Notion Of Information Content Research Articles

A parametric similarity method: Comparative experiments based on semantically annotated large datasets

We present the parametric method SemSimp aimed at measuring semantic similarity of digital resources. SemSimp is based on the notion of information content, and it leverages a reference ontology and taxonomic reasoning, encompassing different approaches for weighting the concepts of the ontology. In particular, weights can be computed by considering either the available digital resources or the structure of the reference ontology of a given domain. SemSimp is assessed against six representative semantic similarity methods for comparing sets of concepts proposed in the literature, by carrying out an experimentation that includes both a statistical analysis and an expert judgment evaluation. To the purpose of achieving a reliable assessment, we used a real-world large dataset based on the Digital Library of the Association for Computing Machinery (ACM), and a reference ontology derived from the ACM Computing Classification System (ACM-CCS). For each method, we considered two indicators. The first concerns the degree of confidence to identify the similarity among the papers belonging to some special issues selected from the ACM Transactions on Information Systems journal, the second the Pearson correlation with human judgment. The results reveal that one of the configurations of SemSimp outperforms the other assessed methods. An additional experiment performed in the domain of physics shows that, in general, SemSimp provides better results than the other similarity methods.

Semantic relatedness maximisation for word sense disambiguation using a hybrid firefly algorithm

Word sense disambiguation (WSD) refers to determining the right meaning of a vague word using its context. The WSD intermediately consolidates the performance of final tasks to achieve high accuracy. Mainly, a WSD solution improves the accuracy of text summarisation, information retrieval, and machine translation. This study addresses the WSD by assigning a set of senses to a given text, where the maximum semantic relatedness is obtained. This is achieved by proposing a swarm intelligence method, called firefly algorithm (FA) to find the best possible set of senses. Because of the FA is based on a population of solutions, it explores the problem space more than exploiting it. Hence, we hybridise the FA with a one-point search algorithm to improve its exploitation capacity. Practically, this hybridisation aims to maximise the semantic relatedness of an eligible set of senses. In this study, the semantic relatedness is measured by proposing a glosses-overlapping method enriched by the notion of information content. To evaluate the proposed method, we have conducted intensive experiments with comparisons to the related works based on benchmark datasets. The obtained results showed that our method is comparable if not superior to the related works. Thus, the proposed method can be considered as an efficient solver for the WSD task.

Entailment Relations on Distributions

In this paper we give an overview of partial orders on the space of probability distributions that carry a notion of information content and serve as a generalisation of the Bayesian order given in (Coecke and Martin, 2011). We investigate what constraints are necessary in order to get a unique notion of information content. These partial orders can be used to give an ordering on words in vector space models of natural language meaning relating to the contexts in which words are used, which is useful for a notion of entailment and word disambiguation. The construction used also points towards a way to create orderings on the space of density operators which allow a more fine-grained study of entailment. The partial orders in this paper are directed complete and form domains in the sense of domain theory.

Using Logic Programming to Represent Information Content Inclusion Relations

Datalog is a widely recognised language for a certain class of deductive databases. Information Content Inclusion Relation (IIR) formulates a general, information theoretic relationship between: data constructs; between data constructs and real world objects, and between real world objects. IIR is particularly concerned with the information that data carry. It would therefore seem desirable to find out whether IIR and reasoning based on IIR may be implemented by using 'safe' Datalog. We present and prove the following theorem: Any database system that can be modelled using IIR can be represented as a 'safe' Datalog program.} This paper explores the nature of the relationship between the two frameworks for representing domains of application, in order that such representations of IIR by `safe' Datalog can then be used as a tool for the analysis of any site that can be approached with the notion of information content, and in particular any given database, and hence how a database works may be approached in terms of information content of events.

Theoretical Comparison of Grab and Composite Sampling Programs

AbstractA mathematical formula for determining the sampling frequency on the basis of the coefficients of variation of the various parameters of interest of water quality is developed.The uncertainty or the variability of each sampling program is expressed as a function of the sampling design variable in order to construct a performance index which will enable one to select the best sampling program. The notion of information content is used as a basis for the performance index.Under certain circumstances, the information content of a single composite is more than that of a single grab sample. However, the average of a series of grab samples provides more information than the average of a series of composite samples. This happens because the individual information content (entropy) of discrete portions of the composite sample are confounded in the average.It is shown that if the variability of the proportions is greater than the upper limit (provided in Table 2), the information content of the composite, where the volume of each discrete portion is collected in proportion to the rate of flow (or discharge) at the time it is collected, is much smaller than that of the same number of grab samples or composite samples collected at constant volume‐constant time intervals.Time constant, volume proportional‐to‐flow since last sample composites have the same disadvantages.