A Distributional Approach for Terminological Semantic Search on the Linked Data Web
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The process of searching and understanding existing vocabularies (terminological artifacts) on the Linked Data Web is an intrinsic activity to the consumption and production of Linked Data. Data consumers trying to find and understand the vocabularies behind datasets in order to query them, or data producers searching for existing resources to describe their data, face the challenge of semantically searching existing concepts in vocabularies. Traditional search mechanisms do not address the level of semantic matching necessary to match users’ information needs to vocabulary elements, bringing an additional barrier to the consumption and production of Linked Data on the Web. This work describes a terminological search mechanism which uses a distributional semantic model to provide a best-effort semantic matching solution. The distributional semantic model leverages the semantic information present in large volumes of unstructured text to improve the semantic matching capabilities of the search process. A quantitative evaluation of the quality of the search results shows that the approach provides an effective semantic matching mechanism for terminological search.
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in their own vocabulary and syntax to the data, abstracting data consumers
from the representation of the data. Despite being a central requirement across natural language interfaces and entity search, there is a lack on the conceptual analysis of schema-agnosticism and on the associated semantic differences between queries and databases. This work aims at providing an initial conceptualization for schema-agnostic queries aiming at providing a fine-grained classification which can support the scoping, evaluation and development of semantic matching approaches for schema-agnostic queries.
Talking to your Data: Natural Language Interfaces for a schema-less world (Ke...
The increase in the size, heterogeneity and complexity of contemporary Big Data environments brings major challenges for the consumption of structured and semi–structured data. Addressing these challenges requires a convergence of approaches from different communities including databases, natural language processing, and information retrieval. Research on Natural Language Interfaces (NLI) and Question Answering systems has played a prominent role in stimulating a multidisciplinary approach to the problem that has moved the field from a futuristic vision to a concrete industry-level technological trend.
In this talk we distill the key principles of state-of-the-art approaches for data consumption using NLI. Particular attention is paid to the maturity and effectiveness of each approach together with discussion on future trends and active research questions.
Question Answering over Linked Data (Reasoning Web Summer School)
Question answering over Linked Data (Reasoning Web Summer School)
Big Data
Linked Data
Semantic Web
Cognitive Computing
More from Andre Freitas (20)
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs ...
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs ...
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Perspectives for Contemporary Question Answering Systems
Semantic Relation Classification: Task Formalisation and Refinement
Semantic Relation Classification: Task Formalisation and Refinement
Categorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary Definitions
Different Semantic Perspectives for Question Answering Systems
Different Semantic Perspectives for Question Answering Systems
WISS QA Do it yourself Question answering over Linked Data
WISS QA Do it yourself Question answering over Linked Data
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
Schema-agnositc queries over large-schema databases: a distributional semanti...
Schema-agnositc queries over large-schema databases: a distributional semanti...
A Semantic Web Platform for Automating the Interpretation of Finite Element ...
A Semantic Web Platform for Automating the Interpretation of Finite Element ...
How Semantic Technologies can help to cure Hearing Loss?
How Semantic Technologies can help to cure Hearing Loss?
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
Talking to your Data: Natural Language Interfaces for a schema-less world (Ke...
Talking to your Data: Natural Language Interfaces for a schema-less world (Ke...
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Event Link:-
https://meetups.mulesoft.com/events/details/mulesoft-mysore-presents-exploring-gemini-ai-and-integration-with-mulesoft/
Agenda
● Java 17 Upgrade Overview
● Why and by when do customers need to upgrade to Java 17?
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● Which MuleSoft products are in scope?
For Upcoming Meetups Join Mysore Meetup Group - https://meetups.mulesoft.com/mysore/
YouTube:- youtube.com/@mulesoftmysore
Mysore WhatsApp group:- https://chat.whatsapp.com/EhqtHtCC75vCAX7gaO842N
Speaker:-
Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Priya Shaw - https://www.linkedin.com/in/priya-shaw
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Shubham Chaurasia - https://www.linkedin.com/in/shubhamchaurasia1/
Giridhar Meka - https://www.linkedin.com/in/giridharmeka
Priya Shaw - https://www.linkedin.com/in/priya-shaw
Shyam Raj Prasad-
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http://sandymillin.wordpress.com/iateflwebinar2024
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A Distributional Approach for Terminological Semantic Search on the Linked Data Web
- 1. Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
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- 14. Term basis ESA concept basis Indexed vocabulary term
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- 23. Query: [airplane] dbpedia-ontology:Aircraft (score = 0.1146) dbpedia-ontology:aircraftAttack (score = 0.1097) dbpedia-ontology:flyingHours (score = 0.1008) dbpedia-ontology:aircraftTransport (score = 0.0876) dbpedia-ontology:aircraftPatrol (score = 0.0738) dbpedia-ontology:aircraftHelicopterTransport (score = 0.0709) dbpedia-ontology:aircraftHelicopter (score = 0.0706) Query: [gun] dbpedia-ontology:Weapon (score = 0.0910) dbpedia-ontology:shipBeam (score = 0.0662) dbpedia-ontology:Ship (score = 0.0562) dbpedia-ontology:militaryCommand (score = 0.0542) dbpedia-ontology:Aircraft (score = 0.0532) dbpedia-ontology:field (score = 0.0523) dbpedia-ontology:aircraftAttack (score = 0.0509) dbpedia-ontology:aircraftTransport (score = 0.0470)
- 24. Query: [king] dbpedia-ontology:Monarch (score = 0.216) dbpedia-ontology:monarch (score = 0.2162) dbpedia-ontology:kingdom (score = 0.0786) dbpedia-ontology:title (score = 0.0539) dbpedia-ontology:headteacher (score = 0.0490) dbpedia-ontology:actingHeadteacher (score = 0.0465) dbpedia-ontology:appointer (score = 0.0459) dbpedia-ontology:executiveHeadteacher (score = 0.04357) Query: [justice] dbpedia-ontology:SupremeCourtOfTheUnitedStatesCase (score = 0.329) dbpedia-ontology:Judge (score = 0.129) dbpedia-ontology:showJudge (score = 0.1277) dbpedia-ontology:chiefEditor (score = 0.0934) dbpedia-ontology:appointer (score = 0.0868) dbpedia-ontology:Criminal (score = 0.0850) dbpedia-ontology:department (score = 0.0691) dbpedia-ontology:retired (score = 0.0578)
- 25. Query: [engine] dbpedia-ontology:engineer (score = 1.0) dbpedia-ontology:engine (score = 1.0) dbpedia-ontology:engineType (score = 0.7174) dbpedia-ontology:gameEngine (score = 0.6452) dbpedia-ontology:principalEngineer (score = 0.5306) dbpedia-ontology:cylinderCount (score = 0.1784) dbpedia-ontology:cylinderBore (score = 0.17584) dbpedia-ontology:AutomobileEngine/cylinderBore (score = 0.17584) dbpedia-ontology:pistonStroke (score = 0.12070) dbpedia-ontology:AutomobileEngine/pistonStroke (score = 0.12070) dbpedia-ontology:AutomobileEngine (score = 0.10195) Query: [car engine] dbpedia-ontology:carNumber (score = 0.38506) dbpedia-ontology:AutomobileEngine (score = 0.15942) dbpedia-ontology:layout (score = 0.12297) dbpedia-ontology:cylinderBore (score = 0.10015) dbpedia-ontology:AutomobileEngine/cylinderBore (score = 0.10015) dbpedia-ontology:cylinderCount (score = 0.0965) dbpedia-ontology:engineer (score = 0.0944) dbpedia-ontology:engine (score = 0.0944) dbpedia-ontology:displacement (score = 0.0921) dbpedia-ontology:AutomobileEngine/displacement (score = 0.0921) dbpedia-ontology:secondDriver (score = 0.0876)
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