linked open vocabulary ranking and terms discovery

Linked Open Vocabulary Ranking and Terms

Discovery

Ioannis StavrakantonakisPhD candidate

University of Innsbruck - STI Innsbruck

Ioannis Stavrakantonakis, Anna Fensel, Dieter Fensel

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webpage

n keywords

n searches

n*m search results

n result terms

extract

perform

extract

filter

Discovering vocabulary terms

use

vocab-recommender

Outline

• Survey of vocabulary terms discovery.

• State of the art.

• Vocab-recommender: The vocabulary terms discovery assistant.

• Outlook - What is next?

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Survey of vocabulary terms discovery

Survey of vocabulary terms discovery

• 64 participants with valid submissions out of 66.

• Familiar to Computer Science but without any experience in the annotations topic.

• 4 use cases: article (NASA), exhibition (Louvre), hotel (room description), recipe (pizza).

• 1 week time to submit the answers.

• Completion time provided by them.

• “Use solely the LOV Search for the discovery of terms.”

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Number of selected terms

median 50% ofdata points

whisker - minimum

whisker - maximum

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Measured selection time

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• A few outliers in the terms selection time.

• Least skewed measurements for the exhibition and recipe cases.

• Takes in average 1hr.

• Exhibition & recipe cases have the lowest time.

Distribution of participants and schema.org

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The 47% of the proposed terms belong to the schema.org namespace.

Is it due to a specific use case? - No.

General observations• 2 out of 66 participants failed to provide a valid

submission (3%).

• Static parts (media) were considered for inclusion only from 10% of the participants.

• The terms discovery process per se, didn’t include a guideline to follow.

• Participants faced cases that the vocabulary terms description was hard to follow.

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Outline





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State of the art

Inclusion rules

• Be written in RDF and be dereferenceable (URI).

• Be parseable without errors.

• Terms should have an rdfs:label.

• Reuse relevant existing vocabularies.

• Provide metadata about itself.

No guarantee about the effectiveness.

Linked Open Vocabularies

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Ranking

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• Atemezing and Troncy: Information Content (IC) in LOV. a) Terms occurrence in comparison to the maximum term occurrence in the set of vocabularies. b) Centrality of the vocabulary.

• DWRank: a) Hub score. b) Authority score. (no LOD usage)

• TermPicker: Suggests types and properties from vocabularies that other LOD providers have combined together with the one the engineer has used to model the given part (using Schema Level Patterns).

Outline





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Vocab-recommender: The vocabulary terms discovery assistant

MethodologyAim: Assist the exploration of the vocabulary space for a given input webpage W.

Output: A set of vocabulary terms covering the needs of a given W.

Requires: • Perform all the discovery steps in an automatic manner. • Rank result terms to provide the best matches. • Describe the output in a transparent way that helps the

user educate herself about the vocabulary space.

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Methodology

Ranker

Extractor

Searcher

Static recommenderRecommender

webpage Vocabulary terms set generator

Result set T

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Ranking dimensions

• Vocabularies ranking (backlinks, inactive vocabs).

• Vocabulary authors profile.

• Vocabulary terms ranking (LOD usage: LODStats, vocab.cc).

• Vocabulary terms result set for similar webpages.

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Vocabulary authorsHow can we address the low ranking scores of new

vocabularies?

Promote newly created vocabularies by authors that have provided vocabularies in the past with a

desired quality level (reflected in ranking).

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vocabularies

authors

Static recommendations• Refers to images, videos, audio objects.

• Important aspect of the webpage interpretation by the search engines.

• Static mappings to some well defined schema.org terms.

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Implementation

• As a Web service.

• Modular, i.e. any part can be substituted.

• Input: URL or Keywords.

• Output: Set of vocabulary terms described using the vSearch vocabulary.

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Describing search results

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The vSearch vocabulary

Example

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Comparison with survey

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Approachrecall: 71% ( - cooking time, cooking method) precision: 100% (no irrelevant terms)

Outline





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OutlookWhat’s next?

Approach accomplishments• Provide vocabulary terms recommendations for a given

webpage.

• Simplify the discovery process.

• Address the cold start problem for new vocabularies in the search.

• Educate the users around the semantic annotations topic.

• Provide a general vocabulary that can be used to describe search results.

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What is next?

• For the presented approach: Recommendation of actions given the entities that have been proposed by the approach.

• For the Web: Utilise the structured and semantically annotated data to assist our daily lives. Interpret the annotated websites as APIs.

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Thank you!

[email protected] istavrak.com

@istavrak

mailto:[email protected]

http://istavrak.com

References Ioannis Stavrakantonakis, Anna Fensel, and Dieter Fensel: Linked Open Vocabulary ranking and terms discovery. In Proceedings of the SEMANTiCS 2016.

Ioannis Stavrakantonakis, Anna Fensel, and Dieter Fensel: Towards a vocabulary terms discovery assistant. In Proceedings of the SEMANTiCS 2016 Posters & Demos.

G. A. Atemezing and R. Troncy. Information content based ranking metric for Linked Open Vocabularies. In Proceedings of the 10th International Conference on Semantic Systems, 2014.

P.-Y. Vandenbussche, G. A. Atemezing, M. Poveda-Villalón, and B. Vatant. Linked Open Vocabularies (LOV): a gateway to reusable semantic vocabularies on the web. Semantic Web, 2015.

J. Schaible, T. Gottron, and A. Scherp. TermPicker: Enabling the reuse of vocabulary terms by exploiting data from the Linked Open Data cloud. In European Semantic Web Conference (ESWC), 2016.

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linked open vocabulary ranking and terms discovery

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