I. Budak Arpinar

Ontology-driven Web services composition platform

Discovering and assembling individual Web services into more complex yet new and more useful Web processes is an important challenge. In this paper, we present techniques for (semi) automatically composing Web services into Web processes by using their ontological descriptions and relationships to other services. In interface-matching automatic composition technique, the possible compositions are obtained by checking semantic similarities between interfaces of individual services. Then these compositions are ranked and an optimum composition is selected. In Human-Assisted composition the user selects a service from a ranked list at certain stages. We also address automatic compositions in a peer-to-peer network.

Semantic Association Identification and Knowledge Discovery for National Security Applications

Public and private organizations have access to a vast amount of internal, deep Web and open Web information. Transforming this heterogeneous and distributed information into actionable and insightful information is the key to the emerging new classes of business intelligence and national security applications. Although the role of semantics in search and integration has been often talked about, in this paper we discuss semantic approaches to support analytics on vast amounts of heterogeneous data. In particular, we bring together novel academic research and commercialized Semantic Web technology. The academic research related to semantic association identification is built upon commercial Semantic Web technology for semantic metadata extraction. A prototypical demonstration of this research and technology is presented in the context of an aviation security application of significance to national security.

Geospatial Ontology Development and Semantic Analytics

Geospatial ontology development and semantic knowledge discovery addresses the need for modeling, analyzing and visualizing multimodal information, and is unique in offering integrated analytics that encompasses spatial, temporal and thematic dimensions of information and knowledge. The comprehensive ability to provide integrated analysis from multiple forms of information and use of explicit knowledge make this approach unique. This also involves specification of spatiotemporal thematic ontologies and populating such ontologies with high quality knowledge. Such ontologies form the basis for defining the meaning of important relations terms, such as near or surrounded by, and enable computation of spatiotemporal thematic proximity measures we define. SWETO (Semantic Web Technology Evaluation Ontology) and geospatial extension SWETO-GS are examples of these ontologies. The Geospatial Semantics Analytics (GSA) framework incorporates: (1) the ability to automatically and semi-automatically tract metadata from syntactically (including unstructured, semi-structured and structured data) and semantically heterogeneous and multimodal data from diverse sources; and (2) analytical processing that exploits these ontologies and associated knowledge bases, with integral support for what we term spatiotemporal thematic proximity (STTP) reasoning and interactive visualization capabilities. This paper discusses the results of our geospatial ontology development efforts as well as some new semantic analytics methods on this ontology such as STTP.

IntelliGEN: A Distributed Workflow System for Discovering Protein-Protein Interactions

A large genomics project involves a significant number of researchers and technicians performing dozens of tasks, either manual (e.g. performing laboratory experiments), computer assisted (e.g. looking for genes in the GENBANK database), or sometimes performed entirely automatically by the computer (e.g. sequence assembly). It has become apparent that managing such projects poses overwhelming problems and may lead to results of lower or even unacceptable quality, or possibly drastically increased project costs. In this paper, we present a design and an initial implementation of a distributed workflow system created to schedule and support activities in a genomics laboratory. The focus of the activities in the laboratory is the discovery of protein-protein interactions of fungi, specifically Neurospora crassa. We present our approach of developing, adapting and applying workflow technology in the genomics lab and illustrate it using one distinct part of a larger workflow to discover protein-protein interactions. Novel features of our system include the ability to monitor the quality and timeliness of the results and if necessary, suggesting and incorporating changes to the selected tasks and their scheduling.

SwetoDblp ontology of Computer Science publications

SwetoDblp is a large populated ontology with a shallow schema yet a large number of real-world instance data. We describe how such ontology is built from an XML source and how it can be maintained. Instead of a one-to-one mapping from XML to RDF, the creation of the ontology emphasizes the addition of relationships and the value of URIs. SwetoDblp is publicly available online. We also summarize research efforts that have used or are using this freely available community resource.

Ontology Evaluation and Ranking using OntoQA

In this paper, we design a context-aware architecture for dealing with intelligent application services in ubiquitous computing. The context-aware architecture is composed of middleware, context server, and client. The middleware component of our context-aware architecture plays an important role in recognizing a moving node with mobility by using a Bluetooth wireless communication technology as well as in executing an appropriate execution module according to the context acquired from a context server. The context server functions as a manager that efficiently stores into the database server context information, such as users current status, physical environment, and resources of a computing system. To verify the usefulness of our architecture, we finally develop a context-aware application system base on it, which provides users with a music playing service in ubiquitous computing environment.Ontologies form the cornerstone of the Semantic Web and are intended to help researchers to analyze and share knowledge, and as more ontologies are being introduced, it is difficult for users to find good ontologies related to their work. Therefore, tools for evaluating and ranking the ontologies are needed. In this paper, we present OntoQA, a tool that evaluates ontologies related to a certain set of terms and then ranks them according a set of metrics that captures different aspects of ontologies. Since there are no global criteria defining how a good ontology should be, OntoQA allows users to tune the ranking towards certain features of ontologies to suit the need of their applications. We also show the effectiveness of OntoQA in ranking ontologies by comparing its results to the ranking of other comparable approaches as well as expert users.

Ontological Evaluation and Validation

In the last few years, the Semantic Web gained scientific acceptance as the means of sharing knowledge in different domains, and the cornerstone of the Semantic Web is ontologies. Currently, users trying to incorporate ontologies in their applications have to rely on their experience to try to find a suitable ontology for their applications. Methods for evaluating ontology quality and validity, ontology characterization and ranking have been developed for that purpose. In this chapter, we introduce several approaches that have been developed to aid in evaluating ontologies. In addition, we present highlights of OntoQA, an ontology evaluation and analysis tool that uses a set of metrics measuring different aspects of the ontology schema and knowledgebase to give an insight to the overall characteristics of the ontology. It is important to keep in mind while reading this chapter that the definition “goodness” or the “validity” of an ontology might vary between different users or different domains.

Discovering and ranking semantic associations over a Large RDF metabase

Information retrieval over semantic metadata has recently received a great amount of interest in both industry and academia. In particular, discovering complex and meaningful relationships among this data is becoming an active research topic. Just as ranking of documents is a critical component of todays search engines, the ranking of relationships will be essential in tomorrows semantic analytics engines. Building upon our recent work on specifying these semantic relationships, which we refer to as Semantic Associations, we demonstrate a system where these associations are discovered among a large semantic metabase represented in RDF. Additionally we employ ranking techniques to provide users with the most interesting and relevant results.

Exception Handling for Conflict Resolution in Cross-Organizational Workflows

Workflow management systems (WfMSs) are being increasingly deployed to deliver e-business transactions across organizational boundaries. To ensure a high service quality in such transactions, exception-handling schemes for conflict resolution are needed. The conflicts primarily arise due to failure of a task in workflow execution because of underlying application, or controlling WfMS component failures or insufficient user input. So far, little progress has been reported in addressing conflict resolution in cross-organizational business processes, though its importance has been recognized. In this paper, we identify the exception handling techniques that support conflict resolution in cross-organizational settings. In particular, we propose a novel, “bundled” exception-handling approach, which supports (1) exception knowledge sharing--sharing exception specifications and handling experiences, (2) coordinated exception handling, and (3) intelligent problem solving--using case based reasoning to reuse exception handing experiences. A prototype of this exception handling mechanism is developed and integrated as a part of the METEOR Workflow Management System. An evaluation of our approach is also presented through some sample workflow applications.

Scalable semantic analytics on social networks for addressing the problem of conflict of interest detection

In this article, we demonstrate the applicability of semantic techniques for detection of Conflict of Interest (COI). We explain the common challenges involved in building scalable Semantic Web applications, in particular those addressing connecting-the-dots problems. We describe in detail the challenges involved in two important aspects on building Semantic Web applications, namely, data acquisition and entity disambiguation (or reference reconciliation). We extend upon our previous work where we integrated the collaborative network of a subset of DBLP researchers with persons in a Friend-of-a-Friend social network (FOAF). Our method finds the connections between people, measures collaboration strength, and includes heuristics that use friendship/affiliation information to provide an estimate of potential COI in a peer-review scenario. Evaluations are presented by measuring what could have been the COI between accepted papers in various conference tracks and their respective program committee members. The experimental results demonstrate that scalability can be achieved by using a dataset of over 3 million entities (all bibliographic data from DBLP and a large collection of FOAF documents).