Better performing keywords cover search

This report documents the program and the outcome of Dagstuhl Seminar 13441 Evaluation Methodologies in Information Retrieval, which brought together 42 participants from 11 countries. The seminar was motivated by the fact that today's information retrieval (IR) applications can hardly be evaluated based on the classic test collection paradigm, thus there is a need for new evaluation approaches. The event started with five introductory talks on evaluation frameworks, user modeling for evaluation, evaluation criteria, measures, evaluation methodology, and new trends in IR evaluation. The seminar participants then formed working groups addressing specific aspects of IR evaluation, such as reliability and validity, task-based IR, learning as search outcome, searching for fun, IR and social media, graph search, domain-specific IR, interaction measures and models, and searcher-aware information access systems.

The proliferation of geo-textual data gives prominence to spatial keyword search. The basic top-k spatial keyword query, returns k geo-textual objects that rank the highest according to their textual relevance and spatial proximity to query keywords and a query location. We define, study, and provide means of computing the reverse top-k keyword-based location query. This new type of query takes a set of keywords, a query object q, and a number k as arguments, and it returns a spatial region such that any top-k spatial keyword query with the query keywords and a location in this region would contain object q in its result. This query targets applications in market analysis, geographical planning, and location optimization, and it may support applications related to safe zones and influence zones that are used widely in location-based services. We show that computing an exact query result requires evaluating and merging a set of weighted Voronoi cells, which is expensive. We therefore devise effective algorithms that approximate result regions with quality guarantees. We develop novel pruning techniques on top of an index, and we offer a series of optimization techniques that aim to further accelerate query processing. Empirical studies suggest that the proposed query processing is efficient and scalable.

More and more sensitive information of big data are being now stored in the cloud for it offers quality service and convenient management. But the Cloud Server cannot be fully trusted because it may leak information which raises security and privacy concerns of the stored data. Therefore, sensitive data has to be encrypted before shifted to the cloud. It is a great convenience for authorized users to directly retrieve the encrypted data stored in the cloud by keyword search. As we all known, returning all the matching documents is a waste of network bandwidth. Although there are some schemes that support sorting fuzzy multi-keyword search, and they are generally based on edit distance to looking for fuzzy alternatives set, which leads to significantly larger index file size and higher search complexity. Then we rank the search results according to the matching score between keywords and documents. In this paper, we give a novel multi-keyword fuzzy and sortable search scheme. We use n-gram technology to realize fuzzy search and give a novel ranking search scheme based on the fuzzy multi- keyword search. For the matching files may be more than one, there are differences between the original query keywords and the fuzzy keywords. When ranking files, we will not only consider the relevance score between the index keywords and the documents, but also the similarity between the query and keywords. Thus we calculate the comprehensive matching score of the file to the set of query keywords, which is more efficient and accurate.

Thematic ranking of object summaries for keyword search

There is a growing need to explore attributed graphs such as social networks, expert networks, and biological networks. A well-known mechanism for non-technical users to explore such graphs is keyword search, which receives a set of query keywords and returns a connected subgraph that contains the keywords. However, existing approaches, such as methods based on shortest paths between nodes containing the query keywords, may generate weakly-connected answers as they ignore the structure of the whole graph. To address this issue, we formulate and solve the robust keyword search problem for attributed graphs to find strongly-connected answers. We prove that the problem is NP-hard and we propose a solution based on a random walk with restart (RWR). To improve the efficiency and scalability of RWR, we use Monte Carlo approximation and we also propose a distributed version, which we implement in Apache Spark. Finally, we provide experimental evidence of the efficiency and effectiveness of our approach on real-world graphs.

Top-k-size keyword search on tree structured data

Web objects, often associated with descriptive text documents, are increasingly being geo-tagged. A spatial keyword top-k query retrieves the best k such objects according to a scoring function that considers both spatial distance and textual similarity. However, it is in some cases difficult for users to identify the exact keywords that describe their query intent. After a user issues an initial query and gets back the result, the user may find that some expected objects are missing and may wonder why. Answering the resulting why-not questions can aid users in retrieving better results. However, no existing techniques are able to answer why-not questions by adapting the query keywords. We propose techniques capable of adapting an initial set of query keywords so that expected, but missing, objects enter the result along with other relevant objects. We develop a basic algorithm with a set of optimizations that sequentially examines a sequence of candidate keyword sets. In addition, we present an index-based bound-and-prune algorithm that is able to determine the best sample out of a set of candidates in just one pass of index traversal, thus speeding up the query processing. We also extend the proposed algorithms to handle multiple missing objects. Extensive experimental results offer insight into the efficiency of the proposed techniques in terms of running time and I/O cost.

Keyword based queries are inherently ambiguous such that given a set of keywords the database search engine has only an uncertain guess about the user's informational need represented by the query. Possibly high complexity of the data makes providing intelligent search results effectively extremely challenging. Databases enable users to precisely express their informational needs using structured queries. However, database query construction is a laborious and error-prone process, which cannot be performed well by most end users. Keyword search alleviates the usability problem at the price of query expressiveness. As keyword search algorithms do not differentiate between the possible informational needs represented by a keyword query, users may not receive adequate results. This paper presents Extended Incremental Query Processing - a novel approach to bridge the gap between usability of keyword search and expressiveness of database queries. Extended Incremental Query Processing enables a user to start with an arbitrary keyword query and incrementally refine it into a structured query through an interactive interface. The enabling techniques of Extended Incremental Query Processing include: 1) A probabilistic framework for incremental query construction; 2) A probabilistic model to assess the possible informational needs represented by a keyword query; 3) An algorithm to obtain the optimal query construction process. This paper presents the detailed design of Extended Incremental Query Processing, and demonstrates its effectiveness and scalability through experiments over real-world data and a user study. Extracting information from semi structured documents is a very hard task. Documents are often so large that the data set returned as answer to a query may be too big to convey interpretable knowledge. In this, we describe an approach based on Tree-Based Association Rules (TARs): mined rules, which provide approximate, intentional information on both the structure and contents of XML documents. This mined knowledge is later used to provide: a concise idea—the gist—of both the structure and the content of the XML document .quick, approximate answers to queries.

It is meaningful for researchers to find the interested and high quality new papers. We propose the Joint Text and Influence Embedding recommendation model (JTIE) to consider both the paper quality and the content correlation. We train a paper embedding based on its core elements: contents, authors and publication venues. The quality of a new paper is evaluated based on the author authority and the venue reputation. The citation relationships between papers are considered asymmetric such that they can reflect the user’s consideration on the intrinsic influence of a paper. We learn user interests by one’s historical references or a set of query keywords. Finally, papers are recommended according to the relatedness between user interests and paper embeddings. We perform experiments against three real-world datasets. The results show that our model outperforms baseline methods on both the personalized recommendation and the query keywords based retrieval.

In this paper, we investigate the selectivity estimation problem for streaming spatio-textual data, which arises in many social network and geo-location applications. Specifically, given a set of continuously and rapidly arriving spatio-textual objects, each of which is described by a geo-location and a short text, we aim to accurately estimate the cardinality of a spatial keyword query on objects seen so far, where a spatial keyword query consists of a search region and a set of query keywords. To the best of our knowledge, this is the first work to address this important problem. We first extend two existing techniques to solve this problem, and show their limitations. Inspired by two key observations on the "locality" of the correlations among query keywords, we propose a local correlation based method by utilizing an augmented adaptive space partition tree ( A 2 SP -tree for short) to approximately learn a local Bayesian network on-the-fly for a given query and estimate its selectivity. A novel local boosting approach is presented to further enhance the learning accuracy of local Bayesian networks. Our comprehensive experiments on real-life datasets demonstrate the superior performance of the local correlation based algorithm in terms of estimation accuracy compared to other competitors.

As probabilistic data management is becoming one of the main research focuses and keyword search is turning into a more popular query means, it is natural to think how to support keyword queries on probabilistic XML data. With regards to keyword query on deterministic XML documents, ELCA (Exclusive Lowest Common Ancestor) semantics allows more relevant fragments rooted at the ELCAs to appear as results and is more popular compared with other keyword query result semantics (such as SLCAs). In this paper, we investigate how to evaluate ELCA results for keyword queries on probabilistic XML documents. After defining probabilistic ELCA semantics in terms of possible world semantics, we propose an approach to compute ELCA probabilities without generating possible worlds. Then we develop an efficient stack-based algorithm that can find all probabilistic ELCA results and their ELCA probabilities for a given keyword query on a probabilistic XML document. Finally, we experimentally evaluate the proposed ELCA algorithm and compare it with its SLCA counterpart in aspects of result probability, time and space efficiency, and scalability.

Keyword search on XML document has received wide attention. Many search semantics and algorithms have been proposed for XML keyword queries. But the existing approaches fall short in their abilities to support keyword queries over fuzzy XML documents. To overcome this limitation, in this paper, we discuss how to obtain and evaluate top-k smallest lowest common ancestor (SLCA) results of keyword queries on fuzzy XML documents. We define the fuzzy SLCA semantics on the fuzzy XML document, and then propose a novel encoding scheme to denote different types of nodes in fuzzy XML documents. After these, we propose two efficient algorithms to find k SLCA results with highest possibilities for a given keyword query on the fuzzy XML document. First one is an algorithm which can obtain the top-k SLCA results and their possibilities based on the stack technique. The second algorithm can obtain top-k SLCA results of keyword queries based on a set of SLCA’s properties. Finally, we compare and evaluate the performances of the two algorithms.

SAIL: Structure-aware indexing for effective and progressive top- k keyword search over XML documents

The structure of the ever increasing large RDF repositories is too complex to allow non-expert users extract useful information from them. Keyword search is an interesting alternative but in the context of RDF graph data, where query answers are RDF graph fragments, itfaces two major problems: the query quality answer problem and the result computation algorithm scalability problem. In this paper we focus on empowering keyword search on RDF data by exploiting personalized information. We proposean original approach which exploits the structural summary of the RDF graph to generate pattern graphs for the input keyword query. Pattern graphs are structured conjunctive queries and are seen as possible interpretations of the unstructured keyword query. Personalized information is represented as collections of profile graphs, a concept similar to pattern graphs. The ran king of the results is achieved by measuring graph similarity between the user profile graph and the generated pattern graphs. Novel similarity metrics have been introduced which consider intrinsic and extrinsic similarity and take into account both structural and semantic characteristics of the pattern and profile graphs. Effectiveness and efficiency experimental results show that our approach can tackle the two major problems that hinder the widespread use of keyword search on RDF data.

Many online or local data sources provide powerful querying mechanisms but limited ranking capabilities. For instance, PubMed allows users to submit highly expressive Boolean keyword queries, but ranks the query results by date only. However, a user would typically prefer a ranking by relevance, measured by an information retrieval (IR) ranking function. A naive approach would be to submit a disjunctive query with all query keywords, retrieve all the returned matching documents, and then rerank them. Unfortunately, such an operation would be very expensive due to the large number of results returned by disjunctive queries. In this paper, we present algorithms that return the top results for a query, ranked according to an IR-style ranking function, while operating on top of a source with a Boolean query interface with no ranking capabilities (or a ranking capability of no interest to the end user). The algorithms generate a series of conjunctive queries that return only documents that are candidates for being highly ranked according to a relevance metric. Our approach can also be applied to other settings where the ranking is monotonic on a set of factors (query keywords in IR) and the source query interface is a Boolean expression of these factors. Our comprehensive experimental evaluation on the PubMed database and a TREC data set show that we achieve order of magnitude improvement compared to the current baseline approaches.