51,158 research outputs found
KMF: Knowledge-Aware Multi-Faceted Representation Learning for Zero-Shot Node Classification
Recently, Zero-Shot Node Classification (ZNC) has been an emerging and
crucial task in graph data analysis. This task aims to predict nodes from
unseen classes which are unobserved in the training process. Existing work
mainly utilizes Graph Neural Networks (GNNs) to associate features' prototypes
and labels' semantics thus enabling knowledge transfer from seen to unseen
classes. However, the multi-faceted semantic orientation in the
feature-semantic alignment has been neglected by previous work, i.e. the
content of a node usually covers diverse topics that are relevant to the
semantics of multiple labels. It's necessary to separate and judge the semantic
factors that tremendously affect the cognitive ability to improve the
generality of models. To this end, we propose a Knowledge-Aware Multi-Faceted
framework (KMF) that enhances the richness of label semantics via the extracted
KG (Knowledge Graph)-based topics. And then the content of each node is
reconstructed to a topic-level representation that offers multi-faceted and
fine-grained semantic relevancy to different labels. Due to the particularity
of the graph's instance (i.e., node) representation, a novel geometric
constraint is developed to alleviate the problem of prototype drift caused by
node information aggregation. Finally, we conduct extensive experiments on
several public graph datasets and design an application of zero-shot
cross-domain recommendation. The quantitative results demonstrate both the
effectiveness and generalization of KMF with the comparison of state-of-the-art
baselines
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.Comment: ICLR 2019 (Oral). Project page: http://nscl.csail.mit.edu
Ontology of core data mining entities
In this article, we present OntoDM-core, an ontology of core data mining
entities. OntoDM-core defines themost essential datamining entities in a three-layered
ontological structure comprising of a specification, an implementation and an application
layer. It provides a representational framework for the description of mining
structured data, and in addition provides taxonomies of datasets, data mining tasks,
generalizations, data mining algorithms and constraints, based on the type of data.
OntoDM-core is designed to support a wide range of applications/use cases, such as
semantic annotation of data mining algorithms, datasets and results; annotation of
QSAR studies in the context of drug discovery investigations; and disambiguation of
terms in text mining. The ontology has been thoroughly assessed following the practices
in ontology engineering, is fully interoperable with many domain resources and
is easy to extend
Mapping Big Data into Knowledge Space with Cognitive Cyber-Infrastructure
Big data research has attracted great attention in science, technology,
industry and society. It is developing with the evolving scientific paradigm,
the fourth industrial revolution, and the transformational innovation of
technologies. However, its nature and fundamental challenge have not been
recognized, and its own methodology has not been formed. This paper explores
and answers the following questions: What is big data? What are the basic
methods for representing, managing and analyzing big data? What is the
relationship between big data and knowledge? Can we find a mapping from big
data into knowledge space? What kind of infrastructure is required to support
not only big data management and analysis but also knowledge discovery, sharing
and management? What is the relationship between big data and science paradigm?
What is the nature and fundamental challenge of big data computing? A
multi-dimensional perspective is presented toward a methodology of big data
computing.Comment: 59 page
- …