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.     

MQSS: multimodal query suggestion and searching for video search

In this paper, we propose a multimodal query suggestion method for video search which can leverage multimodal processing to improve the quality of search results. When users type general or ambiguous textual queries, our system MQSS provides keyword suggestions and representative image examples in an easy-to-use dropdown manner which can help users specify their search intent more precisely and effortlessly. It is a powerful complement to initial queries. After the queries are formulated as multimodal query (i.e., text, image), the new queries are input to individual search models, such as text-based, concept-based and visual example-based search model. Then we apply multimodal fusion method to aggregate the above-mentioned several search results. The effectiveness of MQSS is demonstrated by evaluations over a web video data set.     

Finding smallest k-Compact tree set for keyword queries on graphs using mapreduce

Keyword search is integrated in many applications on account of the convenience to convey users’ query intention. Most existing works in keyword search on graphs modeled the query results as individual minimal connected trees or connected graphs that contain the keywords. We observe that significant overlap may exist among those query results, which would affect the result diversification. Besides, most solutions required accessing graph data and pre-built indexes in memory, which is not suitable to process big dataset. In this paper, we define the smallest k-compact tree set as the keyword query result, where no shared graph node exists between any two compact trees. We then develop a progressive A* based scalable solution using MapReduce to compute the smallest k-compact tree set, where the computation process could be stopped once the generated compact tree set is sufficient to compute the keyword query result. We conduct experiments to show the efficiency of our proposed algorithm.     

Assisting web search using query suggestion based on word similarity measure and query modification patterns

One of the useful tools offered by existing web search engines is query suggestion (QS), which assists users in formulating keyword queries by suggesting keywords that are unfamiliar to users, offering alternative queries that deviate from the original ones, and even correcting spelling errors. The design goal of QS is to enrich the web search experience of users and avoid the frustrating process of choosing controlled keywords to specify their special information needs, which releases their burden on creating web queries. Unfortunately, the algorithms or design methodologies of the QS module developed by Google, the most popular web search engine these days, is not made publicly available, which means that they cannot be duplicated by software developers to build the tool for specifically-design software systems for enterprise search, desktop search, or vertical search, to name a few. Keyword suggested by Yahoo! and Bing, another two well-known web search engines, however, are mostly popular currently-searched words, which might not meet the specific information needs of the users. These problems can be solved by WebQS, our proposed web QS approach, which provides the same mechanism offered by Google, Yahoo!, and Bing to support users in formulating keyword queries that improve the precision and recall of search results. WebQS relies on frequency of occurrence, keyword similarity measures, and modification patterns of queries in user query logs, which capture information on millions of searches conducted by millions of users, to suggest useful queries/query keywords during the user query construction process and achieve the design goal of QS. Experimental results show that WebQS performs as well as Yahoo! and Bing in terms of effectiveness and efficiency and is comparable to Google in terms of query suggestion time.     

Search personalization through query and page topical analysis

Thousands of users issue keyword queries to the Web search engines to find information on a number of topics. Since the users may have diverse backgrounds and may have different expectations for a given query, some search engines try to personalize their results to better match the overall interests of an individual user. This task involves two great challenges. First the search engines need to be able to effectively identify the user interests and build a profile for every individual user. Second, once such a profile is available, the search engines need to rank the results in a way that matches the interests of a given user. In this article, we present our work towards a personalized Web search engine and we discuss how we addressed each of these challenges. Since users are typically not willing to provide information on their personal preferences, for the first challenge, we attempt to determine such preferences by examining the click history of each user. In particular, we leverage a topical ontology for estimating a user’s topic preferences based on her past searches, i.e. previously issued queries and pages visited for those queries. We then explore the semantic similarity between the user’s current query and the query-matching pages, in order to identify the user’s current topic preference. For the second challenge, we have developed a ranking function that uses the learned past and current topic preferences in order to rank the search results to better match the preferences of a given user. Our experimental evaluation on the Google query-stream of human subjects over a period of 1 month shows that user preferences can be learned accurately through the use of our topical ontology and that our ranking function which takes into account the learned user preferences yields significant improvements in the quality of the search results.     

SINA: Semantic interpretation of user queries for question answering on interlinked data

The architectural choices underlying Linked Data have led to a compendium of data sources which contain both duplicated and fragmented information on a large number of domains. One way to enable non-experts users to access this data compendium is to provide keyword search frameworks that can capitalize on the inherent characteristics of Linked Data. Developing such systems is challenging for three main reasons. First, resources across different datasets or even within the same dataset can be homonyms. Second, different datasets employ heterogeneous schemas and each one may only contain a part of the answer for a certain user query. Finally, constructing a federated formal query from keywords across different datasets requires exploiting links between the different datasets on both the schema and instance levels. We present Sina, a scalable keyword search system that can answer user queries by transforming user-supplied keywords or natural-languages queries into conjunctive SPARQL queries over a set of interlinked data sources. Sina uses a hidden Markov model to determine the most suitable resources for a user-supplied query from different datasets. Moreover, our framework is able to construct federated queries by using the disambiguated resources and leveraging the link structure underlying the datasets to query. We evaluate Sina over three different datasets. We can answer 25 queries from the QALD-1 correctly. Moreover, we perform as well as the best question answering system from the QALD-3 competition by answering 32 questions correctly while also being able to answer queries on distributed sources. We study the runtime of SINA in its mono-core and parallel implementations and draw preliminary conclusions on the scalability of keyword search on Linked Data.     

Optimizing top-k selection queries over multimedia repositories

Repositories of multimedia objects having multiple types of attributes (e.g., image, text) are becoming increasingly common. A query on these attributes will typically, request not just a set of objects, as in the traditional relational query model (filtering), but also a grade of match associated with each object, which indicates how well the object matches the selection condition (ranking). Furthermore, unlike in the relational model, users may just want the k top-ranked objects for their selection queries for a relatively small k. In addition to the differences in the query model, another peculiarity of multimedia repositories is that they may allow access to the attributes of each object only through indexes. We investigate how to optimize the processing of top-k selection queries over multimedia repositories. The access characteristics of the repositories and the above query model lead to novel issues in query optimization. In particular, the choice of the indexes used to search the repository strongly influences the cost of processing the filtering condition. We define an execution space that is search-minimal, i.e., the set of indexes searched is minimal. Although the general problem of picking an optimal plan in the search-minimal execution space is NP-hard, we present an efficient algorithm that solves the problem optimally with respect to our cost model and execution space when the predicates in the query are independent. We also show that the problem of optimizing top-k selection queries can be viewed, in many cases, as that of evaluating more traditional selection conditions. Thus, both problems can be viewed together as an extended filtering problem to which techniques of query processing and optimization may be adapted.     

寻找XLCA的XML数据流TOP-K关键字查询算法

XML关键字查询是一个用户比较方便的信息搜索方法,非常适用于用户在不熟悉XML查询语言和底层结构的情况下进行信息查询。现有的XML数据流上关键字查询多采用查找SLCA结果集的方式,为了解决基于SLCA结果集定义的不完备性,引入了基于XLCA的结果集定义,使其查询包含尽可能全的结果。文中对于XML数据流提出利用滑动窗口模型保存数据,基于XLCA的结果集定义,提出了一种TOP-K关键字查询算法,并从理论上证明了此算法的正确性和查询的完备性,分析了其时间复杂性和空间复杂性。     

An efficient approach to suggesting topically related web queries using hidden topic model

Keyword-based Web search is a widely used approach for locating information on the Web. However, Web users usually suffer from the difficulties of organizing and formulating appropriate input queries due to the lack of sufficient domain knowledge, which greatly affects the search performance. An effective tool to meet the information needs of a search engine user is to suggest Web queries that are topically related to their initial inquiry. Accurately computing query-to-query similarity scores is a key to improve the quality of these suggestions. Because of the short lengths of queries, traditional pseudo-relevance or implicit-relevance based approaches expand the expression of the queries for the similarity computation. They explicitly use a search engine as a complementary source and directly extract additional features (such as terms or URLs) from the top-listed or clicked search results. In this paper, we propose a novel approach by utilizing the hidden topic as an expandable feature. This has two steps. In the offline model-learning step, a hidden topic model is trained, and for each candidate query, its posterior distribution over the hidden topic space is determined to re-express the query instead of the lexical expression. In the online query suggestion step, after inferring the topic distribution for an input query in a similar way, we then calculate the similarity between candidate queries and the input query in terms of their corresponding topic distributions; and produce a suggestion list of candidate queries based on the similarity scores. Our experimental results on two real data sets show that the hidden topic based suggestion is much more efficient than the traditional term or URL based approach, and is effective in finding topically related queries for suggestion.     

Efficient keyword search over virtual XML views

Emerging applications such as personalized portals, enterprise search, and web integration systems often require keyword search over semi-structured views. However, traditional information retrieval techniques are likely to be expensive in this context because they rely on the assumption that the set of documents being searched is materialized. In this paper, we present a system architecture and algorithm that can efficiently evaluate keyword search queries over virtual (unmaterialized) XML views. An interesting aspect of our approach is that it exploits indices present on the base data and thereby avoids materializing large parts of the view that are not relevant to the query results. Another feature of the algorithm is that by solely using indices, we can still score the results of queries over the virtual view, and the resulting scores are the same as if the view was materialized. Our performance evaluation using the INEX data set in the Quark (Bhaskar et al. in Quark: an efficient XQuery full-text implementation. In: SIGMOD, 2006) open-source XML database system indicates that the proposed approach is scalable and efficient.     

How do users describe their information need: Query recommendation based on snippet click model

Query recommendation helps users to describe their information needs more clearly so that search engines can return appropriate answers and meet their needs. State-of-the-art researches prove that the use of users’ behavior information helps to improve query recommendation performance. Instead of finding the most similar terms previous users queried, we focus on how to detect users’ actual information need based on their search behaviors. The key idea of this paper is that although the clicked documents are not always relevant to users’ queries, the snippets which lead them to the click most probably meet their information needs. Based on analysis into large-scale practical search behavior log data, two snippet click behavior models are constructed and corresponding query recommendation algorithms are proposed. Experimental results based on two widely-used commercial search engines’ click-through data prove that the proposed algorithms outperform practical recommendation methods of these two search engines. To the best of our knowledge, this is the first time that snippet click models are proposed for query recommendation task.     

XML keyword search with promising result type recommendations

Keyword search enables inexperienced users to easily search XML database with no specific knowledge of complex structured query languages and XML data schemas. Existing work has addressed the problem of selecting data nodes that match keywords and connecting them in a meaningful way, e.g., SLCA and ELCA. However, it is time-consuming and unnecessary to serve all the connected subtrees to the users because in general the users are only interested in part of the relevant results. In this paper, we propose a new keyword search approach which basically utilizes the statistics of underlying XML data to decide the promising result types and then quickly retrieves the corresponding results with the help of selected promising result types. To guarantee the quality of the selected promising result types, we measure the correlations between result types and a keyword query by analyzing the distribution of relevant keywords and their structures within the XML data to be searched. In addition, relevant result types can be efficiently computed without keyword query evaluation and any schema information. To directly return top-k keyword search results that conform to the suggested promising result types, we design two new algorithms to adapt to the structural sensitivity of the keyword nodes over the keyword search results. Lastly, we implement all proposed approaches and present the relevant experimental results to show the effectiveness of our approach.     

ELCA evaluation for keyword search on probabilistic XML data

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.     

Efficient Monte Carlo clustering in subspaces

One of the key difficulties for users in information retrieval is to formulate appropriate queries to submit to the search engine. In this paper, we propose an approach to enrich the user’s queries by additional context. We used the Language Model to build the query context, which is composed of the most similar queries to the query to expand and their top-ranked documents. Then, we applied a query expansion approach based on the query context and the Latent Semantic Analyses method. Using a web test collection, we tested our approach on short and long queries. We varied the number of recommended queries and the number of expansion terms to specify the appropriate parameters for the proposed approach. Experimental results show that the proposed approach improves the effectiveness of the information retrieval system by 19.23 % for short queries and 52.94 % for long queries according to the retrieval results using the original users’ queries.     

Top-<Emphasis Type="Italic">k</Emphasis> coupled keyword recommendation for relational keyword queries

Providing top-k typical relevant keyword queries would benefit the users who cannot formulate appropriate queries to express their imprecise query intentions. By extracting the semantic relationships both between keywords and keyword queries, this paper proposes a new keyword query suggestion approach which can provide typical and semantically related queries to the given query. Firstly, a keyword coupling relationship measure, which considers both intra- and inter-couplings between each pair of keywords, is proposed. Then, the semantic similarity of different keyword queries can be measured by using a semantic matrix, in which the coupling relationships between keywords in queries are reserved. Based on the query semantic similarities, we next propose an approximation algorithm to find the most typical queries from query history by using the probability density estimation method. Lastly, a threshold-based top-k query selection method is proposed to expeditiously evaluate the top-k typical relevant queries. We demonstrate that our keyword coupling relationship and query semantic similarity measures can capture the coupling relationships between keywords and semantic similarities between keyword queries accurately. The efficiency of query typicality analysis and top-k query selection algorithm is also demonstrated.     

XBridge-Mobile: efficient XML keyword search on mobile web data

Mobile computing over intelligent mobile is affecting human’s habits of obtaining information over Internet, especially keyword search. Most of previous keyword search works are mainly focused on traditional web data sources, in which the performance can be improved by adding more computing power and/or building more offline-computed index. However, it is very challenging to apply the traditional keyword search methods to mobile web-based keyword search because mobile computing has many different features, e.g., frequent disconnections, variety of bandwidths, limited power of mobile devices, limited data size to be downloaded, etc.. To address this challenge, in this paper we design an adaptive mobile-based XML keyword search approach, called XBridge-Mobile, that can derive the semantics of a keyword query and generate a set of effective structured patterns by analyzing the given keyword query and the schemas of XML data sources. Each structured pattern represents one of user’s possible search intentions. The patterns will be firstly sent to the mobile client from web server. And then, the mobile client can select some interested patterns to load the results. By doing this, we can reduce the communication cost a lot between web server and mobile client because only the derived patterns and a few results need to be transferred, not all the keyword search results, by which we can save lots of expenses when the downloaded data is priced. In addition, we can economically maintain the frequent structured pattern queries in the mobile device, which can further reduce the expense of downloading data. At last, we analyze and propose a ranking function to measure the quality of keyword search results, design a set of algorithms to optimize mobile keyword search based on the maintained structured patterns, and present the experimental study of XBridge-Mobile with real XML datasets.     

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

Keyword search in XML documents has recently gained a lot of research attention. Given a keyword query, existing approaches first compute the lowest common ancestors (LCAs) or their variants of XML elements that contain the input keywords, and then identify the subtrees rooted at the LCAs as the answer. In this the paper we study how to use the rich structural relationships embedded in XML documents to facilitate the processing of keyword queries. We develop a novel method, called SAIL, to index such structural relationships for efficient XML keyword search. We propose the concept of minimal-cost trees to answer keyword queries and devise structure-aware indices to maintain the structural relationships for efficiently identifying the minimal-cost trees. For effectively and progressively identifying the top-k answers, we develop techniques using link-based relevance ranking and keyword-pair-based ranking. To reduce the index size, we incorporate a numbering scheme, namely schema-aware dewey code, into our structure-aware indices. Experimental results on real data sets show that our method outperforms state-of-the-art approaches significantly, in both answer quality and search efficiency.     

Finding similar queries based on query representation analysis

In order to understand user intents behind their queries, many researchers study similar query finding. Recently, the click graph has shown its utility in describing the relationship between queries and URLs. The previous approaches mainly either generate related terms or find relevant queries based on the co-clicked URLs. However, these approaches may suffer from the complexity of natural language processing and click-through data sparseness. In this paper, we tackle this problem through three query probability distribution representation models: Click Model, Term Model, and Semantic Model. The Click Model extracts credible transition probability from queries to URLs, and describes a query without considering web contents. The Term Model focuses on representing a query via term distribution over its main entities and purposes, which can better capture information needs behind short and ambiguous keyword queries. The Semantic Model learns potential intent distribution of queries to distinguish user intents behind a query. Among the three models, we apply pairwise similarity metrics and graph-based personalized pagerank to find similar queries. Compared to traditional representation models, our representation models are verified to be effective and efficient, especially for long tail queries.     

Personalized Concept-Based Clustering of Search Engine Queries

The exponential growth of information on the Web has introduced new challenges for building effective search engines. A major problem of web search is that search queries are usually short and ambiguous, and thus are insufficient for specifying the precise user needs. To alleviate this problem, some search engines suggest terms that are semantically related to the submitted queries so that users can choose from the suggestions the ones that reflect their information needs. In this paper, we introduce an effective approach that captures the user's conceptual preferences in order to provide personalized query suggestions. We achieve this goal with two new strategies. First, we develop online techniques that extract concepts from the web-snippets of the search result returned from a query and use the concepts to identify related queries for that query. Second, we propose a new two-phase personalized agglomerative clustering algorithm that is able to generate personalized query clusters. To the best of the authors' knowledge, no previous work has addressed personalization for query suggestions. To evaluate the effectiveness of our technique, a Google middleware was developed for collecting clickthrough data to conduct experimental evaluation. Experimental results show that our approach has better precision and recall than the existing query clustering methods.