Search

Ontologies could play an important role in assisting users in their search for Web pages. This paper considers the problem of constructing domain ontologies that support users in their Web search efforts and that increase the number of relevant Web pages that are returned. To achieve this goal, this paper suggests combining Deep Web information, which consists of dynamically generated Web pages, which cannot be indexed by the existing automated Web crawlers, with ontologies. Improvements when finding deep Web sites returned by a search engine are assessed based on the framework formulated in this paper. Experimental results suggest that the proposed methods assist users in finding more relevant Web sites.

In this paper, we recognize the shortcomings of the current search engines that do not index and search the deep Web. We present requirements of a deep Web service search engine, that will lead to the query objects in the deep data sources. In order to realize the DWS search engine, we propose semantic metadata and annotation of deep Web services (DWS), a reasoning component to assess the relevance of DWS for searching the deep Web contents, using likelihood of occurrence of data sources that contain the query terms, and present a method of ranking the DWSs. The deep Web service annotation considers not only the service descriptions like any Web services, but also has the frequency distribution, clustering and semantic prediction functions that may guide the search for DWSs.

1RUTGERS University, CIMIC, Newark, New Jersey, U.S.A. (email: adam, vandana, aabhas, atluri@cimic.rutgers.edu); 2City University of New York, Staten Island, New York, U.S.A. (email: chun@mail.csi.cuny.edu); 3Operations and Emergency Management, Port Authority of New York and New Jersey, U.S.A. (email: jfcooper, jpaczkow@panynj.gov); 4SAP Labs, LLC, Palo Alto Research Center, Palo Alto, California, U.S.A. (email: christof.bornhoevd, joachim.schaper@sap.com)

The term "deep Web" refers to Web pages that are not accessible to search engines, e.g., because those Web pages are dynamically generated in response to queries through Web forms or Web services. The existing automated Web crawlers cannot index these pages, thus they are hidden from the Web search engines. Our goal is to properly annotate such deep Web services (i.e. content generation interfaces of hidden Web sources) with semantic indexing by constructing domain-specific ontologies to represent the contents of the deep Web sources. The fully automatic derivation of ontologies from Web sources without human review is to date a challenging research issue. We present a novel approach to automatically building a large, yet domain-specific, ontology by interweaving sub-taxonomies of WordNet with domain-specific information extracted from deep Web service pages. Our algorithms extract domain concepts from deep Web sources which are augmented with concepts and relationships from WordNet to construct ontology fragments. Structurally, these are directed acyclic graphs (DAGs). An iterative process of extracting WordNet concepts and relationships and bridging concept gaps is used to tie together disparate domain concepts and ontology fragments into one ontology. Using eight domains (airfares, jobs, etc.) from a well-known test-bed, our algorithms constructed an ontology of 1692 concepts from deep Web sources and 4434 concepts from WordNet. This ontology is expressed in the OWL format to support semantic Web searches.

With the proliferation of Web technologies, the need to deliver services via the Web has increased tremendously. More and more, customers now demand one-stop service that calls for multiple services crossing organizational boundaries, which are required to be carefully and dynamically composed in a customized manner. Such a composition should not only select the most appropriate available service, but should also adhere to the policies and regulations governing the organizational services. In this paper, we demonstrate how such a composition can be accomplished to form a coherent service flow by using rules and services expressed as a knowledge base and topic ontology. The description of rules with topic concepts allows the system to easily identify the relevant rules in a certain domain and to identify and select appropriate Web services for composition. We consider different types of compositional rules including syntactic, semantic and pragmatic (contextual), which play a major role in the discovery and selection of Web services. We model the knowledge of rules and of the topic ontology using OWL, DAML-S, RuleML and RDF standards.

This paper presents a performance analysis of indexing structures for the satellite image database. The unique features of this database are that the images vary in resolution level and time of capture, but cover the same geographic area. Each image belongs to one of the following types: raw, processed, vegetation, temperature, water, and color. We are particularly interested in comparing the space requirements, insertion and creation time, and query response time. We have implemented a web-based information system for satellite images using Oracle DBMS. We implemented different indexing structures on top of the system for our experiments. We found that the SS-tree gives the best performance for our application.

Privacy refers to controlling the dissemination and use of personal data, including information that is knowingly disclosed, as well as data that are unintentionally revealed as a byproduct of the use of information technologies. This paper argues that the high resolution geospatial images of our earth’s surface, produced from the earth observing satellites, can make a person visually exposed, resulting in a technological invasion of personal privacy. We propose a suitable authorization model for. geospatial data (GSAM) where controlled access can be specified based on the region covered by an image with privilege modes that include view, zoom-in, overlay and identify.

Mashups is novel content created by extracting and combining data from diverse data sources. In this paper, we present a framework of privacy-preserving data sharing and integration for mashup services. The mashup privacy protection system evaluates privacy preferences expressed in a distributed privacy policy network, i.e., personal privacy policies (PPP), data source organisation’s privacy policies (SPP) and mashup organisation’s privacy policies (MPP). The privacy policy is expressed in terms of sensitive data to be protected, mashup provider, mashup-operations, and purposes. These parameters can link to the open semantic resources on the web, such as a friend of a friend (FOAF), service industry classification codes and UN product and service classification codes, providing richer semantic reference and avoiding data ambiguity. The mashup privacy protection system allows the specification of privacy policies and enforces distributed privacy policies in creating a new content by a third party mashup provider.