375,031 research outputs found
Towards a Knowledge Graph based Speech Interface
Applications which use human speech as an input require a speech interface
with high recognition accuracy. The words or phrases in the recognised text are
annotated with a machine-understandable meaning and linked to knowledge graphs
for further processing by the target application. These semantic annotations of
recognised words can be represented as a subject-predicate-object triples which
collectively form a graph often referred to as a knowledge graph. This type of
knowledge representation facilitates to use speech interfaces with any spoken
input application, since the information is represented in logical, semantic
form, retrieving and storing can be followed using any web standard query
languages. In this work, we develop a methodology for linking speech input to
knowledge graphs and study the impact of recognition errors in the overall
process. We show that for a corpus with lower WER, the annotation and linking
of entities to the DBpedia knowledge graph is considerable. DBpedia Spotlight,
a tool to interlink text documents with the linked open data is used to link
the speech recognition output to the DBpedia knowledge graph. Such a
knowledge-based speech recognition interface is useful for applications such as
question answering or spoken dialog systems.Comment: Under Review in International Workshop on Grounding Language
Understanding, Satellite of Interspeech 201
A Survey on Knowledge Graphs: Representation, Acquisition and Applications
Human knowledge provides a formal understanding of the world. Knowledge
graphs that represent structural relations between entities have become an
increasingly popular research direction towards cognition and human-level
intelligence. In this survey, we provide a comprehensive review of knowledge
graph covering overall research topics about 1) knowledge graph representation
learning, 2) knowledge acquisition and completion, 3) temporal knowledge graph,
and 4) knowledge-aware applications, and summarize recent breakthroughs and
perspective directions to facilitate future research. We propose a full-view
categorization and new taxonomies on these topics. Knowledge graph embedding is
organized from four aspects of representation space, scoring function, encoding
models, and auxiliary information. For knowledge acquisition, especially
knowledge graph completion, embedding methods, path inference, and logical rule
reasoning, are reviewed. We further explore several emerging topics, including
meta relational learning, commonsense reasoning, and temporal knowledge graphs.
To facilitate future research on knowledge graphs, we also provide a curated
collection of datasets and open-source libraries on different tasks. In the
end, we have a thorough outlook on several promising research directions
Massive ontology interface
This paper describes the Massive Ontology Interface (MOI), a web portal which facilitates interaction with a large ontology (over 200,000 concepts and 1.6M assertions) that is built automatically using OpenCyc as a backbone. The aim of the interface is to simplify interaction with the massive amounts of information and guide the user towards understanding the ontologyâs data. Using either a text or graph-based representation, users can discuss and edit the ontology. Social elements utilizing gamification techniques are included to encourage users to create and collaborate on stored knowledge as part of a web community.
An evaluation by 30 users comparing MOI with OpenCycâs original interface showed significant improvements in user understanding of the ontology, although full testing of the interfaceâs social elements lies in the future
AN ERROR ANALYSIS ON REPRESENTATION LEARNING OF KNOWLEDGE GRAPH
Knowledge graph is a knowledge base containing integrated data in a graph-structure. Prior knowledge included in knowledge graph can make up for the insufficient reasoning ability of statistical machine learning methods. By utilizing representation of knowledge, researchers and information practitioners are capable of deepening algorithmsâ understanding of the real world by introducing plentiful common sense. However, a problem confusing researcher for a long period is that computers have difficulty in comprehending knowledge stored in knowledge graph, preventing the efficient usage of these graph-structured information. This study aims at presenting a comprehensive analysis on the knowledge representation generated by multiple methods. This analysis may inspire readers to reflect characteristics, advantages and disadvantages of knowledge representation learning models.Master of Science in Information Scienc
Interpreting Knowlege Graph Relation Representation From Word Embeddings
Many models learn representations of knowledge graph data by exploiting its
low-rank latent structure, encoding known relations between entities and
enabling unknown facts to be inferred. To predict whether a relation holds
between entities, embeddings are typically compared in the latent space
following a relation-specific mapping. Whilst their predictive performance has
steadily improved, how such models capture the underlying latent structure of
semantic information remains unexplained. Building on recent theoretical
understanding of word embeddings, we categorise knowledge graph relations into
three types and for each derive explicit requirements of their representations.
We show that empirical properties of relation representations and the relative
performance of leading knowledge graph representation methods are justified by
our analysis
A Theory of Link Prediction via Relational Weisfeiler-Leman on Knowledge Graphs
Graph neural networks are prominent models for representation learning over
graph-structured data. While the capabilities and limitations of these models
are well-understood for simple graphs, our understanding remains incomplete in
the context of knowledge graphs. Our goal is to provide a systematic
understanding of the landscape of graph neural networks for knowledge graphs
pertaining to the prominent task of link prediction. Our analysis entails a
unifying perspective on seemingly unrelated models and unlocks a series of
other models. The expressive power of various models is characterized via a
corresponding relational Weisfeiler-Leman algorithm. This analysis is extended
to provide a precise logical characterization of the class of functions
captured by a class of graph neural networks. The theoretical findings
presented in this paper explain the benefits of some widely employed practical
design choices, which are validated empirically.Comment: Proceedings of the Thirty-Seventh Annual Conference on Advances in
Neural Information Processing Systems (NeurIPS 2023). Code available at:
https://github.com/HxyScotthuang/CMPN
Joint Video and Text Parsing for Understanding Events and Answering Queries
We propose a framework for parsing video and text jointly for understanding
events and answering user queries. Our framework produces a parse graph that
represents the compositional structures of spatial information (objects and
scenes), temporal information (actions and events) and causal information
(causalities between events and fluents) in the video and text. The knowledge
representation of our framework is based on a spatial-temporal-causal And-Or
graph (S/T/C-AOG), which jointly models possible hierarchical compositions of
objects, scenes and events as well as their interactions and mutual contexts,
and specifies the prior probabilistic distribution of the parse graphs. We
present a probabilistic generative model for joint parsing that captures the
relations between the input video/text, their corresponding parse graphs and
the joint parse graph. Based on the probabilistic model, we propose a joint
parsing system consisting of three modules: video parsing, text parsing and
joint inference. Video parsing and text parsing produce two parse graphs from
the input video and text respectively. The joint inference module produces a
joint parse graph by performing matching, deduction and revision on the video
and text parse graphs. The proposed framework has the following objectives:
Firstly, we aim at deep semantic parsing of video and text that goes beyond the
traditional bag-of-words approaches; Secondly, we perform parsing and reasoning
across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG
representation; Thirdly, we show that deep joint parsing facilitates subsequent
applications such as generating narrative text descriptions and answering
queries in the forms of who, what, when, where and why. We empirically
evaluated our system based on comparison against ground-truth as well as
accuracy of query answering and obtained satisfactory results
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