Graph Search

Tree (SPT) problem. The presented algorithm is an improvement over a previously published work of the authors. The effort is put in to improve the running/execution time of the SPT problem. Introduced improvement is simple and easy to incorporate in to the ...

This article presents an algorithm for information retrieval in an information system that is represented as a graph. We first explain the AI-Strata approach for audiovisual documents annotation, and the notion of characterized potential graphs, used for audiovisual contexts and queries description. The query resolution problem appears as partial subgraph isomorphism between potential graphs and a global graph search problem.

Image segmentation is a broad area, which covers strategies for splitting one input image into its components. This paper aims to present a re-segmentation approach applied to urban imagery, where the interest elements (houses roofs) are considered to have a rectangular shape. Our technique finds and generates rectangular objects, leaving the remaining objects as background. With an over-segmented image we

Efficient graph search is a central issue in many aspects of AI. In most of existing work there is a distinction between the active “searcher”, which both executes the algorithm and holds the memory, and the passive “searched graph”, over which the searcher has no control at all. Large dynamic networks like the Internet, where the nodes are powerful computers and the links have narrow bandwidth and are heavily-loaded, call for a different paradigm, in which most of the burden of computing and memorizing is moved from the ...

A variation of the graph search problem is presented. The assumption is that each edge can be searched by any searcher in a single step and the fugitive can move with unbounded speed. The problem is to search a graph in a single step such that no fugitive can hide between the searchers. A sufficient and necessary condition is presented for a graph G = (V,E) to be single step searchable with ⫫;E⫫ searchers. If ⫫E⫫ searchers are not enough, it can be shown that to determine the minimum number of extra searchers needed is intractable. An O(⫫E⫫) algorithm to single step search an interval graph of ⫫E⫫ edges with the minimum number of extra searchers is also presented.

Quantitative imaging biomarkers are of particular interest in drug development for their potential to accelerate the drug development pipeline. The lack of consensus methods and carefully characterized performance hampers the widespread availability of these quantitative measures. A framework to support collaborative work on quantitative imaging biomarkers would entail advanced statistical techniques, the development of controlled vocabularies, and a service-oriented architecture for processing large image archives. Until now, this framework has not been developed. With the availability of tools for automatic ontology-based annotation of datasets, coupled with image archives, and a means for batch selection and processing of image and clinical data, imaging will go through a similar increase in capability analogous to what advanced genetic profiling techniques have brought to molecular biology. We report on our current progress on developing an informatics infrastructure to store, query, and retrieve imaging biomarker data across a wide range of resources in a semantically meaningful way that facilitates the collaborative development and validation of potential imaging biomarkers by many stakeholders. Specifically, we describe the semantic components of our system, QI-Bench, that are used to specify and support experimental activities for statistical validation in quantitative imaging

We describe a new method for summarizing similarities and differences in a pair of related documents using a graph representation for text. Concepts denoted by words, phrases, and proper names in the document are represented positionally as nodes in the graph along with edges corresponding to semantic relations between items. Given a perspective in terms of which the pair of documents is to be summarized, the algorithm first uses a spreading activation technique to discover, in each document, nodes semantically related to the topic. The activated graphs of each document are then matched to yield a graph corresponding to similarities and differences between the pair, which is rendered in natural language. An evaluation of these techniques has been carried out.

We describe a constrained natural language interface to a large knowledge base, the Foundational Model of Anatomy (FMA). The interface, called GAPP, handles simple or nested questions that can be parsed to the form, subject-relation-object, where subject or object is unknown. With the aid of domain-specific dictionaries the parsed sentence is converted to queries in the StruQL graph-searching query language, then sent to a server we developed, called OQAFMA, that queries the FMA and returns output as XML. Preliminary evaluation shows that GAPP has the potential to be used in the evaluation of the FMA by domain experts in anatomy.