13,639 research outputs found
Building Rule Hierarchies for Efficient Logical Rule Learning from Knowledge Graphs
Many systems have been developed in recent years to mine logical rules from
large-scale Knowledge Graphs (KGs), on the grounds that representing
regularities as rules enables both the interpretable inference of new facts,
and the explanation of known facts. Among these systems, the walk-based methods
that generate the instantiated rules containing constants by abstracting
sampled paths in KGs demonstrate strong predictive performance and
expressivity. However, due to the large volume of possible rules, these systems
do not scale well where computational resources are often wasted on generating
and evaluating unpromising rules. In this work, we address such scalability
issues by proposing new methods for pruning unpromising rules using rule
hierarchies. The approach consists of two phases. Firstly, since rule
hierarchies are not readily available in walk-based methods, we have built a
Rule Hierarchy Framework (RHF), which leverages a collection of subsumption
frameworks to build a proper rule hierarchy from a set of learned rules. And
secondly, we adapt RHF to an existing rule learner where we design and
implement two methods for Hierarchical Pruning (HPMs), which utilize the
generated hierarchies to remove irrelevant and redundant rules. Through
experiments over four public benchmark datasets, we show that the application
of HPMs is effective in removing unpromising rules, which leads to significant
reductions in the runtime as well as in the number of learned rules, without
compromising the predictive performance
Symbols and the bifurcation between procedural and conceptual thinking
Symbols occupy a pivotal position between processes to be carried out and concepts to be thought about. They allow us both to d o mathematical problems and to think about mathematical relationships.
In this presentation we consider the discontinuities that occur in the learning path taken by different students, leading to a divergence between conceptual and procedural thinking. Evidence will be given from several different contexts in the development of symbols through
arithmetic, algebra and calculus, then on to the formalism of axiomatic mathematics. This is taken from a number of research studies recently performed for doctoral dissertations at the University of Warwick by students from the USA, Malaysia, Cyprus and Brazil, with data collected
in the USA, Malaysia and the United Kingdom. All the studies form part of a broad investigation into why some students succeed yet others fail
Inductive queries for a drug designing robot scientist
It is increasingly clear that machine learning algorithms need to be integrated in an iterative scientific discovery loop, in which data is queried repeatedly by means of inductive queries and where the computer provides guidance to the experiments that are being performed. In this chapter, we summarise several key challenges in achieving this integration of machine learning and data mining algorithms in methods for the discovery of Quantitative Structure Activity Relationships (QSARs). We introduce the concept of a robot scientist, in which all steps of the discovery process are automated; we discuss the representation of molecular data such that knowledge discovery tools can analyse it, and we discuss the adaptation of machine learning and data mining algorithms to guide QSAR experiments
Towards an Intelligent Tutor for Mathematical Proofs
Computer-supported learning is an increasingly important form of study since
it allows for independent learning and individualized instruction. In this
paper, we discuss a novel approach to developing an intelligent tutoring system
for teaching textbook-style mathematical proofs. We characterize the
particularities of the domain and discuss common ITS design models. Our
approach is motivated by phenomena found in a corpus of tutorial dialogs that
were collected in a Wizard-of-Oz experiment. We show how an intelligent tutor
for textbook-style mathematical proofs can be built on top of an adapted
assertion-level proof assistant by reusing representations and proof search
strategies originally developed for automated and interactive theorem proving.
The resulting prototype was successfully evaluated on a corpus of tutorial
dialogs and yields good results.Comment: In Proceedings THedu'11, arXiv:1202.453
Practopoiesis: Or how life fosters a mind
The mind is a biological phenomenon. Thus, biological principles of
organization should also be the principles underlying mental operations.
Practopoiesis states that the key for achieving intelligence through adaptation
is an arrangement in which mechanisms laying a lower level of organization, by
their operations and interaction with the environment, enable creation of
mechanisms lying at a higher level of organization. When such an organizational
advance of a system occurs, it is called a traverse. A case of traverse is when
plasticity mechanisms (at a lower level of organization), by their operations,
create a neural network anatomy (at a higher level of organization). Another
case is the actual production of behavior by that network, whereby the
mechanisms of neuronal activity operate to create motor actions. Practopoietic
theory explains why the adaptability of a system increases with each increase
in the number of traverses. With a larger number of traverses, a system can be
relatively small and yet, produce a higher degree of adaptive/intelligent
behavior than a system with a lower number of traverses. The present analyses
indicate that the two well-known traverses-neural plasticity and neural
activity-are not sufficient to explain human mental capabilities. At least one
additional traverse is needed, which is named anapoiesis for its contribution
in reconstructing knowledge e.g., from long-term memory into working memory.
The conclusions bear implications for brain theory, the mind-body explanatory
gap, and developments of artificial intelligence technologies.Comment: Revised version in response to reviewer comment
Ontologies and Information Extraction
This report argues that, even in the simplest cases, IE is an ontology-driven
process. It is not a mere text filtering method based on simple pattern
matching and keywords, because the extracted pieces of texts are interpreted
with respect to a predefined partial domain model. This report shows that
depending on the nature and the depth of the interpretation to be done for
extracting the information, more or less knowledge must be involved. This
report is mainly illustrated in biology, a domain in which there are critical
needs for content-based exploration of the scientific literature and which
becomes a major application domain for IE
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