Quasi-SLCA based Keyword Query Processing over Probabilistic XML Data

The probabilistic threshold query is one of the most common queries in uncertain databases, where a result satisfying the query must be also with probability meeting the threshold requirement. In this paper, we investigate probabilistic threshold keyword queries (PrTKQ) over XML data, which is not studied before. We first introduce the notion of quasi-SLCA and use it to represent results for a PrTKQ with the consideration of possible world semantics. Then we design a probabilistic inverted (PI) index that can be used to quickly return the qualified answers and filter out the unqualified ones based on our proposed lower/upper bounds. After that, we propose two efficient and comparable algorithms: Baseline Algorithm and PI index-based Algorithm. To accelerate the performance of algorithms, we also utilize probability density function. An empirical study using real and synthetic data sets has verified the effectiveness and the efficiency of our approaches

Semantics and result disambiguation for keyword search on tree data

Keyword search is a popular technique for searching tree-structured data (e.g., XML, JSON) on the web because it frees the user from learning a complex query language and the structure of the data sources. However, the convenience of keyword search comes with drawbacks. The imprecision of the keyword queries usually results in a very large number of results of which only very few are relevant to the query. Multiple previous approaches have tried to address this problem. Some of them exploit structural and semantic properties of the tree data in order to filter out irrelevant results while others use a scoring function to rank the candidate results. These are not easy tasks though and in both cases, relevant results might be missed and the users might spend a significant amount of time searching for their intended result in a plethora of candidates. Another drawback of keyword search on tree data, also due to the incapacity of keyword queries to precisely express the user intent, is that the query answer may contain different types of meaningful results even though the user is interested in only some of them. Both problems of keyword search on tree data are addressed in this dissertation. First, an original approach for answering keyword queries is proposed. This approach extracts structural patterns of the query matches and reasons with them in order to return meaningful results ranked with respect to their relevance to the query. The proposed semantics performs comparisons between patterns of results by using different types of ho-momorphisms between the patterns. These comparisons are used to organize the patterns into a graph of patterns which is leveraged to determine ranking and filtering semantics. The experimental results show that the approach produces query results of higher quality compared to the previous ones. To address the second problem, an original approach for clustering the keyword search results on tree data is introduced. The clustered output allows the user to focus on a subset of the results, and to save time and effort while looking for the relevant results. The approach performs clustering at different levels of granularity to group similar results together effectively. The similarity of the results and result clusters is decided using relations on structural patterns of the results defined based on homomor-phisms between path patterns. An originality of the clustering approach is that the clusters are ranked at different levels of granularity to quickly guide the user to the relevant result patterns. An efficient stack-based algorithm is presented for generating result patterns and constructing the clustering hierarchy. The extensive experimentation with multiple real datasets show that the algorithm is fast and scalable. It also shows that the clustering methodology allows the users to effectively retrieve their intended results, and outperforms a recent state-of-the-art clustering approach. In order to tackle the second problem from a different aspect, diversifying the results of keyword search is addressed. Diversification aims to provide the users with a ranked list of results which balances the relevance and redundancy of the results. Measures for quantifying the relevance and dissimilarity of result patterns are presented and a heuristic for generating a diverse set of results using these metrics is introduced

MA Algorithm to Generate Semantic Web Related Clustered Hierarchy for Keyword Search

Keyword search in XML documents is based on the notion of lowest common ancestors in the labeled trees model of XML documents. It has recently gained a lot of research interest in the database community. In this paper we propose the Modified Active (MA) algorithm which is an improvement over the Active clustering algorithm. The algorithm takes into consideration the entity aspect of the nodes to find the level of the node pertaining to a keyword input by the user. A portion of the Bibliography database is used to experimentally evaluate the Modified Active algorithm. Evaluation results show that MA algorithm generates clusters faster than the Active algorithm and this increases the efficiency of the system

Enhancing the Usability of XML keyword Search

Ph.DDOCTOR OF PHILOSOPH

Interpreting XML keyword query using hidden Markov model

Pretraživanje ključne riječi na XML bazi podataka privuklo je prilično zanimanja. Kako se XML dokumenti vrlo razlikuju od plošnih (flat) dokumenata, učinkovita pretraga XML dokumenata zahtijeva posebno razmatranje. Tradicionalni model vreće riječi (bag-of-words) ne uzima u obzir uloge ključnih riječi i odnos između ključnih riječi pa prema tome nije pogodan za XML pretragu ključne riječi. U ovom radu predstavljamo novi model, nazvan polu-strukturno pretraživanje ključne riječi (SSQ), koji podrazumijeva pretraživanje ključne riječi na različit način; to se pretraživanje sastoji od nekoliko cjelina pretrage i svaka cjelina predstavlja stanje pretrage (query condition). Za interpretaciju pretrage po tom modelu, potrebna su dva koraka. Prvo, predlažemo probabilistički pristup zasnovan na skrivenom Markovljevom modelu za izračunavanje najboljeg uklapanja traženih ključnih riječi u termine baze podataka, tj. elemenata, atributa i vrijednosti. Drugo, generiramo konstrukcije ključnih riječi (SSQs) na osnovu uklapanja. Eksperimentalni rezultati potvrđuju učinkovitost naših metoda.Keyword search on XML database has attracted a lot of research interests. As XML documents are very different from flat documents, effective search of XML documents needs special considerations. Traditional bag-of-words model does not take the roles of keywords and the relationship between keywords into consideration, and thus is not suited for XML keyword search. In this paper, we present a novel model, called semi-structured keyword query (SSQ), which understands a keyword query in a different way: a keyword query is composed of several query units, where each unit represents query condition. To interpret a keyword query under this model, we take two steps. First, we propose a probabilistic approach based on a Hidden Markov Model for computing the best mapping of the query keywords into the database terms, i.e., elements, attributes and values. Second, we generate SSQs based on the mapping. Experimental results verify the effectiveness of our methods

A Survey on Intent-based Diversification for Fuzzy Keyword Search

Keyword search is an interesting phenomenon, it is the process of finding important and relevant information from various data repositories. Structured and semistructured data can precisely be stored. Fully unstructured documents can annotate and be stored in the form of metadata. For the total web search, half of the web search is for information exploration process. In this paper, the earlier works for semantic meaning of keywords based on their context in the specified documents are thoroughly analyzed. In a tree data representation, the nodes are objects and could hold some intention. These nodes act as anchors for a Smallest Lowest Common Ancestor (SLCA) based pruning process. Based on their features, nodes are clustered. The feature is a distinctive attribute, it is the quality, property or traits of something. Automatic text classification algorithms are the modern way for feature extraction. Summarization and segmentation produce n consecutive grams from various forms of documents. The set of items which describe and summarize one important aspect of a query is known as the facet. Instead of exact string matching a fuzzy mapping based on semantic correlation is the new trend, whereas the correlation is quantified by cosine similarity. Once the outlier is detected, nearest neighbors of the selected points are mapped to the same hash code of the intend nodes with high probability. These methods collectively retrieve the relevant data and prune out the unnecessary data, and at the same time create a hash signature for the nearest neighbor search. This survey emphasizes the need for a framework for fuzzy oriented keyword search

Keyword-Based Querying for the Social Semantic Web

Enabling non-experts to publish data on the web is an important achievement of the social web and one of the primary goals of the social semantic web. Making the data easily accessible in turn has received only little attention, which is problematic from the point of view of incentives: users are likely to be less motivated to participate in the creation of content if the use of this content is mostly reserved to experts. Querying in semantic wikis, for example, is typically realized in terms of full text search over the textual content and a web query language such as SPARQL for the annotations. This approach has two shortcomings that limit the extent to which data can be leveraged by users: combined queries over content and annotations are not possible, and users either are restricted to expressing their query intent using simple but vague keyword queries or have to learn a complex web query language. The work presented in this dissertation investigates a more suitable form of querying for semantic wikis that consolidates two seemingly conflicting characteristics of query languages, ease of use and expressiveness. This work was carried out in the context of the semantic wiki KiWi, but the underlying ideas apply more generally to the social semantic and social web. We begin by defining a simple modular conceptual model for the KiWi wiki that enables rich and expressive knowledge representation. A component of this model are structured tags, an annotation formalism that is simple yet flexible and expressive, and aims at bridging the gap between atomic tags and RDF. The viability of the approach is confirmed by a user study, which finds that structured tags are suitable for quickly annotating evolving knowledge and are perceived well by the users. The main contribution of this dissertation is the design and implementation of KWQL, a query language for semantic wikis. KWQL combines keyword search and web querying to enable querying that scales with user experience and information need: basic queries are easy to express; as the search criteria become more complex, more expertise is needed to formulate the corresponding query. A novel aspect of KWQL is that it combines both paradigms in a bottom-up fashion. It treats neither of the two as an extension to the other, but instead integrates both in one framework. The language allows for rich combined queries of full text, metadata, document structure, and informal to formal semantic annotations. KWilt, the KWQL query engine, provides the full expressive power of first-order queries, but at the same time can evaluate basic queries at almost the speed of the underlying search engine. KWQL is accompanied by the visual query language visKWQL, and an editor that displays both the textual and visual form of the current query and reflects changes to either representation in the other. A user study shows that participants quickly learn to construct KWQL and visKWQL queries, even when given only a short introduction. KWQL allows users to sift the wealth of structure and annotations in an information system for relevant data. If relevant data constitutes a substantial fraction of all data, ranking becomes important. To this end, we propose PEST, a novel ranking method that propagates relevance among structurally related or similarly annotated data. Extensive experiments, including a user study on a real life wiki, show that pest improves the quality of the ranking over a range of existing ranking approaches

Towards an effective processing of XML keyword query

Ph.DDOCTOR OF PHILOSOPH