24,568 research outputs found
Document Type De�nition (DTD) Metrics
In this paper, we present two complexity metrics for the assessment of schema quality written in Document Type De�finition (DTD) language. Both "Entropy (E) metric: E(DTD)" and "Distinct Structured Element Repetition Scale (DSERS) metric: DSERS(DTD)" are intended to measure the structural complexity of schemas in DTD language. These metrics exploit a directed graph representation of schema document and consider the complexity of schema due to its similar structured elements and the occurrences of these
elements. The empirical and theoretical validations of these metrics prove the robustness of the metrics
Entropy as a Measure of Quality of XML Schema Document
In this paper, a metric for the assessment of the structural complexity of eXtensible Markup Language schema
document is formulated. The present metric ‘Schema Entropy is based on entropy concept and intended to measure the
complexity of the schema documents written in W3C XML Schema Language due to diversity in the structures of its elements. The SE is useful in evaluating the efficiency of the design of Schemas. A good design reduces the maintainability efforts. Therefore, our metric provides valuable information about the reliability and maintainability of systems. In this respect, this
metric is believed to be a valuable contribution for improving the quality of XML-based systems. It is demonstrated with examples and validated empirically through actual test cases
An Approach for the Empirical Validation of Software Complexity Measures
Software metrics are widely accepted tools to control and assure software quality. A large number of software metrics with a variety of content can be found in the literature; however most of them are not adopted in industry as they are seen as irrelevant to needs, as they are unsupported, and the major reason behind this is due to improper
empirical validation. This paper tries to identify possible root causes for the improper empirical validation of the software metrics. A practical model for the empirical validation of software metrics is proposed along with root causes. The model is validated by applying it to recently proposed and well known metrics
Entropy and Graph Based Modelling of Document Coherence using Discourse Entities: An Application
We present two novel models of document coherence and their application to
information retrieval (IR). Both models approximate document coherence using
discourse entities, e.g. the subject or object of a sentence. Our first model
views text as a Markov process generating sequences of discourse entities
(entity n-grams); we use the entropy of these entity n-grams to approximate the
rate at which new information appears in text, reasoning that as more new words
appear, the topic increasingly drifts and text coherence decreases. Our second
model extends the work of Guinaudeau & Strube [28] that represents text as a
graph of discourse entities, linked by different relations, such as their
distance or adjacency in text. We use several graph topology metrics to
approximate different aspects of the discourse flow that can indicate
coherence, such as the average clustering or betweenness of discourse entities
in text. Experiments with several instantiations of these models show that: (i)
our models perform on a par with two other well-known models of text coherence
even without any parameter tuning, and (ii) reranking retrieval results
according to their coherence scores gives notable performance gains, confirming
a relation between document coherence and relevance. This work contributes two
novel models of document coherence, the application of which to IR complements
recent work in the integration of document cohesiveness or comprehensibility to
ranking [5, 56]
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
Artificial Sequences and Complexity Measures
In this paper we exploit concepts of information theory to address the
fundamental problem of identifying and defining the most suitable tools to
extract, in a automatic and agnostic way, information from a generic string of
characters. We introduce in particular a class of methods which use in a
crucial way data compression techniques in order to define a measure of
remoteness and distance between pairs of sequences of characters (e.g. texts)
based on their relative information content. We also discuss in detail how
specific features of data compression techniques could be used to introduce the
notion of dictionary of a given sequence and of Artificial Text and we show how
these new tools can be used for information extraction purposes. We point out
the versatility and generality of our method that applies to any kind of
corpora of character strings independently of the type of coding behind them.
We consider as a case study linguistic motivated problems and we present
results for automatic language recognition, authorship attribution and self
consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression
approach to Information Extraction and Classification" by A. Baronchelli and
V. Loreto. 15 pages; 5 figure
- …