Querying Linked Data using Semantic Relatedness: A Vocabulary Independent Approach
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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
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To support easy querying over complex databases with large schemata, relieving users from the need to understand the formal representation of the data
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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.
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problems such as the increase in schema complexity, semantic inconsistency, incompleteness
and scalability. This paper proposes a selective graph navigation
mechanism based on a distributional relational semantic model which can be applied
to querying & reasoning over heterogeneous knowledge bases (KBs). The
approach can be used for approximative reasoning, querying and associational
knowledge discovery. In this paper we focus on commonsense reasoning as the
main motivational scenario for the approach. The approach focuses on addressing
the following problems: (i) providing a semantic selection mechanism for facts
which are relevant and meaningful in a specific reasoning & querying context
and (ii) allowing coping with information incompleteness in large KBs. The approach
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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
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Crossing the Vocabulary Gap for Querying Complex and Heterogeneous Databases
Crossing the Vocabulary Gap for Querying Complex and Heterogeneous Databases: A Distributional-Compositional Semantics Perspective
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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.
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The increasing availability of data on the Web provided by
the emergence of Web 2.0 applications and, more recently by Linked
Data, brought additional complexity to data management tasks, where
the number of available data sources and their associated heterogeneity drastically increases. In this scenario, where data is reused and repurposed on a new scale, the pattern expressed as Extract-Transform-Load
(ETL) emerges as a fundamental and recurrent process for both producers and consumers of data on the Web. In addition to ETL, provenance,
the representation of source artifacts, processes and agents behind data, becomes another cornerstone element for Web data management, playing
a fundamental role in data quality assessment, data semantics and facilitating the reproducibility of data transformation processes. This paper proposes the convergence of this two Web data management concerns, introducing a principled provenance model for ETL processes in the form
of a vocabulary based on the Open Provenance Model (OPM) standard and focusing on the provision of an interoperable provenance model for Web-based ETL environments. The proposed ETL provenance model is instantiated in a real-world sustainability reporting scenario.
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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.
Towards a Distributional Semantic Web Stack
The ability of distributional semantic models (DSMs) to dis-
cover similarities over large scale heterogeneous and poorly structured data brings them as a promising universal and low-effort framework to support semantic approximation and knowledge discovery. This position paper explores the role of distributional semantics in the Semantic Web vision, based on the state-of-the-art distributional-relational models, categorizing and generalizing existing approaches into a Distributional Semantic Web stack.
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Finite Element (FE) models provide a rich framework to simulate dynamic biological systems, with applications ranging from hearing to cardiovascular research. With the growing complexity and sophistication of FE bio-simulation models (e.g. multi-scale and multi-domain models), the effort associated with the creation, analysis and reuse of
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.
Representing Texts as contextualized Entity Centric Linked Data Graphs
The integration of a small fraction of the information present in the Web of Documents to the Linked Data
Web can provide a significant shift on the amount of information available to data consumers. However, information extracted from text does not easily fit into the usually highly normalized structure of ontology-based datasets. While the representation of structured data assumes a high level of regularity, relatively
simple and consistent conceptual models, the representation of information extracted from texts need to take into account large terminological variation, complex contextual/dependency patterns, and fuzzy or conflicting semantics. This work focuses on bridging the gap between structured and unstructured data, proposing the representation of text as structured discourse graphs (SDGs), targeting an RDF representation of unstructured
data. The representation focuses on a semantic best-effort information extraction scenario, where information from text is extracted under a pay-as-you-go data quality perspective, trading terminological normalization for domain-independency, context capture, wider representation scope and maximization of textual
information capture.
A Multidimensional Semantic Space for Data Model Independent Queries over RDF...
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.
On the Semantic Representation and Extraction of Complex Category Descriptors
Natural language descriptors used for categorizations are
present from folksonomies to ontologies. While some descriptors are composed of simple expressions, other descriptors have complex compositional patterns (e.g. ‘French Senators Of The Second Empire’, ‘Churches
Destroyed In The Great Fire Of London And Not Rebuilt’). As conceptual models get more complex and decentralized, more content is transferred to unstructured natural language descriptors, increasing the
terminological variation, reducing the conceptual integration and the structure level of the model. This work describes a formal representation for complex natural language category descriptors (NLCDs). In the
representation, complex categories are decomposed into a graph of primitive concepts, supporting their interlinking and semantic interpretation. A category extractor is built and the quality of its extraction under the proposed representation model is evaluated.
A Distributional Approach for Terminological Semantic Search on the Linked Da...
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.
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
A distributional structured semantic space for querying rdf graph data
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.
Categorization of Semantic Roles for Dictionary Definitions
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.
How hard is this Query? Measuring the Semantic Complexity of Schema-agnostic ...
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-Agnostic Queries (SAQ-2015): Semantic Web Challenge
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.
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
Tasks such as question answering and semantic search are dependent
on the ability of querying & reasoning over large-scale commonsense knowledge
bases (KBs). However, dealing with commonsense data demands coping with
problems such as the increase in schema complexity, semantic inconsistency, incompleteness
and scalability. This paper proposes a selective graph navigation
mechanism based on a distributional relational semantic model which can be applied
to querying & reasoning over heterogeneous knowledge bases (KBs). The
approach can be used for approximative reasoning, querying and associational
knowledge discovery. In this paper we focus on commonsense reasoning as the
main motivational scenario for the approach. The approach focuses on addressing
the following problems: (i) providing a semantic selection mechanism for facts
which are relevant and meaningful in a specific reasoning & querying context
and (ii) allowing coping with information incompleteness in large KBs. The approach
is evaluated using ConceptNet as a commonsense KB, and achieved high
selectivity, high scalability and high accuracy in the selection of meaningful nav-
igational paths. Distributional semantics is also used as a principled mechanism
to cope with information incompleteness.
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A Multidimensional Semantic Space for Data Model Independent Queries over RDF...
A Multidimensional Semantic Space for Data Model Independent Queries over RDF...
On the Semantic Representation and Extraction of Complex Category Descriptors
On the Semantic Representation and Extraction of Complex Category Descriptors
A Distributional Approach for Terminological Semantic Search on the Linked Da...
A Distributional Approach for Terminological Semantic Search on the Linked Da...
Question Answering over Linked Data (Reasoning Web Summer School)
Question Answering over Linked Data (Reasoning Web Summer School)
A distributional structured semantic space for querying rdf graph data
A distributional structured semantic space for querying rdf graph data
Categorization of Semantic Roles for Dictionary Definitions
Categorization of Semantic Roles for Dictionary Definitions
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-Agnostic Queries (SAQ-2015): Semantic Web Challenge
Schema-Agnostic Queries (SAQ-2015): Semantic Web Challenge
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Distributional Semantics Approach for Selective Reasoning on Commonsense Gr...
A Compositional-distributional Semantic Model over Structured Data
A Compositional-distributional Semantic Model over Structured Data
More from Andre Freitas
AI Beyond Deep Learning
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 Graphs
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 Systems
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
SemEval-2017 Task 5: Fine-Grained Sentiment Analysis on Financial Microblogs ...
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 Refinement
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.
Word Tagging with Foundational Ontology Classes
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 Systems
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-agnositc queries over large-schema databases: a distributional semanti...
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
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
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 or
vocabulary-independent, 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. 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.
Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributio...
The demand to access large amounts of heterogeneous structured
data is emerging as a trend for many users and applications.
However, the effort involved in querying heterogeneous
and distributed third-party databases can create major
barriers for data consumers. At the core of this problem is
the semantic gap between the way users express their information
needs and the representation of the data. This work
aims to provide a natural language interface and an associated
semantic index to support an increased level of vocabulary
independency for queries over Linked Data/Semantic
Web datasets, using a distributional-compositional semantics
approach. Distributional semantics focuses on the automatic
construction of a semantic model based on the statistical distribution
of co-occurring words in large-scale texts. The proposed
query model targets the following features: (i) a principled
semantic approximation approach with low adaptation
effort (independent from manually created resources such as
ontologies, thesauri or dictionaries), (ii) comprehensive semantic
matching supported by the inclusion of large volumes
of distributional (unstructured) commonsense knowledge into
the semantic approximation process and (iii) expressive natural language queries. The approach is evaluated using natural language queries on an open domain dataset and achieved avg. recall=0.81, mean avg. precision=0.62 and mean reciprocal rank=0.49.
More from Andre Freitas (17)
AI & Scientific Discovery in Oncology: Opportunities, Challenges & Trends
AI & Scientific Discovery in Oncology: Opportunities, Challenges & Trends
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
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-agnositc queries over large-schema databases: a distributional semanti...
Schema-agnositc queries over large-schema databases: a distributional semanti...
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
On the Semantic Mapping of Schema-agnostic Queries: A Preliminary Study
Question Answering over Linked Data: Challenges, Approaches & Trends (Tutoria...
Question Answering over Linked Data: Challenges, Approaches & Trends (Tutoria...
Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributio...
Natural Language Queries over Heterogeneous Linked Data Graphs: A Distributio...
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