IEEE International Conference on Semantic Computing (ICSC 2011).
A Multidimensional Semantic Space for Data Model Independent Queries over RDF Data
André Freitas, João Gabriel Oliveira, Edward Curry Seán O’Riain
http://andrefreitas.org/papers/preprint_multidimensional_ieee_icsc_2011.pdf
Abstract: The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data
on the Web today, end-users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively
query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a multidimensional semantic space model which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation
demanded to build the data model independent approach. The final multidimensional semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall =0.491.
A distributional structured semantic space for querying rdf graph dataAndre Freitas
The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a distributional structured semantic space which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The article analyzes the geometric aspects of the proposed space, providing its description as a distributional structured vector space, which is built upon the Generalized Vector Space Model (GVSM). The final semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.
A Semantic Best-Effort Approach for Extracting Structured Discourse Graphs fr...Andre Freitas
Most information extraction approaches available today have either focused on the extraction of simple relations or in scenarios where
data extracted from texts should be normalized into a database schema or ontology. Some relevant information present in natural language texts,
however, can be irregular, highly contextualized, with complex semantic dependency relations, poorly structured, and intrinsically ambiguous.
These characteristics should also be supported by an information extraction approach. To cope with this scenario, this work introduces a seman-
tic best-effort information extraction approach, which targets an information extraction scenario where text information is extracted under a
pay-as-you-go data quality perspective, trading high-accuracy, schema consistency and terminological normalization for domain-independency,
context capture, wider extraction scope and maximization of the text semantics extraction and representation. A semantic information ex-
traction framework (Graphia) is implemented and evaluated over the Wikipedia corpus.
From Linked Data to Semantic ApplicationsAndre Freitas
In this talk we will discuss how to build (today) semantically intelligent systems, i.e. systems with the ability to process and interpret information by its meaning. We will take a multidisciplinary perspective showing how recent advances in other computer science areas such as Information Retrieval and Natural Language Processing can enable, together with Linked Data and Semantic Web resources, the construction of the next generation of information systems. A summary of the core principles and available
resources from these areas will give a concrete understanding on how to jump-start your own semantic system.
A Privacy Preference Manager for the Social Semantic WebOwen Sacco
Current Social Web applications provide users with means to easily publish their personal information on the Web. However, once published, users cannot control how their data can be accessed apart from applying generic preferences (such as “friends” or “family”). In this paper, we describe how we enable finer-grained privacy preferences using the Privacy Preference Ontology (PPO); a light-weight vocabulary for defining privacy settings on the Social Web. In particular, we describe the formal semantic model of PPO and also present MyPrivacyManager, a privacy preference manager that let users (1) create privacy preferences using the aforementioned ontology and (2) restrict access to their data to third-party users based on profile features such as interests, relationships and common attributes.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
A distributional structured semantic space for querying rdf graph dataAndre Freitas
The vision of creating a Linked Data Web brings together the challenge of allowing queries across highly heterogeneous and distributed datasets. In order to query Linked Data on the Web today, end users need to be aware of which datasets potentially contain the data and also which data model describes these datasets. The process of allowing users to expressively query relationships in RDF while abstracting them from the underlying data model represents a fundamental problem for Web-scale Linked Data consumption. This article introduces a distributional structured semantic space which enables data model independent natural language queries over RDF data. The center of the approach relies on the use of a distributional semantic model to address the level of semantic interpretation demanded to build the data model independent approach. The article analyzes the geometric aspects of the proposed space, providing its description as a distributional structured vector space, which is built upon the Generalized Vector Space Model (GVSM). The final semantic space proved to be flexible and precise under real-world query conditions achieving mean reciprocal rank = 0.516, avg. precision = 0.482 and avg. recall = 0.491.
A Semantic Best-Effort Approach for Extracting Structured Discourse Graphs fr...Andre Freitas
Most information extraction approaches available today have either focused on the extraction of simple relations or in scenarios where
data extracted from texts should be normalized into a database schema or ontology. Some relevant information present in natural language texts,
however, can be irregular, highly contextualized, with complex semantic dependency relations, poorly structured, and intrinsically ambiguous.
These characteristics should also be supported by an information extraction approach. To cope with this scenario, this work introduces a seman-
tic best-effort information extraction approach, which targets an information extraction scenario where text information is extracted under a
pay-as-you-go data quality perspective, trading high-accuracy, schema consistency and terminological normalization for domain-independency,
context capture, wider extraction scope and maximization of the text semantics extraction and representation. A semantic information ex-
traction framework (Graphia) is implemented and evaluated over the Wikipedia corpus.
From Linked Data to Semantic ApplicationsAndre Freitas
In this talk we will discuss how to build (today) semantically intelligent systems, i.e. systems with the ability to process and interpret information by its meaning. We will take a multidisciplinary perspective showing how recent advances in other computer science areas such as Information Retrieval and Natural Language Processing can enable, together with Linked Data and Semantic Web resources, the construction of the next generation of information systems. A summary of the core principles and available
resources from these areas will give a concrete understanding on how to jump-start your own semantic system.
A Privacy Preference Manager for the Social Semantic WebOwen Sacco
Current Social Web applications provide users with means to easily publish their personal information on the Web. However, once published, users cannot control how their data can be accessed apart from applying generic preferences (such as “friends” or “family”). In this paper, we describe how we enable finer-grained privacy preferences using the Privacy Preference Ontology (PPO); a light-weight vocabulary for defining privacy settings on the Social Web. In particular, we describe the formal semantic model of PPO and also present MyPrivacyManager, a privacy preference manager that let users (1) create privacy preferences using the aforementioned ontology and (2) restrict access to their data to third-party users based on profile features such as interests, relationships and common attributes.
Reflected Intelligence: Real world AI in Digital TransformationTrey Grainger
The goal of most digital transformations is to create competitive advantage by enhancing customer experience and employee success, so giving these stakeholders the ability to find the right information at their moment of need is paramount. Employees and customers increasingly expect an intuitive, interactive experience where they can simply type or speak their questions or keywords into a search box, their intent will be understood, and the best answers and content are then immediately presented.
Providing this compelling experience, however, requires a deep understanding of your content, your unique business domain, and the collective and personalized needs of each of your users. Modern artificial intelligence (AI) approaches are able to continuously learn from both your content and the ongoing stream of user interactions with your applications, and to automatically reflect back that learned intelligence in order to instantly and scalably deliver contextually-relevant answers to employees and customers.
In this talk, we'll discuss how AI is currently being deployed across the Fortune 1000 to accomplish these goals, both in the digital workplace (helping employees more efficiently get answers and make decisions) and in digital commerce (understanding customer intent and connecting them with the best information and products). We'll separate fact from fiction as we break down the hype around AI and show how it is being practically implemented today to power many real-world digital transformations for the next generation of employees and customers.
Querying Heterogeneous Datasets on the Linked Data WebEdward Curry
The growing number of datasets published on the Web as linked data brings both opportunities for high data availability and challenges inherent to querying data in a semantically heterogeneous and distributed environment. Approaches used for querying siloed databases fail at Web-scale because users don't have an a priori understanding of all the available datasets. This article investigates the main challenges in constructing a query and search solution for linked data and analyzes existing approaches and trends.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
dcat: An RDF vocabulary for interoperability of data cataloguesRichard Cyganiak
Governments produce large amounts of valuable data, such as spreadsheets and maps, as part of daily operations and decision-making. This data can be useful to many citizens and organizations beyond the government, and it is ultimately the citizen who has paid for producing it. More and more governments make this data publicly available through data catalogs, such as data.gov, data.gov.uk, or statcentral.ie. This slide set introduces dcat, a unified format for publishing the metadata of such catalogs, that supports the querying, federation, consumption and archival of these valuable data assets.
One-stop shop for software development informationAftab Iqbal
Talks about the issues which developers face while interacting with the many software repositories and the questions they usually have in their mind while search. Introduce the linked data approach to integrate the information from different software repositories.
The slides discuss the research agenda for search of the semantic web and current available search tools. The slides were prepared for an audience of information
Discovering Semantic Equivalence of People behind Online Profiles (RED 2012 -...kcortis
This paper was presented at the Fifth International Workshop on Resource Discovery (RED 2012: http://www.labf.usb.ve/RED2012/) at ESWC 2012 (http://2012.eswc-conferences.org/) Conference in Heraklion, Crete, Greece on 27 May 2012.
The full paper can be found at: http://ceur-ws.org/Vol-862/REDp5.pdf
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Transitioning web application frameworks towards the Semantic Web (master the...Benjamin Heitmann
Presents the results of a survey of 54 Semantic Web applications and shows how they fit into 6 broad application types/patterns. For every pattern the capabilities, requirements and components are presented.
The full version of the master thesis is available at: http://eyaloren.org/pubs/heitmann-thesis.pdf
The survey itself is available at http://activerdf.org/survey
Querying Heterogeneous Datasets on the Linked Data WebEdward Curry
The growing number of datasets published on the Web as linked data brings both opportunities for high data availability and challenges inherent to querying data in a semantically heterogeneous and distributed environment. Approaches used for querying siloed databases fail at Web-scale because users don't have an a priori understanding of all the available datasets. This article investigates the main challenges in constructing a query and search solution for linked data and analyzes existing approaches and trends.
Coping with Data Variety in the Big Data Era: The Semantic Computing ApproachAndre Freitas
Big Data is based on the vision of providing users and applications with a more complete picture of the reality supported and mediated by data. This vision comes with the inherent price of data variety, i.e. data which is semantically heterogeneous, poorly structured, complex and with data quality issues. Despite the hype on technologies targeting data volume and velocity, solutions for coping with data variety remain fragmented and with limited adoption. In this talk we will focus on emerging data management approaches, supported by semantic technologies, to cope with data variety. We will provide a broad overview of semantic computing approaches and how they can be applied to data management challenges within organizations today. This talk will allow the audience to have a glimpse into the next-generation, Big Data-driven information systems.
Linked data for Enterprise Data IntegrationSören Auer
The Web evolves into a Web of Data. In parallel Intranets of large companies will evolve into Data Intranets based on the Linked Data principles. Linked Data has the potential to complement the SOA paradigm with a light-weight, adaptive data integration approach.
dcat: An RDF vocabulary for interoperability of data cataloguesRichard Cyganiak
Governments produce large amounts of valuable data, such as spreadsheets and maps, as part of daily operations and decision-making. This data can be useful to many citizens and organizations beyond the government, and it is ultimately the citizen who has paid for producing it. More and more governments make this data publicly available through data catalogs, such as data.gov, data.gov.uk, or statcentral.ie. This slide set introduces dcat, a unified format for publishing the metadata of such catalogs, that supports the querying, federation, consumption and archival of these valuable data assets.
One-stop shop for software development informationAftab Iqbal
Talks about the issues which developers face while interacting with the many software repositories and the questions they usually have in their mind while search. Introduce the linked data approach to integrate the information from different software repositories.
The slides discuss the research agenda for search of the semantic web and current available search tools. The slides were prepared for an audience of information
Discovering Semantic Equivalence of People behind Online Profiles (RED 2012 -...kcortis
This paper was presented at the Fifth International Workshop on Resource Discovery (RED 2012: http://www.labf.usb.ve/RED2012/) at ESWC 2012 (http://2012.eswc-conferences.org/) Conference in Heraklion, Crete, Greece on 27 May 2012.
The full paper can be found at: http://ceur-ws.org/Vol-862/REDp5.pdf
The Relevance of the Apache Solr Semantic Knowledge GraphTrey Grainger
The Semantic Knowledge Graph is an Apache Solr plugin that can be used to discover and rank the relationships between any arbitrary queries or terms within the search index. It is a relevancy swiss army knife, able to discover related terms and concepts, disambiguate different meanings of terms given their context, cleanup noise in datasets, discover previously unknown relationships between entities across documents and fields, rank lists of keywords based upon conceptual cohesion to reduce noise, summarize documents by extracting their most significant terms, generate recommendations and personalized search, and power numerous other applications involving anomaly detection, significance/relationship discovery, and semantic search. This talk will walk you through how to setup and use this plugin in concert with other open source tools (probabilistic query parser, SolrTextTagger for entity extraction) to parse, interpret, and much more correctly model the true intent of user searches than traditional keyword-based search approaches.
Transitioning web application frameworks towards the Semantic Web (master the...Benjamin Heitmann
Presents the results of a survey of 54 Semantic Web applications and shows how they fit into 6 broad application types/patterns. For every pattern the capabilities, requirements and components are presented.
The full version of the master thesis is available at: http://eyaloren.org/pubs/heitmann-thesis.pdf
The survey itself is available at http://activerdf.org/survey
Similar to A Multidimensional Semantic Space for Data Model Independent Queries over RDF Data (20)
In this talk we will summarise some of the detectable trends on AI beyond deep learning. We will focus on the current transition from deep learning to deep semantics, describing the enabling infrastructures, challenges and opportunities in the construction of the next generation AI systems. The talk will focus on Natural Language Processing (NLP) as an AI sub-domain and will link to the research at the AI Systems Lab at the University of Manchester.
Building AI Applications using Knowledge GraphsAndre Freitas
Goals of this Tutorial:
Provide a broad view of the multiple perspectives underlying knowledge graphs.
Show knowledge graphs as a foundation for building AI systems.
Method:
Focus on the contemporary and emerging perspectives.
Sampling exemplar approaches and infrastructures on each of these emerging perspectives (not an exhaustive survey).
Effective Semantics for Engineering NLP SystemsAndre Freitas
Provide a synthesis of the emerging representation trends behind NLP systems.
Shift in perspective:
Effective engineering (task driven, scalable) instead of sound formalism.
Best-effort representation.
Knowledge Graphs (Frege revisited)
Information Extraction & Text Classification
Distributional Semantic Models
Knowledge Graphs & Distributional Semantics
(Distributional-Relational Models)
Applications of DRMs
KG Completion
Semantic Parsing
Natural Language Inference
This paper discusses the “Fine-Grained
Sentiment Analysis on Financial Microblogs
and News” task as part of
SemEval-2017, specifically under the
“Detecting sentiment, humour, and truth”
theme. This task contains two tracks, where
the first one concerns Microblog messages
and the second one covers News Statements
and Headlines. The main goal behind both
tracks was to predict the sentiment score for
each of the mentioned companies/stocks.
The sentiment scores for each text instance
adopted floating point values in the range
of -1 (very negative/bearish) to 1 (very
positive/bullish), with 0 designating neutral
sentiment. This task attracted a total of 32
participants, with 25 participating in Track
1 and 29 in Track 2.
Semantic Relation Classification: Task Formalisation and RefinementAndre Freitas
The identification of semantic relations between terms within texts is a fundamental task in Natural Language Processing which can support applications requiring a lightweight semantic interpretation model. Currently, semantic relation classification concentrates on relations which are evaluated over open-domain data. This work provides a critique on the set of abstract relations used for semantic relation classification with regard to their ability to express relationships between terms which are found in a domain-specific corpora. Based on this analysis, this work proposes an alternative semantic relation model based on reusing and extending the set of abstract relations present in the DOLCE ontology. The resulting set of relations is well grounded,
allows to capture a wide range of relations and could thus be used as a foundation for automatic classification of semantic relations.
Categorization of Semantic Roles for Dictionary DefinitionsAndre Freitas
Understanding the semantic relationships between terms is a fundamental task in natural language
processing applications. While structured resources that can express those relationships in
a formal way, such as ontologies, are still scarce, a large number of linguistic resources gathering
dictionary definitions is becoming available, but understanding the semantic structure of natural
language definitions is fundamental to make them useful in semantic interpretation tasks. Based
on an analysis of a subset of WordNet’s glosses, we propose a set of semantic roles that compose
the semantic structure of a dictionary definition, and show how they are related to the definition’s
syntactic configuration, identifying patterns that can be used in the development of information
extraction frameworks and semantic models.
Word Tagging with Foundational Ontology ClassesAndre Freitas
Semantic annotation is fundamental to deal with large-scale
lexical information, mapping the information to an enumerable set of
categories over which rules and algorithms can be applied, and foundational
ontology classes can be used as a formal set of categories for
such tasks. A previous alignment between WordNet noun synsets and
DOLCE provided a starting point for ontology-based annotation, but in
NLP tasks verbs are also of substantial importance. This work presents
an extension to the WordNet-DOLCE noun mapping, aligning verbs according
to their links to nouns denoting perdurants, transferring to the
verb the DOLCE class assigned to the noun that best represents that
verb’s occurrence. To evaluate the usefulness of this resource, we implemented
a foundational ontology-based semantic annotation framework,
that assigns a high-level foundational category to each word or phrase
in a text, and compared it to a similar annotation tool, obtaining an
increase of 9.05% in accuracy.
Different Semantic Perspectives for Question Answering SystemsAndre Freitas
Question Answering systems define one of the most complex tasks in computational semantics. The intrinsic complexity of the QA task allows researchers of QA systems to investigate and explore different perspectives of semantics. However, this complexity also induces a bias towards a systems perspective, where researchers are alienated from a deeper reasoning on the semantic principles that are in place within the different components of the system. In this talk we will explore the semantic challenges, principles and perspectives behind the components of QA systems, aiming at providing a principled map and overview on the contribution of each component within the QA semantic interpretation goal.
Schema-Agnostic Queries (SAQ-2015): Semantic Web ChallengeAndre Freitas
The Challenge in a Nutshell
To create a query mechanism that semantically matches schema-agnostic user queries to knowledge base elements
The Goal
To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data
Relevance
The increase in the size and in the semantic heterogeneity of database schemas are bringing new requirements for users querying and searching structured data. At this scale it can become unfeasible for data consumers to be familiar with the representation of the data in order to query it. At the center of this discussion is the semantic gap between users and databases, which becomes more central as the scale and complexity of the data grows. Addressing this gap is a fundamental part of the Semantic Web vision.
Schema-agnostic query mechanisms aim at allowing users to be abstracted from the representation of the data, supporting the automatic matching between queries and databases. This challenge aims at emphasizing the role of schema-agnosticism as a key requirement for contemporary database management, by providing a test collection for evaluating flexible query and search systems over structured data in terms of their level of schema-agnosticism (i.e. their ability to map a query issued with the user terminology and structure, mapping it to the dataset vocabulary). The challenge is instantiated in the context of Semantic Web datasets.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...Andre Freitas
The growing size, heterogeneity and complexity of databases demand the creation of strategies to facilitate users and systems to consume data. Ideally, query mechanisms should be schema-agnostic, i.e. they should be able to match user queries in their own vocabulary and syntax to the data, abstracting data consumers from the representation of the data. This work provides an informationtheoretical framework to evaluate the semantic complexity involved in the query-database communication, under a schema-agnostic query scenario. Different entropy measures are introduced to quantify the semantic phenomena involved in the user-database communication, including structural complexity, ambiguity, synonymy and vagueness. The entropy measures are validated using natural language queries over Semantic Web databases. The analysis of the semantic complexity is used to improve the understanding of the core semantic dimensions present at the query-data matching process, allowing the improvement of the design of schema-agnostic query mechanisms and defining measures which can be used to assess the semantic uncertainty or difficulty behind a schema-agnostic querying task.
Schema-agnositc queries over large-schema databases: a distributional semanti...Andre Freitas
The evolution of data environments towards the growth in the size, complexity, dy-
namicity and decentralisation (SCoDD) of schemas drastically impacts contemporary
data management. The SCoDD trend emerges as a central data management concern
in Big Data scenarios, where users and applications have a demand for more complete
data, produced by independent data sources, under different semantic assumptions and
contexts of use. Most Database Management Systems (DBMSs) today target a closed
communication scenario, where the symbolic schema of the database is known a priori
by the database user, which is able to interpret it in an unambiguous way. The context
in which the data is consumed and produced is well-defined and it is typically the
same context in which the data was created. In contrast, data management under the
SCoDD conditions target an open communication scenario where the symbolic system of
the database is unknown by the user and multiple interpretation contexts are possible.
In this case the database can be created under a different context from the database
user. The emergence of this new data environment demands the revisit of the semantic
assumptions behind databases and the design of data access mechanisms which can
support semantically heterogeneous (open communication) data environments.
This work aims at filling this gap by proposing a complementary semantic model for
databases, based on distributional semantic models. Distributional semantics provides a
complementary perspective to the formal perspective of database semantics, which supports
semantic approximation as a first-class database operation. Differently from models
which describe uncertain and incomplete data or probabilistic databases, distributional-
relational models focuses on the construction of conceptual approximation approaches
for databases, supported by a comprehensive semantic model automatically built from
large-scale unstructured data external to the database, which serves as a semantic/com-
monsense knowledge base. The semantic model can be used to support schema-agnosticqueries, i.e. abstracting the data consumer from a specific conceptualization behind the
data.
The proposed distributional-relational semantic model is supported by a distributional
structured vector space model, named τ −Space, which represents structured data under
a distributional semantic model representation which, in coordination with a query plan-
ning approach, supports a schema-agnostic query mechanism for large-schema databases.
The query mechanism is materialized in the Treo query engine and is evaluated using
schema-agnostic natural language queries.
The evaluation of the query mechanism confirms that distributional semantics provides
a high-recall, medium-high precision, and low maintainability solution to cope with
the abstraction and conceptual-level differences in schema-agnostic queries over largeschema/
schema-less open domain dataset
A Semantic Web Platform for Automating the Interpretation of Finite Element ...Andre Freitas
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a FE model can grow unmanageable. This work investigates the role of semantic technologies to improve the automation, interpretation and reproducibility of FE simulations. In particular, the paper focuses on
the definition of a reference semantic architecture for FE bio-simulations and on the discussion of strategies to bridge the gap between numerical-level
and conceptual-level representations. The discussion is grounded on the SIFEM platform, a semantic infrastructure for FE simulations for cochlear mechanics.
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Welcome to TechSoup New Member Orientation and Q&A (May 2024).pdfTechSoup
In this webinar you will learn how your organization can access TechSoup's wide variety of product discount and donation programs. From hardware to software, we'll give you a tour of the tools available to help your nonprofit with productivity, collaboration, financial management, donor tracking, security, and more.
A Multidimensional Semantic Space for Data Model Independent Queries over RDF Data
1. Digital Enterprise Research Institute www.deri.ie
A Multidimensional Semantic Space
for Data Model Independent Queries
over RDF Data
André Freitas, João Gabriel Oliveira, Edward Curry
Seán O’Riain
Copyright 2009 Digital Enterprise Research Institute. All rights reserved.
2. Outline
Digital Enterprise Research Institute www.deri.ie
Problem Space & Motivation
Description of the Approach
Evaluation
Conclusion & Future Work
3. Linked Data
Digital Enterprise Research Institute www.deri.ie
Uses the Web infrastructure and standards to
expose and interlink datasets.
Linked Data vision:
The Web as a single Dataspace.
Web of interlinked datasets.
5. Queries over Linked Data
Digital Enterprise Research Institute www.deri.ie
Linked Data brings a fundamental challenge for data
consumption:
How to query heterogeneous and distributed datasets?
At Web scale it is unfeasible for end-users to be aware of the
location and structure of datasets.
Demand for new query mechanisms for Linked Data (data
model independency).
7. Fundamental Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of
Barack Obama graduate?
Popescu (2003): Semantic tractability problem.
8. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
9. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Entity identification
10. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Entity search
11. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Approximate
semantic matching
12. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Approximate
semantic matching
13. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Approximate
semantic matching
14. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
Structural matching
15. Semantic Matching Problem
Digital Enterprise Research Institute www.deri.ie
From which university did the wife of Barack Obama graduate?
T- Space
16. Strategy
Digital Enterprise Research Institute www.deri.ie
Best-effort query model (ranked results).
Use of a distributional semantic model.
Two phase search process combining entity search
with spreading activation search.
22. Semantic Relatedness
Digital Enterprise Research Institute www.deri.ie
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.
Distributional semantic approaches address these
limitations.
23. Distributional Semantics
Digital Enterprise Research Institute www.deri.ie
Assumption: the context surrounding a given word
in a text provides important information about its
meaning.
Meaning is mediated by word distribution in the
corpora.
Simplified semantic model.
Opera is an art form in which singers and musicians perform a
dramatic work combining text (called a libretto) and musical score.
Opera is part of the Western classical music tradition. Opera
incorporates many of the elements of spoken theatre, such as acting,
scenery, and costumes and sometimes includes dance. The
performance is typically given in an opera house, accompanied by an
orchestra or smaller musical ensemble.
24. Explicit Semantic Analysis (ESA)
Digital Enterprise Research Institute www.deri.ie
Based on Wikipedia.
Interpretation vector using Wikipedia articles titles.
25. Building the T- Space (Steps)
Digital Enterprise Research Institute www.deri.ie
Building the distributional semantic model using
ESA.
Construction of instances spaces (TF/IDF).
Construction of classes spaces (ESA).
Construction of relation spaces (ESA).
26. Building the T- Space
Digital Enterprise Research Institute www.deri.ie
relations
instances properties
classes
27. Building the T- Space
Digital Enterprise Research Institute www.deri.ie
42. Conclusion & Future Work
Digital Enterprise Research Institute www.deri.ie
The T-Space semantic model shows a promising direction for
providing data model independent queries over RDF data.
Improvement of semantic tractability.
The distributional semantic model supports a flexible
matching between query terms and dataset terms in a best-
effort scenario.
Further improvements are needed:
QA features (e.g. answer type detection, operators).
User feedback mechanisms (disambiguation).
Entity recognition for complex classes.