Steven B. Seida

Ontology-driven query expansion methods to facilitate federated queries

In view of the need for a highly distributed and federated architecture, a robust query expansion in a specific domain has great impact on the performance of information retrieval. We aim to determine robust expansion terms using different weighting techniques and finding out the most k-top relevant terms. For this, first, we consider each individual ontology and user query keywords to determine the Basic Expansion Terms (BET) using a number of semantic measures namely Density Measure (DM), Betweenness Measure (BM), and Semantic Similarity Measure (SSM). Second, we specify New Expansion Terms (NET) by Ontology Alignment (OA). Third, we weight expanded terms using a combination of these semantic measures. Fourth, we use a Specific Interval(SI) to determine a set of Robust Expansion Terms (RET). Finally, we compare the result of our novel weighting approach with existing expansion approaches and show the effectiveness of our robust expansion in federated architecture.

R2D: Extracting Relational Structure from RDF Stores

The enthusiastic acceptance of Resource Description Framework (RDF) as a data model has given birth to a new data storage paradigm, namely, the RDF Graph model. The pool of modeling and visualization tools available for RDF stores is limited due to the technology being in its fledgling stage. The work presented in this paper, called R2D (RDF-to-Database) is an effort to make available, to RDF data stores, the abundance of relational tools that are currently in the market. This is done in the form of a JDBC wrapper around RDF Stores that presents a relational view of the stores and their data to the modeling and visualization tools. This paper presents key R2D functionalities and mapping constructs, procedures for every stage of R2D deployment, and sample results in the form of screenshots and performance graphs.

A Relational Wrapper for RDF Reification

The importance of provenance information as a means to trust and validate the authenticity of available data cannot be stressed enough in today’s web-enabled world. The abundance of data now accessible due to the Internet explosion brings with it the related issue of determining how much of it is trustworthy. Provenance information, such as who is responsible for the data or how the data came to be, assists in the process of verifying the authenticity of the data. Semantic web technologies such as Resource Description Framework (RDF) include the ability to record such provenance information through the process of reification. RDF’s popularity has resulted in a demand for modeling and visualization tools. The work presented in this paper, called R2D, attempts to address this demand by innovatively integrating existing, stable technologies such as relational systems with the newer web technologies such as RDF. The work in this paper extends our earlier work by adding support for the RDF concept of reification. Reification enables the association of a level of trust and confidence with RDF triples, thereby enabling the ranking/validation of the authenticity of the triples. Details of the algorithmic enhancements to the various components of R2D that were made to support RDF reification are presented along with performance graphs for queries executed on a database containing crime records data from a police department.

R2D: A FRAMEWORK FOR THE RELATIONAL TRANSFORMATION OF RDF DATA

The astronomical growth of the World Wide Web has resulted in data explosion that in turn has given rise to a need for data representation methodologies and standards to present required information in a rapid and automated manner. The Resource Description Framework (RDF) is one such standard proposed by W3C to address the above need. The ubiquitous acceptance of RDF on the Internet has resulted in the emergence of a new data storage paradigm, the RDF Graph Model, which, as with any data storage methodology, requires data modeling and visualization tools to aid with data management. This paper presents R2D (RDF-to-Database), a relational wrapper for RDF Data Stores, which aims to transform, at run-time, semi-structured RDF data into an equivalent domain-specific relational schema, thereby bridging the gap between RDF and RDBMS concepts and making the abundance of relational tools currently in the market available to the RDF Stores. The primary R2D functionalities and mapping constructs, the high-level system architecture, and deployment flowchart are presented along with algorithms and performance graphs for every stage of the transformation process and screenshots of a relational visualization tool using R2D as evidence of the feasibility of the proposed work.

Relationalizing RDF stores for tools reusability

The emergence of Semantic Web technologies and standards such as Resource Description Framework (RDF) has introduced novel data storage models such as the RDF Graph Model. In this paper, we present a research effort called R2D, which attempts to bridge the gap between RDF and RDBMS concepts by presenting a relational view of RDF data stores. Thus, R2D is essentially a relational wrapper around RDF stores that aims to make the variety of stable relational tools that are currently in the market available to RDF stores without data duplication and synchronization issues.

Relationalization of provenance data in complex RDF reification nodes

The plethora of information available to today’s users due to the Internet phenomenon has brought forth an associated concern, namely, determination of the trustworthiness of information. Provenance information, such as who is responsible for the data or how the data came to be, plays a pivotal role in addressing this concern by providing additional facts that could serve as a basis for establishing the authenticity of information. Awareness of the importance of data provenance has ensured that current technologies include support for the ability to record provenance information. These include Semantic Web technologies such as Resource Description Framework (RDF) that records data provenance through the process of reification. Reification enables the association of a level of trust with RDF triples, thereby enabling the validation of the authenticity of the triples. RDF’s rapid acceptance has created an associated demand for RDF data modeling and visualization tools and our research, called R2D, is aimed at addressing and providing a solution for this demand by leveraging and reusing existing mature technologies. The work presented in this paper extends our earlier work on relationalization of the RDF concept of reification by providing support for complex reifications that include a variety of blank nodes. Algorithmic enhancements that were incorporated into the various R2D components in order to support relationalization of complex reifications are presented along with performance graphs and screenshots of the relational equivalent of a reified RDF store as seen through an open source relational visualization tool.

UPDATE-ENABLED TRIPLIFICATION OF RELATIONAL DATA INTO VIRTUAL RDF STORES

The current buzzword in the Internet community is the Semantic Web initiative proposed by the W3C to yield a Web that is more flexible and self-adapting. However, for the Semantic Web initiative to become a reality, heterogeneous data sources need to be integrated in order to enable access to them in a homogeneous manner. Since a vast majority of data currently resides in relational databases, integrating relational data sources with semantic web technologies is at the top of the list of activities required to realize the semantic web vision. Several efforts exist that publish relational data as Resource Description Framework (RDF) triples; however almost all current work in this arena is uni-directional, presenting data from an underlying relational database into a corresponding virtual RDF store in a read-only manner. An enhancement over previous relational-to-RDF bridging work in the form of bi-directionality support is presented in this paper. The bi-directional bridge proposed here allows RDF data updates specified as triples to be propagated back into the underlying relational database as tuples. Towards this end, we present various algorithms to translate the triples to be updated/inserted/deleted into equivalent relational attributes/tuples whenever possible. Particular emphasis is laid, in this paper, on the translation and update propagation process for triples containing blank nodes and reification nodes, and a platform enhanced with our algorithms, called D2RQ++, through which bi-directional translation can be achieved, is presented.

Intelligent Probabilistic Decision System

In this paper we present an intelligent decision support system based on probabilistic graphical models. We have developed a suite of tools allowing rapid model development, model verification and validation, and deployment. To support the decision maker the reasoning engine ranks decisions, uses value of information to determine the most relevant additional information to collect, and provides explanations to support recommended decisions. These tools have been applied to multiple domains, including Border protection which we present in this paper. I. Introduction ntelligent decision systems are used in a broad range of applications including autonomous systems, robotics, situational awareness, etc. They can operate autonomously or they may provide decision recommendations for a human being, who makes the final selection of an appropriate action. The decisions may be derived using deterministic reasoning, e.g. decision tree or rules, or they may require some form of uncertain reasoning, e.g. fuzzy logic or probabilistic methods. In this paper we will focus on decision support in the presence of uncertainty, specifically by applying graphical probabilistic models. In many applications decision selection is a multistep process. The initial evidence may not be sufficient for the selection of a final decision and additional evidence may need to be acquired. For example, in system failure troubleshooting, failure symptoms are often not sufficient to identify root cause failure and additional tests may have to be performed. In such multi-step process it is desirable to provide recommendations for selection of the next best item of evidence in addition to the decision selection. Another important element of decision support is explanation of the recommendation. A human decision maker may not trust the recommendation unless a convincing explanation is provided. We will present a methodology and tools for rapid development of decision support solutions, which include recommendation of additional evidence and explanation of the selected decisions. There exist several techniques for implementation of decision support in the presence of uncertainty. They include: fuzzy logic, Dempster-Shaffer and probabilistic methods. Our approach is based on graphical probabilistic models called Bayesian networks. 1