Users are rarely familiar with the content of a data source they are querying, and therefore cannot avoid using keywords that do not exist in the data source. Traditional systems may respond with an empty result, causing dissatisfaction, while the data source in effect holds semantically related content. In this paper we study this no-but-semantic-match problem on XML keyword search and propose a solution which enables us to present the top-k semantically related results to the user. Our solution involves two steps: (a) extracting semantically related candidate queries from the original query and (b) processing candidate queries and retrieving the top-k semantically related results. Candidate queries are generated by replacement of non-mapped keywords with candidate keywords obtained from an ontological knowledge base. Candidate results are scored using their cohesiveness and their similarity to the original query. Since the number of queries to process can be large, with each result having to be analyzed, we propose pruning techniques to retrieve the top- @math results efficiently. We develop two query processing algorithms based on our pruning techniques. Further, we exploit a property of the candidate queries to propose a technique for processing multiple queries in batch, which improves the performance substantially. Extensive experiments on two real datasets verify the effectiveness and efficiency of the proposed approaches.
Ontologies are being used increasingly in fusion applications, particularly for higher-level fusion, where data must often be understood relationally. This research presents a methodology for utilizing ontologies to enhance the process of graph matching in fusion applications, particularly those associated with soft data (e.g., linguistic data existing in things such as intelligence messages). This paper presents some of the considerations and challenges associated with merging the technologies of ontologies and graph matching, as well as some preliminary research findings that show the effectiveness of using ontologies to enhance the matching capabilities of target graphs (as relational items of interest) against larger data graphs. With the fast development of Semantic Web, more and more RDF and OWL ontologies are created and shared. The effective management, such as storage, inference and query, of these ontologies on databases gains increasing attention. This paper addresses ontology query answering on databases by means of Datalog programs. Via epistemic operators, integrity constraints are introduced, and used for conveying semantic aspects of OWL that are not covered by Datalog-style rule languages. We believe such a processing suitable to capture ontologies in the database flavor, while keeping reasoning tractable. Here, we present a logically equivalent knowledge base whose (sound and complete) inference system appears as a Datalog program. As such, SPARQL query answering on OWL ontologies could be solved in databases. Bi-directional strategies, taking advantage of both forward and backward chaining, are then studied to support this kind of customized Datalog programs, returning exactly answers to the query within our logical framework. Today's search technology delivers impressive results in finding relevant documents for given keywords. However many applications in various fields including genetics, pharmacy, social networks, etc. as well as national security need more than what traditional search can provide. Users need to query a very large knowledge base (KB) using semantic similarity, to discover its relevant subsets. One approach is to use templates that support semantic similarity-based discovery of suspicious activities, that can be exploited to support applications such as money laundering, insider threat and terrorist activities. Such discovery that relies on a semantic similarity notion will tolerate syntactic differences between templates and KB using ontologies. We address the problem of identifying known scenarios using a notion of template-based similarity performed as part of the SemDIS project [1, 3]. This approach is prototyped in a system named TRAKS (Terrorism Related Assessment using Knowledge Similarity) and tested using scenarios involving potential money laundering. The semantic Web aims to represent the contents of Web resources in formalisms that both programs and humans can understand. It relies on rich metadata, called semantic annotations, offering explicit semantic descriptions of Web resources. These annotations are built on ontologies, representing domains through their concepts and the semantic relations between them. Ontologies are the foundations of the semantic Web and the keystone of the Web's automated tasks - searching, merging, sharing, maintaining, customizing, and monitoring. Subgraph querying has been applied in a variety of emerging applications. Traditional subgraph querying based on subgraph isomorphism requires identical label matching, which is often too restrictive to capture the matches that are semantically close to the query graphs. This paper extends subgraph querying to identify semantically related matches by leveraging ontology information. (1) We introduce the ontology-based subgraph querying, which revises subgraph isomorphism by mapping a query to semantically related subgraphs in terms of a given ontology graph. We introduce a metric to measure the similarity of the matches. Based on the metric, we introduce an optimization problem to find top K best matches. (2) We provide a filtering-and-verification framework to identify (top-K) matches for ontology-based subgraph queries. The framework efficiently extracts a small subgraph of the data graph from an ontology index, and further computes the matches by only accessing the extracted subgraph. (3) In addition, we show that the ontology index can be efficiently updated upon the changes to the data graphs, enabling the framework to cope with dynamic data graphs. (4) We experimentally verify the effectiveness and efficiency of our framework using both synthetic and real life graphs, comparing with traditional subgraph querying methods. 