A Taxonomy of Semantic Web data Retrieval Techniques

A Taxonomy of Semantic Web data Retrieval Techniques

Anila Sahar Butt, Armin Haller, Lexing Xie

The Australian National University, Australia.

Introduction

The Semantic Web • Provides access to an increasing amount of

structured information in a wide variety of domains• Information overload due to the large amount of

structured data is as much a problem as on the traditional Web

• Ample research has been proposed on Semantic Web data retrieval (SWR) techniques

Introduction

The questions:• Is the field of Semantic Web data retrieval making

progress? • What are the directions that have been taken?• What are some of the promising significant directions

to pursue future research?

Semantic Web Data Retrieval Approaches

o Ontology Retrieval TechniquesSwoogle, BioPortal, AktiveRank, LOV, OntoSearch2 ,OBO ,OntoSelect WATSON ,OntoKhoj

o Linked/RDF data Retrieval TechniquesSindice, Sig.ma, SWSE, SemRank, LTR

o Graph/Structured data Retrieval TechniquesSLQ, OSQ, Lindex, NeMa, BLINK, SSSGD

Overview of Semantic Web retrieval process

Data Acquisition

Data Warehousing

Reasoning

Indexing

Ranking

Query Evaluation

User Interface

Pre-processing Online-

processing

Semantic Web data Retrieval TechniquesRetrieval Aspects

Storage & Search Ranking Evaluation Practical

Aspects

Implementation

Datasets

Efficiency

Effectiveness

Scalability

Ranking Scope

Ranking Factor

Ranking Domain

Data Acquisition

Indexing

Query Match

Scope

Query Model

Results Type

Data Storage

User Interface

Semantic Web data Retrieval TechniquesRetrieval Aspects

Scope

Query Model

Results Type

Type(s) of the data that can be explored with the approach

• Ontologies • Linked/RDF Data• Graph/Structured Data

Way(s) a user can initiate the retrieval process• Keyword search• Structured query search• Faceted browsing• Hyperlink-based navigation

Type(s) of the output as a result of a user’s query• Relation centric • Entity centric• Document centric

Semantic Web data Retrieval TechniquesRetrieval Aspects

Storage & Search Ranking Evaluation Practical

Aspects

Implementation

Datasets

Efficiency

Effectiveness

Scalability

Ranking Scope

Ranking Factor

Ranking Domain

Data Acquisition

Indexing

Query Match

Scope

Query Model

Results Type

Data Storage

User Interface

Semantic Web data Retrieval TechniquesStorage &

Search

Data Acquisition

Indexing

Query Match

Data Storage

Way(s) and rule(s) of the data collection• Manual collection• HTML agnostic crawlers• HTML aware crawlers• Focussed crawlers

Type(s) of storage structures• Relational databases• Native storage• NoSQL databases

Type(s) of index structures• No index • Full text index • Structural index • Graph index• Multi-level indexing

Way(s) to find query matches in the data collection

• Exact Match• Partial Match

Semantic Web data Retrieval TechniquesRetrieval Aspects

Storage & Search Ranking Evaluation Practical

Aspects

Implementation

Datasets

Efficiency

Effectiveness

Scalability

Ranking Scope

Ranking Factor

Ranking Domain

Data Acquisition

Indexing

Query Match

Scope

Query Model

Results Type

Data Storage

User Interface

Semantic Web data Retrieval Techniques

Ranking

Ranking Scope

Ranking Factor

Ranking Domain

Query dependence of a ranking model • Global• Focus

Factor(s) based on which the ranks are calculated• No Ranking• Popularity• Authority• Informativeness• Relatedness• Coverage • Centrality• Learned model• Feedback

Original domain of a ranking model• Semantic web• Graph databases• Document retrieval• Machine learning

Semantic Web data Retrieval TechniquesRetrieval Aspects

Storage & Search Ranking Evaluation Practical

Aspects

Implementation

Datasets

Efficiency

Effectiveness

Scalability

Ranking Scope

Ranking Factor

Ranking Domain

Data Acquisition

Indexing

Query Match

Scope

Query Model

Results Type

Data Storage

User Interface

Semantic Web data Retrieval Techniques

Evaluation

Efficiency

Effectiveness

Scalability

Time taken to retrieve the relevant results • Query execution time• Index construct time• Index update time

Correctness of the retrieved or relevant results• Recall• Precision• F-Measure• MAP• NDCG

Flexibility of the approach• Data size• Data complexity• Query size• Query complexity

Semantic Web data Retrieval TechniquesRetrieval Aspects

Storage & Search Ranking Evaluation Practical

Aspects

Implementation

Datasets

Efficiency

Effectiveness

Scalability

Ranking Scope

Ranking Factor

Ranking Domain

Data Acquisition

Indexing

Query Match

Scope

Query Model

Results Type

Data Storage

User Interface

Semantic Web data Retrieval TechniquesPractical Aspects

Implementation

Datasets

User Interface

Programming language(s) adopted• Java• Python• C#• C

Type of dataset used for implementation or evaluation

• Synthetic• Real

Ways(s) of user interaction• GUI• API

Survey of Existing SWR Techniques

o Ontology Retrieval TechniquesSwoogle, BioPortal, AktiveRank, LOV, OntoSearch2 ,OBO ,OntoSelect WATSON ,OntoKhoj

o Linked/RDF data Retrieval TechniquesSindice, Sig.ma, SWSE, SemRank, LTR

o Graph/Structured data Retrieval TechniquesSLQ, OSQ, Lindex, NeMa, BLINK, SSSGD

Categorization of Prominent Semantic Web Retrieval Techniques 1/2

Techniques

Search Aspect

Storage and Search

Ranking Evaluation

Practical Aspect

Search Scope

Query Model

Result Type

Data Acquisition

Data Storage

Indexing

Query Match

Ranking Scope

Ranking Factor

Ranking Domain

Efficiency

Effectiveness

Scalability

Implementation

Datasets

User Interface

LOV [16] O K,S D M N F E G P D - - - J R G,A

BioPortal [10] O K,F D M - - E G F D - - - J R G,A

OntoSearch2 [14]

O K,F D M N S P G Co - Q - DS - R G

OBO [13] O H D M - N - - - - - - - - R G

OntoSelect [3] O K,F D M,AG

- - - - - D - - - - R G,A

Swoogle [6] O,L K D AG R F P G P D Q - - J R G

WATSON [5] O,L K,S D AG N S,F

E - N D - - - J R G,A

OntoKhoj [12] O K D AG R F - G P D - F - - R G,A

AKTiveRank[1] O K D - - - P F P,Co,C

G,D - P - - - -

Sindice [11] L K D F NoS F P F Co D Q,C,U - DS J R G,A

Categorization of Prominent Semantic Web Retrieval Techniques 2/2

Techniques

Search Aspect

Storage and Search

Ranking Evaluation Practical Aspect

Search Scope

Query Model

Result Type

Data Acquisition

Data Storage

Indexing

Query Match

Ranking Scope

Ranking Factor

Ranking Domain

Efficiency

Effectiveness

Scalability

Implementation

Datasets

User Interface

Sig.ma [15] L K E - NoS

F P F A,Co D Q - - J R G,A

SWSE [8] L K E AG N S P F A,P D Q,C,U

- DS J R G

SemRank [2]

L - R M - - E F - M - - - - S -

LTR [4] L K E M - - - G L M - N - - R -

SLQ [19] G K D M N S,F P F C M Q P,M,N DS J R,S -

OSQ [17] G S D M N G P - - - Q,C P,M,N DS - R,S -

Lindex [20] G S D M N,M

G P - - - Q,C,U

- DS - R,S -

NeMa [9] G S D M N G P - - - Q,C,U

P,R,F DS - R -

BLINK [7] G K D M N M P F C G Q,C - DS - R -

SSSGD[18] G S D M N G P - - - Q - QS,DS

- R,S -

Discussion and Research Directions 1/3 o Dynamic Faceted Browsing

Challenge: Syntactic diversity in describing the same propertyA title of a resource can be described as a name, a title, a label etc.

Solution: Clustering similar types of properties into a single group

o Ontology Retrieval Challenge: Discovering the most related vocabularies for a

query string Solution: Find relevant ontologies that cover most of the

query terms or related concepts to these terms

Discussion and Research Directions 2/3 o Ontology Ranking Models

Challenge: Match of a search term with a more expressive class, property or ontology description

Solution: Ranking models that consider design perspective, level of details, and extension in ontologies.

o Linked data retrieval Effectiveness vs. Efficiency Challenge: Effectiveness focussed techniques vs. Efficiency

focussed techniques Solution: A reasonable trade off between effectiveness and

efficiency

Discussion and Research Directions 3/3 o Ranking of triples for entity retrieval approaches

Challenge: Ranking of a property depends upon the entity it belongs to, and ranking of the object values for multivalued properties depending upon the entity to which the property belongs to

Solution: Ranking of triples for the entity to prioritize relevant attributes and object values of that entity

o An evaluation framework for Semantic Web data retrieval techniques

Challenge: Comparative evaluation of different SWR techniques with regards to their effectiveness, efficiency and scalability

Solution: Conducting comprehensive comparative experimental studies

Conclusion

o An overview of Semantic Web data retrievalo A taxonomy for Semantic Web data retrieval

techniqueso Categorization of Prominent Semantic Web

Retrieval techniqueso Future research directions

Thanks!o For more details

Anila Sahar Butt, Armin Haller, Lexing Xie, “A Taxonomy of Semantic Web Data Retrieval Techniques” In the Proceedings of the 8th International Conference on Knowledge Capture, Palisades, NY, USA, 7th – 10th October 2015

o Contact [email protected]@anu.edu.au

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