Xiping Liu

Returning Clustered Results for Keyword Search on XML Documents

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. In this paper, we address the problem of returning clustered results for keyword search on XML documents. We first propose a novel semantics for answers to an XML keyword query. The core of the semantics is the conceptually related relationship between keyword matches, which is based on the conceptual relationship between nodes in XML trees. Then, we propose a new clustering methodology for XML search results, which clusters results according to the way they match the given query. Two approaches to implement the methodology are discussed. The first approach is a conventional one which does clustering after search results are retrieved; the second one clusters search results actively, which has characteristics of clustering on the fly. The generated clusters are then organized into a cluster hierarchy with different granularities to enable users locate the results of interest easily and precisely. Experimental results demonstrate the meaningfulness of the proposed semantics as well as the efficiency of the proposed methods.

Exploiting structures in keyword queries for effective XML search

Keyword search on XML documents has received considerable research interests recently. Most existing methods put their emphases on the document side, and focus on how to utilize structural properties of XML documents to produce better search results, more effective ranking methods, or more efficient algorithms. However, effective XML search requires a full understanding of not only XML documents but also XML keyword queries, whereas little attention has been paid to the latter. In this paper, we focus on the query side of XML keyword search instead of the document side. We show that keyword queries have structures, and define a concept called keyword query with structure (QWS) to capture query structure. As query structure provides hints about the intent of the query, it can be used to improve the quality of the search results. We exploit some key observations to characterize the structure in a keyword query and show how to refine search results with the assistance of query structure. In order to take the benefits of query structure, we design a query processing approach to produce results given a keyword query. It first derives some QWSs based on heuristics, and computes results of these queries, then expands the results if needed. We implement the proposed methods and conduct comprehensive experiments. Experimental results verify the effectiveness of our methods.

Effective XML content and structure retrieval with relevance ranking

XML documents can be retrieved by means of not only content-only (CO) queries, but also content-and-structure (CAS) queries. Though promising better retrieval precision, CAS queries introduce several new challenges. To address these challenges, we propose a novel approach for XML CAS retrieval. The distinctive feature of the approach is that it adopts a content-oriented point of view. Specifically, the approach first decomposes a CAS query into several fragments, then retrieves results for each query fragment in a content-centric way, and finally scores each answer node. The approach is adaptive to versatile homogeneous and heterogeneous data environments. To assess the relevance of retrieval results to a query fragment, we present a scoring strategy that measures relevance from both content and structure perspectives. In addition, an effective approach is proposed to infer answer nodes based on the CAS query and document structure. An efficient algorithm is also presented for CAS retrieval. Finally, we demonstrate the effectiveness of the proposed methods through comprehensive experimental studies.

Automatically weighting tags in XML collection

In XML retrieval, nodes with different tags play different roles in XML documents and then tags should be reflected in the relevance ranking. An automatic method is proposed in this paper to infer the weights of tags. We first investigate 15 features about tags, and then select five of them based on the correlations between these features and manual tag weights. Using these features, a tag weight assignment model, ATG, is designed. We evaluate the performance of ATG on two real data sets, IEEECS and Wikipedia from two different perspectives. One is to evaluate the quality of the model by measuring the correlation between weights generated by our model and those given by experts. The other is to test the effectiveness of the model in improving retrieval performance. Experimental results show that the tag weights generated by ATG are highly correlated with the manually assigned weights and the ATG model improves retrieval effectiveness significantly.

LINQ: A Framework for Location-Aware Indexing and Query Processing

This paper studies the generic location-aware rank query (GLRQ) over a set of location-aware objects. A GLRQ is composed of a spatial location, a set of keywords, a query predicate, and a ranking function formulated on location, text and other attributes. The result consists of k objects satisfying the predicate ranked according to the ranking function. An example is a query searching for the restaurants that 1) are nearby, 2) offer “American” food, and 3) have high ratings (rating > 4.0). Such queries can not be processed efficiently using existing techniques. In this work, we propose a novel framework called LINQ for efficient processing of GLRQs. To handle the predicate and the attribute-based scoring, we devise a new index structure called synopses tree, which contains the synopses of different subsets of the dataset. The synopses tree enables pruning of search space according to the satisfiability of the predicate. To process the query constraints over the location and keywords, the framework integrates the synopses tree with the spatio-textual index such as IR-tree. The framework therefore is capable of processing the GLRQs efficiently and holistically. We conduct extensive experiments to demonstrate that our solution provides excellent query performance.

JUFE at INEX 2011 Snippet Retrieval Track

Jiangxi University of Finance and Economics (JUFE) submitted 8 runs to the Snippet Retrieval Track at INEX 2011.This report describes an XML snippet retrieval method based on Average Topic Generalization (ATG) model used by JUFE. The basic idea of the ATG is that different element in an XML document plays different role and hence should has distinguishing importance. The ATG model sets a weight automatically to each element according to its tag or path in the XML document. Then, the BM25EW model based on the ATG is proposed to retrieve and rank the relevant elements in an XML document collection. All windows in the most relevant elements are scored and those windows with higher scores are extracted as snippets. By comparing with the runs under different strategies, the performance are discussed and analyzed in detail.

What happened then and there: Top-k spatio-temporal keyword query

Abstract Social media data, e.g. Tweets, are usually geo-tagged, embedded with creation or posting time, and associated with texts. Nowadays, there is an increasing need for querying such spatio-temporal-text data. In this work, we propose a new type of query, top-k spatio-temporal keyword query (k-STKQ in short), over Twitter-like social media data. A k-STKQ takes a location, a timestamp and a set of keywords as argument, and returns top-k objects that are near the location, close to the timestamp, and relevant to the set of keywords. An example of k-STKQ is to search the tweets mentioning “garage sale” recently sent from some places nearby. The massive amount and dynamic nature of social media data are the primary obstacles towards efficient processing of k-STKQs. In order to return the answers efficiently, we propose a novel index, TiST, for the processing of k-STKQs. TiST partitions the incoming data into subsets, and builds an R-tree index on each subset. The timestamps and texts of the objects are also integrated with the R-trees. To further boost the indexing performance, we propose a routing R-tree based R-tree insertion method, which is inspired by the observation that many sets of objects are similar in their locations. For the texts of objects, we propose a hybrid bitmap-based index, which is space-saving and supports relevance computation. The query processing algorithm is also presented based on the TiST index. We conduct extensive experiments to demonstrate that our solution is capable of providing excellent indexing performance and good query performance.

Keyword query with structure: towards semantic scoring of XML search results

Keyword search is an effective paradigm for information discovery and has been introduced recently to query XML documents. Scoring of XML search results is an important issue in XML keyword search. Traditional “bag-of-words” model cannot differentiate the roles of keywords as well as the relationship between keywords, thus is not proper for XML keyword queries. In this paper, we present a new scoring method based on a novel query model, called keyword query with structure (QWS), which is specially designed for XML keyword query. The method is based on a totally new view taken by the QWS model on a keyword query that, a keyword query is a composition of several query units, each representing a query condition. We believe that this method captures the semantic relevance of the search results. The paper first introduces an algorithm reformulating a keyword query to a QWS. Then, a scoring method is presented which measures the relevance of search results according to how many and how well the query conditions are matched. The scoring method is also extended to clusters of search results. Experimental results verify the effectiveness of our methods.

Weighting tags and paths in XML documents according to their topic generalization

Abstract Text-centric (or document-centric) XML document retrieval aims to rank search results according to their relevance to a given query. To do this, most existing methods mainly rely on content terms and often ignore an important factor – the XML tags and paths, which are useful in determining the important contents of a document. In some previous studies, each unique tag/path is assigned a weight based on domain (expert) knowledge. However, such a manual assignment is both inefficient and subjective. In this paper, we propose an automatic method to infer the weights of tags/paths according to the topical relationship between the corresponding elements and the whole documents. The more the corresponding element can generalize the document’s topic, the more the tag/path is considered to be important. We define a model based on Average Topic Generalization (ATG), which integrates several features used in previous studies. We evaluate the performance of the ATG-based model on two real data sets, the IEEECS collection and the Wikipedia collection, from two different perspectives: the correlation between the weights generated by ATG and those set by experts, and the performance of XML retrieval based on ATG. Experimental results show that the tag/path weights generated by ATG are highly correlated with the manually assigned weights, and the ATG model significantly improves XML retrieval effectiveness.