20,277 research outputs found
Semantic metrics
In the context of the Semantic Web, many ontology-related operations, e.g. ontology ranking, segmentation, alignment, articulation, reuse, evaluation, can be boiled down to one fundamental operation: computing the similarity and?or dissimilarity among ontological entities, and in some cases among ontologies themselves. In this paper, we review standard metrics for computing distance measures and we propose a series of semantic metrics. We give a formal account of semantic metrics drawn from a variety of research disciplines, and enrich them with semantics based on standard Description Logic constructs. We argue that concept-based metrics can be aggregated to produce numeric distances at ontology-level and we speculate on the usability of our ideas through potential areas
Offline Handwritten Signature Verification - Literature Review
The area of Handwritten Signature Verification has been broadly researched in
the last decades, but remains an open research problem. The objective of
signature verification systems is to discriminate if a given signature is
genuine (produced by the claimed individual), or a forgery (produced by an
impostor). This has demonstrated to be a challenging task, in particular in the
offline (static) scenario, that uses images of scanned signatures, where the
dynamic information about the signing process is not available. Many
advancements have been proposed in the literature in the last 5-10 years, most
notably the application of Deep Learning methods to learn feature
representations from signature images. In this paper, we present how the
problem has been handled in the past few decades, analyze the recent
advancements in the field, and the potential directions for future research.Comment: Accepted to the International Conference on Image Processing Theory,
Tools and Applications (IPTA 2017
Does the Geometry of Word Embeddings Help Document Classification? A Case Study on Persistent Homology Based Representations
We investigate the pertinence of methods from algebraic topology for text
data analysis. These methods enable the development of
mathematically-principled isometric-invariant mappings from a set of vectors to
a document embedding, which is stable with respect to the geometry of the
document in the selected metric space. In this work, we evaluate the utility of
these topology-based document representations in traditional NLP tasks,
specifically document clustering and sentiment classification. We find that the
embeddings do not benefit text analysis. In fact, performance is worse than
simple techniques like , indicating that the geometry of the
document does not provide enough variability for classification on the basis of
topic or sentiment in the chosen datasets.Comment: 5 pages, 3 figures. Rep4NLP workshop at ACL 201
kLog: A Language for Logical and Relational Learning with Kernels
We introduce kLog, a novel approach to statistical relational learning.
Unlike standard approaches, kLog does not represent a probability distribution
directly. It is rather a language to perform kernel-based learning on
expressive logical and relational representations. kLog allows users to specify
learning problems declaratively. It builds on simple but powerful concepts:
learning from interpretations, entity/relationship data modeling, logic
programming, and deductive databases. Access by the kernel to the rich
representation is mediated by a technique we call graphicalization: the
relational representation is first transformed into a graph --- in particular,
a grounded entity/relationship diagram. Subsequently, a choice of graph kernel
defines the feature space. kLog supports mixed numerical and symbolic data, as
well as background knowledge in the form of Prolog or Datalog programs as in
inductive logic programming systems. The kLog framework can be applied to
tackle the same range of tasks that has made statistical relational learning so
popular, including classification, regression, multitask learning, and
collective classification. We also report about empirical comparisons, showing
that kLog can be either more accurate, or much faster at the same level of
accuracy, than Tilde and Alchemy. kLog is GPLv3 licensed and is available at
http://klog.dinfo.unifi.it along with tutorials
Edit Distance: Sketching, Streaming and Document Exchange
We show that in the document exchange problem, where Alice holds and Bob holds , Alice can send Bob a message of
size bits such that Bob can recover using the
message and his input if the edit distance between and is no more
than , and output "error" otherwise. Both the encoding and decoding can be
done in time . This result significantly
improves the previous communication bounds under polynomial encoding/decoding
time. We also show that in the referee model, where Alice and Bob hold and
respectively, they can compute sketches of and of sizes
bits (the encoding), and send to the referee, who can
then compute the edit distance between and together with all the edit
operations if the edit distance is no more than , and output "error"
otherwise (the decoding). To the best of our knowledge, this is the first
result for sketching edit distance using bits.
Moreover, the encoding phase of our sketching algorithm can be performed by
scanning the input string in one pass. Thus our sketching algorithm also
implies the first streaming algorithm for computing edit distance and all the
edits exactly using bits of space.Comment: Full version of an article to be presented at the 57th Annual IEEE
Symposium on Foundations of Computer Science (FOCS 2016
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