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.
Many studies have used ontology information for searching the semantic web @cite_29 , @cite_14 , @cite_28 . Studies by Aleman-Meza @cite_36 , Cakmak and " O zsoyoglu @cite_15 as well as Wu, Yang and Yan @cite_6 used ontology information to find frequent patterns in graphs. Wu, Yang and Yan @cite_6 proposed an improved subgraph querying technique by ontology information. They revised subgraph isomorphism by mapping a query to semantically related subgraphs in terms of a given ontology graph. Our work generates substitute queries for the user given keyword query by extracting the semantically related keywords from the ontological knowledge base and thereafter, produce semantically related results to the user query instead of returning an empty result set to the user.
@cite_14 @cite_28 @cite_36 @cite_29 @cite_6 @cite_15
2060072498 1993448295 2078652018 2007269563
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. A query-relevant snippet for ontology search is useful for deciding if an ontology fits users' needs. In this paper, we illustrate a good snippet in a keyword-based ontology search engine should be with term-association view and compact, and propose an approach to generate it. To obtain term-association view snippets, a model of term association graph for ontology is proposed, and a concept of maximal r-radius subgraph is introduced to decompose the term association graph into connected subgraphs, which preserve close relations between terms. To achieve compactness, in a query-relevant maximal r-radius subgraph, a connected subgraph thereof with a small graph weight is extracted as a sub-snippet. Finally, a greedy method is used to select sub-snippets to form a snippet in consideration of query relevance and compactness without violating the length constraint. An empirical study on our implementation shows that our approach is feasible. An evaluation on effectiveness shows that the term-association view snippet is favored by users, and the compactness helps reading and judgment. On the Semantic Web, the types of resources and the semantic relationships between resources are defined in an ontology. By using that information, the accuracy of information retrieval can be improved. In this paper, we present effective ranking and search techniques considering the semantic relationships in an ontology. Our technique retrieves top-k resources which are the most relevant to query keywords through the semantic relationships. To do this, we propose a weighting measure for the semantic relationship. Based on this measure, we propose a novel ranking method which considers the number of meaningful semantic relationships between a resource and keywords as well as the coverage and discriminating power of keywords. In order to improve the efficiency of the search, we prune the unnecessary search space using the length and weight thresholds of the semantic relationship path. In addition, we exploit Threshold Algorithm based on an extended inverted index to answer top-k results efficiently. The experimental results using real data sets demonstrate that our retrieval method using the semantic information generates accurate results efficiently compared to the traditional methods. In this paper we present a novel index data structure tailored towards semantic full-text search. Semantic full-text search, as we call it, deeply integrates keyword-based full-text search with structured search in ontologies. Queries are SPARQL-like, with additional relations for specifying word-entity co-occurrences. In order to build such queries the user needs to be guided. We believe that incremental query construction with context-sensitive suggestions in every step serves that purpose well. Our index has to answer queries and provide such suggestions in real time. We achieve this through a novel kind of posting lists and query processing, avoiding very long (intermediate) result lists and expensive (non-local) operations on these lists. In an evaluation of 8000 queries on the full English Wikipedia (40 GB XML dump) and the YAGO ontology (26.6 million facts), we achieve average query and suggestion times of around 150ms.