5,094 research outputs found
Growing a Tree in the Forest: Constructing Folksonomies by Integrating Structured Metadata
Many social Web sites allow users to annotate the content with descriptive
metadata, such as tags, and more recently to organize content hierarchically.
These types of structured metadata provide valuable evidence for learning how a
community organizes knowledge. For instance, we can aggregate many personal
hierarchies into a common taxonomy, also known as a folksonomy, that will aid
users in visualizing and browsing social content, and also to help them in
organizing their own content. However, learning from social metadata presents
several challenges, since it is sparse, shallow, ambiguous, noisy, and
inconsistent. We describe an approach to folksonomy learning based on
relational clustering, which exploits structured metadata contained in personal
hierarchies. Our approach clusters similar hierarchies using their structure
and tag statistics, then incrementally weaves them into a deeper, bushier tree.
We study folksonomy learning using social metadata extracted from the
photo-sharing site Flickr, and demonstrate that the proposed approach addresses
the challenges. Moreover, comparing to previous work, the approach produces
larger, more accurate folksonomies, and in addition, scales better.Comment: 10 pages, To appear in the Proceedings of ACM SIGKDD Conference on
Knowledge Discovery and Data Mining(KDD) 201
TiFi: Taxonomy Induction for Fictional Domains [Extended version]
Taxonomies are important building blocks of structured knowledge bases, and their construction from text sources and Wikipedia has received much attention. In this paper we focus on the construction of taxonomies for fictional domains, using noisy category systems from fan wikis or text extraction as input. Such fictional domains are archetypes of entity universes that are poorly covered by Wikipedia, such as also enterprise-specific knowledge bases or highly specialized verticals. Our fiction-targeted approach, called TiFi, consists of three phases: (i) category cleaning, by identifying candidate categories that truly represent classes in the domain of interest, (ii) edge cleaning, by selecting subcategory relationships that correspond to class subsumption, and (iii) top-level construction, by mapping classes onto a subset of high-level WordNet categories. A comprehensive evaluation shows that TiFi is able to construct taxonomies for a diverse range of fictional domains such as Lord of the Rings, The Simpsons or Greek Mythology with very high precision and that it outperforms state-of-the-art baselines for taxonomy induction by a substantial margin
Inferring Concept Hierarchies from Text Corpora via Hyperbolic Embeddings
We consider the task of inferring is-a relationships from large text corpora.
For this purpose, we propose a new method combining hyperbolic embeddings and
Hearst patterns. This approach allows us to set appropriate constraints for
inferring concept hierarchies from distributional contexts while also being
able to predict missing is-a relationships and to correct wrong extractions.
Moreover -- and in contrast with other methods -- the hierarchical nature of
hyperbolic space allows us to learn highly efficient representations and to
improve the taxonomic consistency of the inferred hierarchies. Experimentally,
we show that our approach achieves state-of-the-art performance on several
commonly-used benchmarks
Efficient pruning of large knowledge graphs
In this paper we present an efficient and highly accurate algorithm to prune noisy or over-ambiguous knowledge graphs given as input an extensional definition of a domain of interest, namely as a set
of instances or concepts. Our method climbs the graph in a bottom-up fashion, iteratively layering
the graph and pruning nodes and edges in each layer while not compromising the connectivity of the set of input nodes. Iterative layering and protection of pre-defined nodes allow to extract semantically coherent DAG structures from noisy or over-ambiguous cyclic graphs, without loss of information and without incurring in computational bottlenecks, which are the main problem of stateof- the-art methods for cleaning large, i.e., Webscale,
knowledge graphs. We apply our algorithm to the tasks of pruning automatically acquired taxonomies using benchmarking data from a SemEval evaluation exercise, as well as the extraction of a domain-adapted taxonomy from theWikipedia category hierarchy. The results show the superiority of our approach over state-of-art algorithms in terms of both output quality and computational efficiency
Models of incremental concept formation
Given a set of observations, humans acquire concepts that organize those observations and use them in classifying future experiences. This type of concept formation can occur in the absence of a tutor and it can take place despite irrelevant and incomplete information. A reasonable model of such human concept learning should be both incremental and capable of handling this type of complex experiences that people encounter in the real world. In this paper, we review three previous models of incremental concept formation and then present CLASSIT, a model that extends these earlier systems. All of the models integrate the process of recognition and learning, and all can be viewed as carrying out search through the space of possible concept hierarchies. In an attempt to show that CLASSIT is a robust concept formation system, we also present some empirical studies of its behavior under a variety of conditions
Learning Hierarchically-Structured Concepts II: Overlapping Concepts, and Networks With Feedback
We continue our study from Lynch and Mallmann-Trenn (Neural Networks, 2021),
of how concepts that have hierarchical structure might be represented in
brain-like neural networks, how these representations might be used to
recognize the concepts, and how these representations might be learned.
In Lynch and Mallmann-Trenn (Neural Networks, 2021), we considered simple
tree-structured concepts and feed-forward layered networks. Here we extend the
model in two ways: we allow limited overlap between children of different
concepts, and we allow networks to include feedback edges.
For these more general cases, we describe and analyze algorithms for
recognition and algorithms for learning
Recommended from our members
A survey of clustering methods
In this paper, I describe a large variety of clustering methods within a single framework. This paper unifies work across different fields, from biology (numerical taxonomy) to machine learning (concept formation). An important objective for this paper is to show that one can benefit by a knowledge of research across different disciplines. After describing the task from a set of different viewpoints or paradigms, I begin by describing the similarity measures or evaluation functions that form the basis of any clustering technique. Next, I describe a number of different algorithms that use these measures, and I close with a brief discussion of ways to evaluate different approaches to clustering
- …