Instances of the subject which either have relations with all the keywords or have relations with some of the keywords.

Operational problem the number of combination gets big when there are many

keywords involved and there are lots of matches for each keyword.

Rules for combination reduction: Only considering the subject keyword as class entities Choosing the closest matches to the keyword as possible Choosing the most specific class match among the class

matches.


Query construction In SeRQL

Three building blocks– Head block: what needs to be retrieved, i.e., <Is, r, Ikx>

– Body block: how to retrieve the triples

– Condition block: conditions need to be satisfied

Union block : in order to cover bidirectional relations

SELECT DISTINCTlabel(ArtefactTitle), MuseumName

FROM{Artefact} arts:created_by {} arts:first_name {"Rem-

brandt"},{Artefact} arts:exhibited {} dc:title {MuseumName},{Artefact} dc:title {ArtefactTitle}

WHEREisLiteral(ArtefactTitle) ANDlang(ArtefactTitle) = "en" ANDlabel(ArtefactTitle) LIKE "*night*"


Query construction algorithm

No

No

Adding query blocks for class-property relations retrieval

YesAdding query blocks for class-class re-

lations retrieval

Yes

Adding blocks for class-instance rela-tions retrieval

Has keyword match?

Yes

Initializing the query blocks

Composing queries using the blocks

No

Is class?

Is property?

Is instance?Yes

No


Simple query example


Refinement support


Complex query example


Conclusions

A keyword-based semantic search engine has been de-veloped Google-like query interface Supporting relatively complex queries Providing relatively quick response


Opinions

Pros Google-like query interface (intuitive) Supporting relatively complex queries

Cons Limitation of the target data form. (RDF) Ranking Simple semantic matching

Issues Finding out the semantic meaning of keyword Storage modeling Strategy of the semantic match between keyword and se-

mantic entity

Date post:	31-Dec-2015
Category:	Documents
Upload:	donna-wright
View:	212 times
Download:	0 times

SemSearch: A Search Engine for the Semantic Web Yuangui Lei, Victoria Uren, Enrico Motta Knowledge...

Documents