On Implicational Dependency Families Possessing Finite Armstrong Relations

Let X ≠ 0 be a finite collection of nonempty relations over the relation scheme R(A1, A2 , ... , A,.); then the closure of X under embedding and direct product (up to isomorphism) is a finitely generated Implicational Dependency family (ID-family) generated by X. In this paper, we show that the class of finitely generated ID-families is identical to the class of those ID-families which possess a finite Armstrong relation

Final report of Task #5: Current document index system for document retrieval investigation

In Part I of this report, we describe the work completed during the last fiscal year (October 1, 2002 thru September 30, 2003). The single biggest challenge this past year has been to develop and deliver a new software technology to classify Homeland Security Sensitive documents with high precision. Not only was a satisfactory system developed, an operational version was delivered to CACI in April 2003. The delivered system is called the Homeland Security Classifier (HSC). In Part II we give an overview of the projects ISRI has completed during the first four years of this cooperative agreement (October 1, 1998 thru September 30, 2002). Each of the deliverables associated with these projects has been thoroughly described in previous reports

On Purely Exponential Logic Queries

The recursive nature of logic programs has long been the subject of optimization techniques [2, 8]. Recently, the database community has taken interest in extending the expressive power of relational algebra by augmenting it with function-free Horn style logic queries. This extension has led to various optimization techniques [2, 6, 8]. It seems, almost invariably, these techniques are most efficient in the processing of linear recursive queries. Moreover, as the equivalence of nonlinear rules to linear rules in general is undecidable [3, 9], the best one can hope is to rewrite some nonlinear rules as linear rules. For this reason, there is a genuine interest in identifying those classes of non-linear recursive queries which can be rewritten as linear queries. Among these classes are binary chained purely exponential queries [5] and doubly recursive queries [11]

Identification of Sensitive Unclassified Information

Sensitive Unclassified information is defined as any unclassified information that may cause adverse consequences against the government facilities. In this chapter, we explore the use of categorization techniques and information extraction to discover this kind of information in scanned documents. We show here that the combined use of a K-Dependence Bayesian categorization engine and a semi-automated review application reduce by nearly 95% the number of man hours required to redact sensitive unclassified information. We also discuss and provide statistics on how OCR errors can affect the information extraction tasks

The Building of a Large Government Digital Library

This paper reports on the methodology of building an electronic digital library. In particular, it addresses the issues involving OCR errors and conversion. It also reports on data mining applications required for filtering sensitive information

Some Characterizations of Finitely Specifiable Implicational Dependency Families

Let r be a relation for the relation scheme R(A1,A2,…,An); then we define Dom(r) to be Domr(A1)×Domr(A2)×…×Domr(An), where Domr(Ai) for each i is the set of all ith coordinates of tuples of r. A relation s is said to be a substructure of the relation r if and only if Dom(s)∩r = s. The following theorems analogous to Tarski-Fraisse-Vaught\u27s characterizations of universal classes are proved: (i) An implicational dependency family (ID-family) F over the relation scheme R is finitely specifiable if and only if there exists a finite number of relations r1,r2,…,rm for R such that r ∈ F if and only if no ri is isomorphic to a substructure of r. (ii) F is finitely specifiable if and only if there exists a natural number k such that r ∈ F whenever F contains all substructures of r with at most k elements. We shall use these characterizations to obtain a new proof for Hull\u27s (1984) characterization of finitely specifiable ID-families

Model Completeness and Direct Power

The concept of model completeness for a first order theory T was first formulated by A. ROBINSON [6], who proved the basic results about it and gave several examples of model complete theories in 1956. Two of the most prominent examples of model complete theories are those of the theory of algebraically closed fields and the theory of real closed fields. It was thought that it was likely that direct powers preserve model completeness: however, there are some simple counter examples to this conjecture (see [3]). Because of the sensitivity of model completeness of a theory to a given language, it is natural to ask whether one can begin with a model complete theory T in a language L and obtain an extension L* of L in such a way that T x T be model complete in L*. One can always form a new language L*, obtained by adding a new relation symbol for each formula of the original language L, and define for any theory T in L a new theory T* in L* which extends T and is model complete with respect to L* (since it admits elimination of quantifiers). Also, in the entire language of generalized product of FEFERMAN-VAUCHT, one authomatically as a theorem gets elimination of quantifiers, and hence, model completeness. We are only aware of these two solutions to this problem in the literature, both being infinite. In this paper, we will effectively construct a finite extension L* of L in which T x T is model complete whenever T is model complete in L. In addition, we will give some results concerning w0-categoricity, w1-categoricity, and finite axiomatizability of T x T in L*. We also provide an easy proof for preservation of w-categoricity under direct powers, and give some general results regarding model completeness and direct powers

Acronym Expansion via Hidden Markov Models

In this paper, we report on design and implementation of a Hidden Markov Model (HMM) to extract acronyms and their expansions. We also report on the training of this HMM with Maximum Likelihood Estimation (MLE) algorithm using a set of examples. Finally, we report on our testing using standard recall and precision. The HMM achieves a recall and precision of 98% and 92% respectively