From keywords to semantic queries—Incremental query construction on the semantic web

Constructing semantic queries is a demanding task for human users, as it requires mastering a query language as well as the schema which has been used for storing the data. In this paper, we describe QUICK, a novel system for helping users to construct semantic queries in a given domain. QUICK combines the convenience of keyword search with the expressivity of semantic queries. Users start with a keyword query and then are guided through a process of incremental refinement steps to specify the query intention. We describe the overall design of QUICK, present the core algorithms to enable efficient query construction, and finally demonstrate the effectiveness of our system through an experimental study.     

Approximation and relaxation of semantic web path queries

Given the heterogeneity of complex graph data on the web, such as RDF linked data, it is likely that a user wishing to query such data will lack full knowledge of the structure of the data and of its irregularities. Hence, providing flexible querying capabilities that assist users in formulating their information seeking requirements is highly desirable. In this paper we undertake a detailed theoretical investigation of query approximation, query relaxation, and their combination, for this purpose. The query language we adopt comprises conjunctions of regular path queries, thus encompassing recent extensions to SPARQL to allow for querying paths in graphs using regular expressions (SPARQL 1.1). To this language we add standard notions of query approximation based on edit distance, as well as query relaxation based on RDFS inference rules. We show how both of these notions can be integrated into a single theoretical framework and we provide incremental evaluation algorithms that run in polynomial time in the size of the query and the data, returning answers in ranked order of their ‘distance’ from the original query. We also combine for the first time these two disparate notions into a single ‘flex’ operation that simultaneously applies both approximation and relaxation to a query conjunct, providing even greater flexibility for users, but still retaining polynomial time evaluation complexity and the ability to return query answers in ranked order.     

Mass argumentation and the semantic web

The World Wide Web (WWW) can be seen as an ideal platform for enhancing argumentative expression and communication, due to its ubiquity and openness. Much argumentation takes place on personal blogs and on unstructured or semi-structured discussion forums. Recently, an increasing number of Web 2.0 applications provide specific support for large-scale socially-contributed argumentative content. When compared with traditional methods of Web discourse, these tools enable better visualisation, navigation and analysis of the ‘state of the debate’ by participants and, potentially, by automated tools. In this paper, I outline some potential benefits of Semantic Web techniques in supporting mass-scale, socially-contributed argument tagging. I also present some recent research in this direction.     

An ontology-based retrieval system using semantic indexing

In this paper, we present an ontology-based information extraction and retrieval system and its application in the soccer domain. In general, we deal with three issues in semantic search, namely, usability, scalability and retrieval performance. We propose a keyword-based semantic retrieval approach. The performance of the system is improved considerably using domain-specific information extraction, inferencing and rules. Scalability is achieved by adapting a semantic indexing approach and representing the whole world as small independent models. The system is implemented using the state-of-the-art technologies in Semantic Web and its performance is evaluated against traditional systems as well as the query expansion methods. Furthermore, a detailed evaluation is provided to observe the performance gain due to domain-specific information extraction and inferencing. Finally, we show how we use semantic indexing to solve simple structural ambiguities.     

Finding association rules in semantic web data

The amount of ontologies and semantic annotations available on the Web is constantly growing. This new type of complex and heterogeneous graph-structured data raises new challenges for the data mining community. In this paper, we present a novel method for mining association rules from semantic instance data repositories expressed in RDF/(S) and OWL. We take advantage of the schema-level (i.e. Tbox) knowledge encoded in the ontology to derive appropriate transactions which will later feed traditional association rules algorithms. This process is guided by the analyst requirements, expressed in the form of query patterns. Initial experiments performed on semantic data of a biomedical application show the usefulness and efficiency of the approach.     

An architectural design for effective information retrieval in semantic web

The current web IR system retrieves relevant information only based on the keywords which is inadequate for that vast amount of data. It provides limited capabilities to capture the concepts of the user needs and the relation between the keywords. These limitations lead to the idea of the user conceptual search which includes concepts and meanings. This study deals with the Semantic Based Information Retrieval System for a semantic web search and presented with an improved algorithm to retrieve the information in a more efficient way.This architecture takes as input a list of plain keywords provided by the user and the query is converted into semantic query. This conversion is carried out with the help of the domain concepts of the pre-existing domain ontologies and a third party thesaurus and discover semantic relationship between them in runtime. The relevant information for the semantic query is retrieved and ranked according to the relevancy with the help of an improved algorithm. The performance analysis shows that the proposed system can improve the accuracy and effectiveness for retrieving relevant web documents compared to the existing systems.     

WESONet: Applying semantic web technologies and collaborative tagging to multimedia web information systems

The publication of different media types, like images, audio and video in the World Wide Web is getting more importance each day. However, searching and locating content in multimedia sites is challenging. In this paper, we propose a platform for the development of multimedia web information systems. Our approach is based on the combination between semantic web technologies and collaborative tagging. Producers can add meta-data to multimedia content associating it with different domain-specific ontologies. At the same time, users can tag the content in a collaborative way. The proposed system uses a search engine that combines both kinds of meta-data to locate the desired content. It will also provide browsing capabilities through the ontology concepts and the developed tags.     

Using evolution strategy for cooperative focused crawling on semantic web

Conventional focused crawling systems have difficulties on contextual information retrieval in semantic web environment. In order to deal with these problems, we propose a cooperative crawler platform based on evolution strategy to build semantic structure (i.e., local ontologies) of web spaces. Mainly, multiple crawlers can discover semantic instances (i.e., ontology fragments) from annotated resources in a web space, and a centralized meta-crawler can carry out incremental aggregation of the semantic instances sent by the multiple crawlers. To do this, we exploit similarity-based ontology matching algorithm for computing semantic fitness of a population, i.e., summation of all possible semantic similarities between the semantic instances. As a result, we could efficiently obtain the best mapping condition (i.e., maximizing the semantic fitness) of the estimated semantic structures. We have shown two significant contributions of this paper; (1) reconciling semantic conflicts between multiple crawlers, and (2) adapting to evolving semantic structures of web spaces over time.

Extensible soft semantic web services agent

Web services technology is critical for the success of business integration and other application fields such as bioinformatics. However, there are two challenges facing the practicality of Web services: (a) efficient location of the Web service registries that contain the requested Web services and (b) efficient retrieval of the requested services from these registries with high quality of service (QoS). The main reason for this problem is that current Web services technology is not semantic-oriented. Several proposals have been made to add semantics to Web services to facilitate discovery and composition of relevant Web services. Such proposals are being referred to as semantic Web services (SWS). However, most of these proposals do not address the second problem of retrieval of web services with high QoS. In this paper, we propose a framework called soft semantic Web services agent (soft SWS agent) for providing high QoS Semantic Web services using soft computing methodology. Since different application domains have different requirement for QoS, it is impractical to use classical mathematical modeling methods to evaluate the QoS of semantic Web services. We use fuzzy neural networks with Genetic Algorithms (GA) as our study case. Simulation results show that the soft computing methodology is practicable to handle fuzzy and uncertain QoS metrics effectively.     

Mining various semantic relationships from unstructured user-generated web data

With the emergence of Web 2.0, the amount of user-generated web data has sharply increased. Thus, many studies have proposed techniques to extract wisdom from these user-generated datasets. Some of these works have focused on extracting semantic relationships through the use of search logs or social annotations, but only hierarchical relationships have been considered. The goal of this paper is to detect various semantic relationships (hierarchical and non-hierarchical) between concepts using search logs and social annotations. The experimental results demonstrate that our proposed approach constructs adequate relationships.     

A systematic review on the functional testing of semantic web services

Semantic web services are gaining more attention as an important element of the emerging semantic web. Therefore, testing semantic web services is becoming a key concern as an essential quality assurance measure. The objective of this systematic literature review is to summarize the current state of the art of functional testing of semantic web services by providing answers to a set of research questions. The review follows a predefined procedure that involves automatically searching 5 well-known digital libraries. After applying the selection criteria to the results, a total of 34 studies were identified as relevant. Required information was extracted from the studies and summarized. Our systematic literature review identified some approaches available for deriving test cases from the specifications of semantic web services. However, many of the approaches are either not validated or the validation done lacks credibility. We believe that a substantial amount of work remains to be done to improve the current state of research in the area of testing semantic web services.     

Internet search: Subdivision-based interactive query expansion and the soft semantic web

The expansion of the Internet has made the task of searching a crucial one. Internet users, however, have to make a great effort in order to formulate a search query that returns the required results. Many methods have been devised to assist in this task by helping the users modify their query to give better results. In this paper we propose an interactive method for query expansion. It is based on the observation that documents are often found to contain terms with high information content, which can summarise their subject matter. We present experimental results, which demonstrate that our approach significantly shortens the time required in order to accomplish a certain task by performing web searches.     

Trust estimation of the semantic web using semantic web clustering

Abstract

Development of semantic web and social network is undeniable in the Internet world these days. Widespread nature of semantic web has been very challenging to assess the trust in this field. In recent years, extensive researches have been done to estimate the trust of semantic web. Since trust of semantic web is a multidimensional problem, in this paper, we used parameters of social network authority, the value of pages links authority and semantic authority to assess the trust. Due to the large space of semantic network, we considered the problem scope to the clusters of semantic subnetworks and obtained the trust of each cluster elements as local and calculated the trust of outside resources according to their local trusts and trust of clusters to each other. According to the experimental result, the proposed method shows more than 79% Fscore that is about 11.9% in average more than Eigen, Tidal and centralised trust methods. Mean of error in this proposed method is 12.936, that is 9.75% in average less than Eigen and Tidal trust methods.     

Supporting the semi-automatic semantic annotation of web services: A systematic literature review

ContextSemantically annotating web services is gaining more attention as an important aspect to support the automatic matchmaking and composition of web services. Therefore, the support of well-known and agreed ontologies and tools for the semantical annotation of web services is becoming a key concern to help the diffusion of semantic web services.ObjectiveThe objective of this systematic literature review is to summarize the current state-of-the-art for supporting the semantical annotation of web services by providing answers to a set of research questions.MethodThe review follows a predefined procedure that involves automatically searching well-known digital libraries. As a result, a total of 35 primary studies were identified as relevant. A manual search led to the identification of 9 additional primary studies that were not reported during the automatic search of the digital libraries. Required information was extracted from these 44 studies against the selected research questions and finally reported.ResultsOur systematic literature review identified some approaches available for semantically annotating functional and non-functional aspects of web services. However, many of the approaches are either not validated or the validation done lacks credibility.ConclusionWe believe that a substantial amount of work remains to be done to improve the current state of research in the area of supporting semantic web services.     

Using semantic techniques to access web data

Nowadays, people frequently use different keyword-based web search engines to find the information they need on the web. However, many words are polysemous and, when these words are used to query a search engine, its output usually includes links to web pages referring to their different meanings. Besides, results with different meanings are mixed up, which makes the task of finding the relevant information difficult for the users, especially if the user-intended meanings behind the input keywords are not among the most popular on the web.