Hasan M. Jamil

Automatic ontology matching using application semantics

We propose the use of application semantics to enhance the process of semantic reconciliation. Application semantics involves those elements of business reasoning that affect the way concepts are presented to users: their layout, and so on. In particular, we pursue in this article the notion of precedence, in which temporal constraints determine the order in which concepts are presented to the user. Existing matching algorithms use either syntactic means (such as term matching and domain matching) or model semantic means, the use of structural information that is provided by the specific data model to enhance the matching process. The novelty of our approach lies in proposing a class of matching techniques that takes advantage of ontological structures and application semantics. As an example, the use of precedence to reflect business rules has not been applied elsewhere, to the best of our knowledge. We have tested the process for a variety of web sites in domains such as car rentals and airline reservations, and we share our experiences with precedence and its limitations.

OntoBuilder: fully automatic extraction and consolidation of ontologies from Web sources

Ontologies, formal specifications of domains, have evolved in recent years as a leading tool in representing and interpreting Web data. The OntoBuilder project supports the extraction of ontologies from Web search interfaces, ranging from simple search engine forms to multiple-pages, complex reservation systems. OntoBuilder enables fully-automatic ontology matching. The use of ontologies, as opposed to relational schema or XML, as an underlying data model allows a flexible representation of metadata, that can be tailored to many different types of applications. OntoBuilder was developed using Java, which makes it portable to various platforms and operating system environments. We demonstrate OntoBuilder using an easy-to-follow example of matching car rental ontologies. The system creates ontologies of car rental Web sites on-the-fly, and combine them into a global ontology. The benefits of OntoBuilder in resolving, in an automatic manner, semantic heterogeneity, including synonyms and designer errors are highlighted.

Automatic composite wrapper generation for semi-structured biological data based on table structure identification

Biological data analyses usually require complex manipulations involving tool applications, multiple web site navigation, result selection and filtering, and iteration over the internet. Most biological data are generated from structured databases and by applications and presented to the users embedded within repeated structures, or tables, in HTML documents. In this paper we outline a novel technique for the identification of table structures in HTML documents. This identification technique is then used to automatically generate composite wrappers for applications requiring distributed resources. We demonstrate that our method is robust enough to discover standard as well as non-standard table structures in HTML documents. Thus our technique outperforms contemporary techniques used in systems such as XWrap and AutoWrapper. We discuss our technique in the context of our PickUp system that exploits the theoretical developments presented in this paper and emerges as an elegant automatic wrapper generation system.

BioFlow: A Web-Based Declarative Workflow Language for Life Sciences

Scientific workflows in life sciences are usually complex, and use many online databases, analysis tools, publication repositories and customized computation intensive desktop software in a coherent manner to respond to investigative queries. These investigative queries are generally ad hoc, ill-formed, and often, used only once to test a single hypothesis. In such cases, developing customized workflows becomes a major undertaking, rendering the effort truly expensive, prohibitive and resource intensive. Such high development costs often act as deterrents to many interesting queries and promising on-time scientific discoveries. In this paper, we introduce a new query language that combines workflow features for scientific applications, called BioFlow, that exploits many recent developments in internet communication, databases, wrapper and mediator technologies, ontology, and data integration. BioFlow is a declarative language that abstracts these features to help hide most procedural aspects of mediation, data integration, communication protocols, data extraction and workflow details. We will demonstrate that fairly complex workflows can be effortlessly and declaratively expressed in BioFlow in an ad hoc fashion at minimal costs. We also report a prototype implementation of BioFlow in Windows VB .NET that includes most of its powerful and representative features as proof of feasibility of our proposal.

OntoMatch: A monotonically improving schema matching system for autonomous data integration

Traditional schema matchers use a set of distinct simple matchers and use a composition function to combine the individual scores using an arbitrary order of matcher application leading to non-intuitive scores, produce improper matches, and wasteful and counterproductive computation, especially when no consideration is given to the properties of the individual matchers and the context of the application. In this paper, we propose a new method for schema matching in which wasteful computation is avoided by a prudent, and objective selection and ordering of a subset of useful matchers. This method thus has the potential to improve the matching efficiency and accuracy of many popular ontology generation engines. Such efficiency and quality assurance are imperative in autonomous systems because users rarely have a chance to validate the processing accuracy until the computation is complete. Experimental results to support the claim that such an approach monotonically improves the matching score at successive application of the matchers are also provided.

Top-k Similar Graph Matching Using TraM in Biological Networks

Many emerging database applications entail sophisticated graph-based query manipulation, predominantly evident in large-scale scientific applications. To access the information embedded in graphs, efficient graph matching tools and algorithms have become of prime importance. Although the prohibitively expensive time complexity associated with exact subgraph isomorphism techniques has limited its efficacy in the application domain, approximate yet efficient graph matching techniques have received much attention due to their pragmatic applicability. Since public domain databases are noisy and incomplete in nature, inexact graph matching techniques have proven to be more promising in terms of inferring knowledge from numerous structural data repositories. In this paper, we propose a novel technique called TraM for approximate graph matching that off-loads a significant amount of its processing on to the database making the approach viable for large graphs. Moreover, the vector space embedding of the graphs and efficient filtration of the search space enables computation of approximate graph similarity at a throw-away cost. We annotate nodes of the query graphs by means of their global topological properties and compare them with neighborhood biased segments of the data-graph for proper matches. We have conducted experiments on several real data sets, and have demonstrated the effectiveness and efficiency of the proposed method.

Computing subgraph isomorphic queries using structural unification and minimum graph structures

Graphs and trees play a major role in many applications including social networks, internet management, science, and business. Yet, there remains a serious lack of tools for graph data management, analysis and querying. Systems that rely on non-traditional complex relationships among objects naturally invite a fresh look at data models and query languages suitable for applications that require graphs as first class citizens. In this paper, we propose a new data model for the storage and management of graph objects, and present a heuristic algorithm to efficiently compute subgraph isomorphic queries, and show that the same algorithm can be adapted to perform a wide range of graph queries. We rely upon the introduction of the idea of structural unification, a novel graph representation based on minimum structures, and an indexing mechanism for storing minimum graph structures. We experimentally show that our approach yields significant speed up over the two leading subgraph isomorphism algorithms Ullmann and VFLib.

In silico Analysis of Combinatorial microRNA Activity Reveals Target Genes and Pathways Associated with Breast Cancer Metastasis

Abstract Aberrant microRNA activity has been reported in many diseases, and studies often find numerous microRNAs concurrently dysregulated. Most target genes have binding sites for multiple microRNAs, and mounting evidence indicates that it is important to consider their combinatorial effect on target gene repression. A recent study associated the coincident loss of expression of six microRNAs with metastatic potential in breast cancer. Here, we used a new computational method, miR-AT!, to investigate combinatorial activity among this group of microRNAs. We found that the set of transcripts having multiple target sites for these microRNAs was significantly enriched with genes involved in cellular processes commonly perturbed in metastatic tumors: cell cycle regulation, cytoskeleton organization, and cell adhesion. Network analysis revealed numerous target genes upstream of cyclin D1 and c-Myc, indicating that the collective loss of the six microRNAs may have a focal effect on these two key regulatory nodes. A number of genes previously implicated in cancer metastasis are among the predicted combinatorial targets, including TGFB1, ARPC3, and RANKL. In summary, our analysis reveals extensive combinatorial interactions that have notable implications for their potential role in breast cancer metastasis and in therapeutic development.

The Power of Declarative Languages: A Comparative Exposition of Scientific Workflow Design Using BioFlow and Taverna

Scientific workflow design is usually complex and demands integration of numerous resources. Geographical distribution and semantic heterogeneity of resources add to this complexity. The cost effectiveness of such workflow design thus depends upon the lifespan of the application and its anticipated use. Shorter application lifespan usually entails prohibitive development costs. In this paper, we present an alternative platform for declarative workflow design using BioFlow in such environments. We argue that a declarative workflow design using BioFlow is more efficient and cost effective compared to traditional approaches using systems such as Taverna. To demonstrate the advantages of BioFlow, we compare a canonical micro array data analysis workflow application design approach using both Taverna and BioFlow. We show that BioFlow supports ad hoc and modular application design at a throw away cost, and produces a superior maintainable application that can adapt to changes in the source without significant effort. Finally, we discuss a visual application builder, called VizBuilder, with which end users are able to design workflows without any knowledge of BioFlow.

On-the-Fly Integration and Ad Hoc Querying of Life Sciences Databases Using LifeDB

Data intensive applications in Life Sciences extensively use the hidden web as a platform for information sharing. Access to these heterogeneous hidden web resources is limited through the use of predefined web forms and interactive interfaces that users navigate manually, and assume responsibility for reconciling schema heterogeneity, extracting information and piping, transforming formats and so on in order to implement desired query sequences or scientific work flows. In this paper, we present a new data management system, called LifeDB , in which we offer support for currency without view materialization, and autonomous reconciliation of schema heterogeneity in one single platform through a declarative query language called BioFlow . In our approach, schema heterogeneity is resolved at run time by treating the hidden web resources as a virtual warehouses, and by supporting a set of primitives for data integration on-the-fly, extracting information and piping to other resources, and manipulating data in a way similar to traditional database systems to respond to application demands.