Querying Linked Data using Semantic Relatedness: A Vocabulary Independent Approach

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The document discusses the Digital Enterprise Research Institute's research into digital business strategies and transformation. It describes how digital technologies impact business models and provides frameworks for analyzing digital capabilities and maturity. The document also outlines challenges for businesses and offers recommendations for creating competitive advantage through digital innovation.

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Copyright 2009 Digital Enterprise Research Institute. All rights reserved.

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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 resources from these areas will give a concrete understanding on how to jump-start your own semantic system.

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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.

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Different Semantic Perspectives for Question Answering Systems

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

WISS QA Do it yourself Question answering over Linked Data

Semantics at Scale: A Distributional Approach

Semantics at Scale: A Distributional Approach

Semantics at Scale: A Distributional Approach

Schema-agnositc queries over large-schema databases: a distributional semanti...

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

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...

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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