763 research outputs found
Named Entity Resolution in Personal Knowledge Graphs
Entity Resolution (ER) is the problem of determining when two entities refer
to the same underlying entity. The problem has been studied for over 50 years,
and most recently, has taken on new importance in an era of large,
heterogeneous 'knowledge graphs' published on the Web and used widely in
domains as wide ranging as social media, e-commerce and search. This chapter
will discuss the specific problem of named ER in the context of personal
knowledge graphs (PKGs). We begin with a formal definition of the problem, and
the components necessary for doing high-quality and efficient ER. We also
discuss some challenges that are expected to arise for Web-scale data. Next, we
provide a brief literature review, with a special focus on how existing
techniques can potentially apply to PKGs. We conclude the chapter by covering
some applications, as well as promising directions for future research.Comment: To appear as a book chapter by the same name in an upcoming (Oct.
2023) book `Personal Knowledge Graphs (PKGs): Methodology, tools and
applications' edited by Tiwari et a
Explainable History Distillation by Marked Temporal Point Process
Explainability of machine learning models is mandatory when researchers
introduce these commonly believed black boxes to real-world tasks, especially
high-stakes ones. In this paper, we build a machine learning system to
automatically generate explanations of happened events from history by \gls{ca}
based on the \acrfull{tpp}. Specifically, we propose a new task called
\acrfull{ehd}. This task requires a model to distill as few events as possible
from observed history. The target is that the event distribution conditioned on
left events predicts the observed future noticeably worse. We then regard
distilled events as the explanation for the future. To efficiently solve
\acrshort{ehd}, we rewrite the task into a \gls{01ip} and directly estimate the
solution to the program by a model called \acrfull{model}. This work fills the
gap between our task and existing works, which only spot the difference between
factual and counterfactual worlds after applying a predefined modification to
the environment. Experiment results on Retweet and StackOverflow datasets prove
that \acrshort{model} significantly outperforms other \acrshort{ehd} baselines
and can reveal the rationale underpinning real-world processes
A Boxology of Design Patterns for Hybrid Learning and Reasoning Systems
We propose a set of compositional design patterns to describe a large variety
of systems that combine statistical techniques from machine learning with
symbolic techniques from knowledge representation. As in other areas of
computer science (knowledge engineering, software engineering, ontology
engineering, process mining and others), such design patterns help to
systematize the literature, clarify which combinations of techniques serve
which purposes, and encourage re-use of software components. We have validated
our set of compositional design patterns against a large body of recent
literature.Comment: 12 pages,55 reference
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