1,508 research outputs found
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
On the Evolution of Knowledge Graphs: A Survey and Perspective
Knowledge graphs (KGs) are structured representations of diversified
knowledge. They are widely used in various intelligent applications. In this
article, we provide a comprehensive survey on the evolution of various types of
knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs)
and techniques for knowledge extraction and reasoning. Furthermore, we
introduce the practical applications of different types of KGs, including a
case study in financial analysis. Finally, we propose our perspective on the
future directions of knowledge engineering, including the potential of
combining the power of knowledge graphs and large language models (LLMs), and
the evolution of knowledge extraction, reasoning, and representation
Text2Bundle: Towards Personalized Query-based Bundle Generation
Bundle generation aims to provide a bundle of items for the user, and has
been widely studied and applied on online service platforms. Existing bundle
generation methods mainly utilized user's preference from historical
interactions in common recommendation paradigm, and ignored the potential
textual query which is user's current explicit intention. There can be a
scenario in which a user proactively queries a bundle with some natural
language description, the system should be able to generate a bundle that
exactly matches the user's intention through the user's query and preferences.
In this work, we define this user-friendly scenario as Query-based Bundle
Generation task and propose a novel framework Text2Bundle that leverages both
the user's short-term interests from the query and the user's long-term
preferences from the historical interactions. Our framework consists of three
modules: (1) a query interest extractor that mines the user's fine-grained
interests from the query; (2) a unified state encoder that learns the current
bundle context state and the user's preferences based on historical interaction
and current query; and (3) a bundle generator that generates personalized and
complementary bundles using a reinforcement learning with specifically designed
rewards. We conduct extensive experiments on three real-world datasets and
demonstrate the effectiveness of our framework compared with several
state-of-the-art methods
Semantic-guided predictive modeling and relational learning within industrial knowledge graphs
The ubiquitous availability of data in todayās manufacturing environments, mainly driven by the extended usage of software and built-in sensing capabilities in automation systems, enables companies to embrace more advanced predictive modeling and analysis in order to optimize processes and usage of equipment. While the potential insight gained from such analysis is high, it often remains untapped, since integration and analysis of data silos from diļ¬erent production domains requires high manual eļ¬ort and is therefore not economic. Addressing these challenges, digital representations of production equipment, so-called digital twins, have emerged leading the way to semantic interoperability across systems in diļ¬erent domains. From a data modeling point of view, digital twins can be seen as industrial knowledge graphs, which are used as semantic backbone of manufacturing software systems and data analytics. Due to the prevalent historically grown and scattered manufacturing software system landscape that is comprising of numerous proprietary information models, data sources are highly heterogeneous. Therefore, there is an increasing need for semi-automatic support in data modeling, enabling end-user engineers to model their domain and maintain a uniļ¬ed semantic knowledge graph across the company. Once the data modeling and integration is done, further challenges arise, since there has been little research on how knowledge graphs can contribute to the simpliļ¬cation and abstraction of statistical analysis and predictive modeling, especially in manufacturing.
In this thesis, new approaches for modeling and maintaining industrial knowledge graphs with focus on the application of statistical models are presented. First, concerning data modeling, we discuss requirements from several existing standard information models and analytic use cases in the manufacturing and automation system domains and derive a fragment of the OWL 2 language that is expressive enough to cover the required semantics for a broad range of use cases. The prototypical implementation enables domain end-users, i.e. engineers, to extend the basis ontology model with intuitive semantics. Furthermore it supports eļ¬cient reasoning and constraint checking via translation to rule-based representations. Based on these models, we propose an architecture for the end-user facilitated application of statistical models using ontological concepts and ontology-based data access paradigms.
In addition to that we present an approach for domain knowledge-driven preparation of predictive models in terms of feature selection and show how schema-level reasoning in the OWL 2 language can be employed for this task within knowledge graphs of industrial automation systems. A production cycle time prediction model in an example application scenario serves as a proof of concept and demonstrates that axiomatized domain knowledge about features can give competitive performance compared to purely data-driven ones. In the case of high-dimensional data with small sample size, we show that graph kernels of domain ontologies can provide additional information on the degree of variable
dependence. Furthermore, a special application of feature selection in graph-structured data is presented and we develop a method that allows to incorporate domain constraints derived from meta-paths in knowledge graphs in a branch-and-bound pattern enumeration algorithm.
Lastly, we discuss maintenance of facts in large-scale industrial knowledge graphs focused on latent variable models for the automated population and completion of missing facts. State-of-the art approaches can not deal with time-series data in form of events that naturally occur in industrial applications. Therefore we present an extension of learning knowledge graph embeddings in conjunction with data in form of event logs. Finally, we design several use case scenarios of missing information and evaluate our embedding approach on data coming from a real-world factory environment.
We draw the conclusion that industrial knowledge graphs are a powerful tool that can be used by end-users in the manufacturing domain for data modeling and model validation.
They are especially suitable in terms of the facilitated application of statistical models in conjunction with background domain knowledge by providing information about features upfront. Furthermore, relational learning approaches showed great potential to semi-automatically infer missing facts and provide recommendations to production operators on how to keep stored facts in synch with the real world
Combining Representation Learning with Logic for Language Processing
The current state-of-the-art in many natural language processing and
automated knowledge base completion tasks is held by representation learning
methods which learn distributed vector representations of symbols via
gradient-based optimization. They require little or no hand-crafted features,
thus avoiding the need for most preprocessing steps and task-specific
assumptions. However, in many cases representation learning requires a large
amount of annotated training data to generalize well to unseen data. Such
labeled training data is provided by human annotators who often use formal
logic as the language for specifying annotations. This thesis investigates
different combinations of representation learning methods with logic for
reducing the need for annotated training data, and for improving
generalization.Comment: PhD Thesis, University College London, Submitted and accepted in 201
Knowledge Graphs Meet Multi-Modal Learning: A Comprehensive Survey
Knowledge Graphs (KGs) play a pivotal role in advancing various AI
applications, with the semantic web community's exploration into multi-modal
dimensions unlocking new avenues for innovation. In this survey, we carefully
review over 300 articles, focusing on KG-aware research in two principal
aspects: KG-driven Multi-Modal (KG4MM) learning, where KGs support multi-modal
tasks, and Multi-Modal Knowledge Graph (MM4KG), which extends KG studies into
the MMKG realm. We begin by defining KGs and MMKGs, then explore their
construction progress. Our review includes two primary task categories:
KG-aware multi-modal learning tasks, such as Image Classification and Visual
Question Answering, and intrinsic MMKG tasks like Multi-modal Knowledge Graph
Completion and Entity Alignment, highlighting specific research trajectories.
For most of these tasks, we provide definitions, evaluation benchmarks, and
additionally outline essential insights for conducting relevant research.
Finally, we discuss current challenges and identify emerging trends, such as
progress in Large Language Modeling and Multi-modal Pre-training strategies.
This survey aims to serve as a comprehensive reference for researchers already
involved in or considering delving into KG and multi-modal learning research,
offering insights into the evolving landscape of MMKG research and supporting
future work.Comment: Ongoing work; 41 pages (Main Text), 55 pages (Total), 11 Tables, 13
Figures, 619 citations; Paper list is available at
https://github.com/zjukg/KG-MM-Surve
Unifying Large Language Models and Knowledge Graphs: A Roadmap
Large language models (LLMs), such as ChatGPT and GPT4, are making new waves
in the field of natural language processing and artificial intelligence, due to
their emergent ability and generalizability. However, LLMs are black-box
models, which often fall short of capturing and accessing factual knowledge. In
contrast, Knowledge Graphs (KGs), Wikipedia and Huapu for example, are
structured knowledge models that explicitly store rich factual knowledge. KGs
can enhance LLMs by providing external knowledge for inference and
interpretability. Meanwhile, KGs are difficult to construct and evolving by
nature, which challenges the existing methods in KGs to generate new facts and
represent unseen knowledge. Therefore, it is complementary to unify LLMs and
KGs together and simultaneously leverage their advantages. In this article, we
present a forward-looking roadmap for the unification of LLMs and KGs. Our
roadmap consists of three general frameworks, namely, 1) KG-enhanced LLMs,
which incorporate KGs during the pre-training and inference phases of LLMs, or
for the purpose of enhancing understanding of the knowledge learned by LLMs; 2)
LLM-augmented KGs, that leverage LLMs for different KG tasks such as embedding,
completion, construction, graph-to-text generation, and question answering; and
3) Synergized LLMs + KGs, in which LLMs and KGs play equal roles and work in a
mutually beneficial way to enhance both LLMs and KGs for bidirectional
reasoning driven by both data and knowledge. We review and summarize existing
efforts within these three frameworks in our roadmap and pinpoint their future
research directions.Comment: 29 pages, 25 figure
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