a distributional structured semantic space for querying rdf graph data

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A Distributional Structured Semantic Space for Querying RDF Graph Data

André Freitas, Edward Curry, João G. Oliveira, Seán O’Riain


Outline

Motivation & Problem Space Description of the Proposed Approach Evaluation Conclusions


Motivation & Problem Space

Linked Data: Heterogeneous and distributed data environment.

Traditional Database and Information Retrieval approaches for querying/searching Linked Data provide limited solutions for casual users.

Querying and searching Linked Data remains a fundamental challenge.

?


Query/Search Spectrum

Adapted from Kauffman et al (2009)


Semantic Gap

From which university did the wife of Barack Obama graduate?

Data model independent (DMI) queries

Semantic Matching


Semantic Matching Problem



Semantic Matching Problem


Semantic matching


Semantic Matching Requirements

Expressivity: Supporting expressive natural language queries. Path, conjunctions, disjunctions, aggregations, conditions.

Flexibility & Accuracy: Accurate and flexible semantic matching approach.

Maintainability: Easily transportable across datasets from different domains. Can support Open domain (e.g. Wikipedia) and doman-specific

(e.g. Financial) datasets without customization. Performance:

Suitable for realtime querying (low query execution time). Scalability:

Scalable to a large number of datasets (Organization-scale, Web-scale).


Research Questions

Q1: How to provide a query mechanism which allows users to expressively query linked datasets without a previous understanding of the vocabularies behind the data?

Q2: Which semantic model could support this query mechanism?


Proposed Approach


Proposed Approach (Outline)

Treo: Query approach based on dynamic Linked Data navigation. Performance limitations.

Treo T-Space: Existing IR approaches: traditional Vector Space Models

(VSMs) were not able to: (i) capture the structure of graphs and (ii) support a precise semantic matching behavior.

A VSM supporting these two requirements was formulated: T-Space.

Ranking function based on semantic relatedness.


Proposed Approach (Outline)


First Approach (Treo)

Natural language queries. Ranked query results. Two phase search process combining entity search

with spreading activation search. Use of Wikipedia-based semantic relatedness to

semantically match query terms to dataset terms. Limited query execution performance.

Requirements: Expressivity, Flexibility & Accuracy.


Semantic Relatedness

Computation of a measure of “semantic proximity” between two terms.

Allows a semantic approximate matching between query terms and dataset terms.

Most existing approaches use WordNet-based

solutions for approximate semantic matching (limited solution).

Use of Wikipedia-based semantic relatedness measures (addresses the limitations of WordNet-based measures).


Query Approach Rationale (Treo)



RDF



Wikipedia Link Measure



RDF



Final Query- Data Matching:

Querying Linked Data using Semantic Relatedness: A Vocabulary Independent Approach. In Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems (NLDB) 2011.

Currently limited to path queries


Treo (Irish): Direction, path


Second Approach: Treo T- Space

Motivation: Performance problem of the original approach. Transportability across different domains.

Rationale: Rephrasing of the proposed approach as a vector

space model. Addressing the limitations of the vector space

model (BoW: lack of structure) to represent the structure present in the data.

Keeping the semantic matching behavior.

Requirements: Performance & Scalability, Maintainability.


T- Space

A Vector Space Model for building RDF indexes that preserve the graph structure and which allows a semantic matching/search behavior.


Generalization (Treo T- Space)

Approach: Ranked results. Natural language queries. Two phase search process combining entity search

with spreading activation search. Use of distributional semantics to semantically

match query terms to dataset terms. Semantic manifold (‘structured vector space’)

model: T-Space.


Distributional Semantic Model

Assumption: the context surrounding a given word in a text provides important information about its meaning.

Simplified semantic model. Explicit Semantic Analysis (ESA) is the primary

distributional model used in this work.

A wife is a female partner in a marriage. The term "wife" seems to be a close term to bride, the latter is a female participant in a wedding ceremony, while a wife is a married woman during her marriage. ...


T- Space

instances

classes

relations Distributional semantic model:

Semantic statistical knowledge extracted

from large Web corpora

Embedding the structure of a graph into a semantic vector space


Querying the T- Space


Querying the T- Space

A Multidimensional Semantic Space for Data Model Independent Queries over RDF Data. In Proceedings of the 5th IEEE International Conference on Semantic Computing (ICSC), 2011.

Querying Heterogeneous Datasets on the Linked Data Web: Challenges, Approaches and Trends. IEEE Internet Computing, Special Issue on Internet-Scale Data, 2012.


Ranking/Filtering Results

How to filter non-related results. Analysis of semantic relatedness as a ranking

function. Creation of a methodology for the determination of

a semantic relatedness threshold. Semantic differential analysis.

A Distributional Approach for Terminological Semantic Search on the Linked Data Web. 27th ACM Applied Computing Symposium, Semantic Web and Its Applications Track, 2012.

Requirements: Flexibility & Accuracy.


VSM & Formalization

Based on the Generalized Vector Space Model. Simplification and clarification of T-Space

properties. Tensors are used to support domain specific

meaning. Tensors can be used to connect concepts across

datasets and T-Spaces


VSM & Formalization

1. The distributional space can be transformed to the keyword space.

Requirements: Flexibility & Accuracy


VSM & Formalization

2. The vector field defines an additional structure over the vector space model.

Requirements: Expressivity


Customized Distributional Model

Distributional models customizable for different datasets.


Customized Distributional Model

Transformation from different distributional models

DBPedia Financial Dataset

Wikipedia Financial Corpus

Requirements: Maintainability A Distributional Structured Semantic Space for Querying RDF Graph Data. International Journal of Semantic Computing (IJSC), 2012.


Evaluation (Treo T- Space)


Quality of Results

Q1: How does the approach work for addressing the vocabulary problem?

Q2: How does the approach work for addressing natural language queries? Precision, recall, mean reciprocal rank, % of

answered queries.


Quality of Results

Full DBPedia QuerySet (50 queries)

Avg. Precision Avg. Recall

MRR % of queries answered

0.482 0.491 0.516 58%

QALD DBPedia Training Set. 50 natural language queries. DBpedia 3.6.

Partial DBPedia QuerySet (38 queries)

Avg. Precision Avg. Recall


0.634 0. 645 0.679 76%


Error Distribution

Q4: What is the error associated with each component of the search approach? Error distribution measurements.


Error Distribution

Conclusion: Robustness of the distributional semantic model for open domain queries.

Conclusion: Major need for improvement of the pre/post processing phases.


Evaluating Terminology- level Semantic Matching

Q1: How does the approach work for addressing the vocabulary problem?

Q5: Does the approach improve the semantic matching of the queries?

Quantitative evaluation: P@5, P@10, MRR, % of the

queries answered, comparative evaluation using string matching and WordNet-based query expansion.


Evaluating Terminology- level Semantic Matching

Avg. Precision@5

Avg. Precision@10


0.732 0.646 0.646 92.25%

Approach % of queries answered

ESA 92.25% String matching 45.77%

String matching + WordNet QE 52.48%


Comparative Analysis

Q3: What is the best distributional semantic model for the vocabulary problem? Preliminary comparative analysis between

different distributional semantic models.


Comparative Analysis (Treo vs Treo T- Space)

ESA (Full Query Set)

Avg. Precision Avg. Recall MRR % of queries answered

0.482 0.491 0.516 58%

Wikipedia Link Measure (WLM) vs Explicit Semantic Analysis (ESA)

WLM (Full Query Set)

Avg. Precision Avg. Recall MRR % of queries answered

0.395 0. 451 0.489 56%

Improvement

% Avg. Precision % Avg. Recall % MRR % of queries answered

18% 8.2% 5.2% 3.5%


Comparative Analysis

Conclusion: From the two tested approaches, ESA provides a better semantic model.


Conclusions

The T-Space model provides a principled way to build data model intependent queries over RDF graphs.

The distributional semantic model supports a flexible matching between query terms and dataset terms in a semantic best-effort scenario.

The ESA semantic model provides a better distributional model compared to WLM.

Improvements are needed on the pre/post processing phase of the approach.


Associated Article

André Freitas, Edward Curry, João Gabriel Oliveira, Sean O'Riain, A Distributional Structured Semantic Space for Querying RDF Graph Data. International Journal of Semantic Computing (IJSC),2012.

http://www.worldscinet.com/ijsc/05/0504/S1793351X1100133X.html

http://andrefreitas.org/papers/preprint_distributional_structured_space.pdf

http://www.worldscinet.com/ijsc/05/0504/S1793351X1100133X.html�

http://andrefreitas.org/papers/preprint_distributional_structured_space.pdf�


Related Publications

André Freitas, Edward Curry, João Gabriel Oliveira, Sean O'Riain, Querying Heterogeneous Datasets on the Linked Data Web: Challenges, Approaches and Trends. IEEE Internet Computing, Special Issue on Internet-Scale Data, 2012 (Article).

André Freitas, Edward Curry, João Gabriel Oliveira, Sean O'Riain, A Distributional Structured Semantic Space for Querying

RDF Graph Data. International Journal of Semantic Computing (IJSC), 2012 (Article). André Freitas, Sean O'Riain, Edward Curry, A Distributional Approach for Terminological Semantic Search on the Linked Data

Web. 27th ACM Applied Computing Symposium, Semantic Web and Its Applications Track, 2012 (Conference Full Paper). André Freitas, João Gabriel Oliveira, Edward Curry, Sean O'Riain, A Multidimensional Semantic Space for Data Model

Independent Queries over RDF Data. In Proceedings of the 5th International Conference on Semantic Computing (ICSC), 2011. (Conference Full Paper).

André Freitas, João Gabriel Oliveira, Sean O'Riain, Edward Curry, João Carlos Pereira da Silva, Querying Linked Data using

Semantic Relatedness: A Vocabulary Independent Approach. In Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems (NLDB) 2011. (Conference Full Paper).

André Freitas, João Gabriel Oliveira, Sean O'Riain, Edward Curry, João Carlos Pereira da Silva, Treo: Combining Entity-Search,

Spreading Activation and Semantic Relatedness for Querying Linked Data, In 1st Workshop on Question Answering over Linked Data (QALD-1) Workshop at 8th Extended Semantic Web Conference (ESWC), 2011 (Workshop Full Paper) .

André Freitas, João Gabriel Oliveira, Sean O'Riain, Edward Curry, João Carlos Pereira da Silva, Treo: Best-Effort Natural

Language Queries over Linked Data, In Proceedings of the 16th International Conference on Applications of Natural Language to Information Systems (NLDB), 2011 (Poster in Proceedings).


http://andrefreitas.org

a distributional structured semantic space for querying rdf graph data

Education

query data matching

original approach

semantic model

query mechanism

query results

semantic relatednessmeasures

low query execution

linked datasets