828 research outputs found
Hierarchy-based Image Embeddings for Semantic Image Retrieval
Deep neural networks trained for classification have been found to learn
powerful image representations, which are also often used for other tasks such
as comparing images w.r.t. their visual similarity. However, visual similarity
does not imply semantic similarity. In order to learn semantically
discriminative features, we propose to map images onto class embeddings whose
pair-wise dot products correspond to a measure of semantic similarity between
classes. Such an embedding does not only improve image retrieval results, but
could also facilitate integrating semantics for other tasks, e.g., novelty
detection or few-shot learning. We introduce a deterministic algorithm for
computing the class centroids directly based on prior world-knowledge encoded
in a hierarchy of classes such as WordNet. Experiments on CIFAR-100, NABirds,
and ImageNet show that our learned semantic image embeddings improve the
semantic consistency of image retrieval results by a large margin.Comment: Accepted at WACV 2019. Source code:
https://github.com/cvjena/semantic-embedding
Learning Structured Knowledge from Social Tagging Data A critical review of methods and techniques
For more than a decade, researchers have been proposing various methods and techniques to mine social tagging data and to learn structured knowledge. It is essential to conduct a comprehensive survey on the related work, which would benefit the research community by providing better understanding of the state-of-the-art and insights into the future research directions. The paper first defines the spectrum of Knowledge Organization Systems, from unstructured with less semantics to highly structured with richer semantics. It then reviews the related work by classifying the methods and techniques into two main categories, namely, learning term lists and learning relations. The method and techniques originated from natural language processing, data mining, machine learning, social network analysis, and the Semantic Web are discussed in detail under the two categories. We summarize the prominent issues with the current research and highlight future directions on learning constantly evolving knowledge from social media data
Evaluating Knowledge Anchors in Data Graphs against Basic Level Objects
The growing number of available data graphs in the form of RDF Linked Da-ta enables the development of semantic exploration applications in many domains. Often, the users are not domain experts and are therefore unaware of the complex knowledge structures represented in the data graphs they in-teract with. This hinders users’ experience and effectiveness. Our research concerns intelligent support to facilitate the exploration of data graphs by us-ers who are not domain experts. We propose a new navigation support ap-proach underpinned by the subsumption theory of meaningful learning, which postulates that new concepts are grasped by starting from familiar concepts which serve as knowledge anchors from where links to new knowledge are made. Our earlier work has developed several metrics and the corresponding algorithms for identifying knowledge anchors in data graphs. In this paper, we assess the performance of these algorithms by considering the user perspective and application context. The paper address the challenge of aligning basic level objects that represent familiar concepts in human cog-nitive structures with automatically derived knowledge anchors in data graphs. We present a systematic approach that adapts experimental methods from Cognitive Science to derive basic level objects underpinned by a data graph. This is used to evaluate knowledge anchors in data graphs in two ap-plication domains - semantic browsing (Music) and semantic search (Ca-reers). The evaluation validates the algorithms, which enables their adoption over different domains and application contexts
Multiple Texts as a Limiting Factor in Online Learning: Quantifying (Dis-)similarities of Knowledge Networks across Languages
We test the hypothesis that the extent to which one obtains information on a
given topic through Wikipedia depends on the language in which it is consulted.
Controlling the size factor, we investigate this hypothesis for a number of 25
subject areas. Since Wikipedia is a central part of the web-based information
landscape, this indicates a language-related, linguistic bias. The article
therefore deals with the question of whether Wikipedia exhibits this kind of
linguistic relativity or not. From the perspective of educational science, the
article develops a computational model of the information landscape from which
multiple texts are drawn as typical input of web-based reading. For this
purpose, it develops a hybrid model of intra- and intertextual similarity of
different parts of the information landscape and tests this model on the
example of 35 languages and corresponding Wikipedias. In this way the article
builds a bridge between reading research, educational science, Wikipedia
research and computational linguistics.Comment: 40 pages, 13 figures, 5 table
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Linked knowledge sources for topic classification of microposts: a semantic graph-based approach
Short text messages, a.k.a microposts (e.g., tweets), have proven to be an effective channel for revealing information about trends and events, ranging from those related to disaster (e.g., Hurricane Sandy) to those related to violence (e.g., Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond.
In this work we study the problem of topic classification (TC) of microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information.
In order to provide contextual information to microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of microposts with features extracted only from the microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of microposts.
Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and microposts at a conceptual level, considering the enriched representation of these documents.
Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures
Type-Constrained Representation Learning in Knowledge Graphs
Large knowledge graphs increasingly add value to various applications that
require machines to recognize and understand queries and their semantics, as in
search or question answering systems. Latent variable models have increasingly
gained attention for the statistical modeling of knowledge graphs, showing
promising results in tasks related to knowledge graph completion and cleaning.
Besides storing facts about the world, schema-based knowledge graphs are backed
by rich semantic descriptions of entities and relation-types that allow
machines to understand the notion of things and their semantic relationships.
In this work, we study how type-constraints can generally support the
statistical modeling with latent variable models. More precisely, we integrated
prior knowledge in form of type-constraints in various state of the art latent
variable approaches. Our experimental results show that prior knowledge on
relation-types significantly improves these models up to 77% in link-prediction
tasks. The achieved improvements are especially prominent when a low model
complexity is enforced, a crucial requirement when these models are applied to
very large datasets. Unfortunately, type-constraints are neither always
available nor always complete e.g., they can become fuzzy when entities lack
proper typing. We show that in these cases, it can be beneficial to apply a
local closed-world assumption that approximates the semantics of relation-types
based on observations made in the data
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