Query Relaxation Research Articles

Concepts of neighbors and their application to instance-based learning on relational data

Knowledge graphs and other forms of relational data have become a widespread kind of data, and powerful methods to analyze and learn from them are needed. Formal Concept Analysis (FCA) is a mathematical framework for the analysis of symbolic datasets, which has been extended to graphs and relational data, like Graph-FCA. It encompasses various tasks such as pattern mining or machine learning, but its application generally relies on the computation of a concept lattice whose size can be exponential with the number of instances. We propose to follow an instance-based approach where the learning effort is delayed until a new instance comes in, and an inference task is set. This is the approach adopted in k-Nearest Neighbors, and this relies on a distance between instances. We define a conceptual distance based on FCA concepts, and from there the notion of concepts of neighbors, which can be used as a basis for instance-based reasoning. Those definitions are given for both classical FCA and Graph-FCA. We provide efficient algorithms for computing concepts of neighbors, and we demonstrate their inference capabilities by presenting three different applications: query relaxation, knowledge graph completion, and relation extraction.

Investigating the Sketchplan: A Novel Way of Identifying Tactical Behavior in Massive Soccer Datasets.

Coaches and analysts prepare for upcoming matches by identifying common patterns in the positioning and movement of the competing teams in specific situations. Existing approaches in this domain typically rely on manual video analysis and formation discussion using whiteboards; or expert systems that rely on state-of-the-art video and trajectory visualization techniques and advanced user interaction. We bridge the gap between these approaches by contributing a light-weight, simplified interaction and visualization system, which we conceptualized in an iterative design study with the coaching team of a European first league soccer team. Our approach is walk-up usable by all domain stakeholders, and at the same time, can leverage advanced data retrieval and analysis techniques: a virtual magnetic tactic-board. Users place and move digital magnets on a virtual tactic-board, and these interactions get translated to spatio-temporal queries, used to retrieve relevant situations from massive team movement data. Despite such seemingly imprecise query input, our approach is highly usable, supports quick user exploration, and retrieval of relevant results via query relaxation. Appropriate simplified result visualization supports in-depth analyses to explore team behavior, such as formation detection, movement analysis, and what-if analysis. We evaluated our approach with several experts from European first league soccer clubs. The results show that our approach makes the complex analytical processes needed for the identification of tactical behavior directly accessible to domain experts for the first time, demonstrating our support of coaches in preparation for future encounters.

Steering-by-example for Progressive Visual Analytics

Progressive visual analytics allows users to interact with early, partial results of long-running computations on large datasets. In this context, computational steering is often brought up as a means to prioritize the progressive computation. This is meant to focus computational resources on data subspaces of interest so as to ensure their computation is completed before all others. Yet, current approaches to select a region of the view space and then to prioritize its corresponding data subspace either require a one-to-one mapping between view and data space, or they need to establish and maintain computationally costly index structures to trace complex mappings between view and data space. We present steering-by-example, a novel interactive steering approach for progressive visual analytics, which allows prioritizing data subspaces for the progression by generating a relaxed query from a set of selected data items. Our approach works independently of the particular visualization technique and without additional index structures. First benchmark results show that steering-by-example considerably improves Precision and Recall for prioritizing unprocessed data for a selected view region, clearly outperforming random uniform sampling.

Adaptive query relaxation and top‐ k result sorting of fuzzy spatiotemporal data based on XML

With the increasing popularity of Extensible Markup Language (XML) for data representation, there is a lot of interest in searching XML data. Due to the structural heterogeneity of XML, it is daunting for users to formulate exact queries and search for accurate answers. Especially when performing queries on fuzzy spatiotemporal data, the imprecision and uncertainty of information make the amount of query results is small or even empty. In this case, query relaxation is necessary. Existing efforts are mainly made on expanding simple queries. Once there are multiple conditions in the query, the existing algorithms often have poor performance. To address this problem, we first propose an adaptive query relaxation algorithm based on XML, which can handle complex queries efficiently. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results into two sets after query relaxation. Moreover, the algorithm prestores the potential matching objects in the auxiliary structure, which provides support for efficient query. In addition, after query relaxation, we present the result sorting algorithm to address the problem of information overload. Finally, we report experimental results to show that our algorithms are superior to previous approaches.

Adaptive query relaxation and result categorization of fuzzy spatiotemporal data based on XML

With the rapid development of spatiotemporal information and its applications, querying spatiotemporal data has received considerable attention. Meanwhile, the imprecision and uncertainty of information cannot be ignored in many practical applications. Querying fuzzy spatiotemporal data have become one of the most important topics in academia and industry. Although there have been some achievements in querying aspect, the study about fuzzy spatiotemporal query relaxation is still few. In fact, query relaxation is necessary when the amount of query results is small or even empty, especially in the process of querying fuzzy spatiotemporal data. In this paper, we propose an adaptive query relaxation and result categorization approach for fuzzy spatiotemporal data based on XML, which is compatible with spatiotemporal features when spatiotemporal related queries are performed. The approach does not depend on any specific domain or user, it can adaptively relax the initial query requirements, and classify the results by user context preferences and data distribution after query relaxation. In order to locate the spatiotemporal data quickly and show the corresponding fuzziness apparently, we adopted XML to construct the fuzzy spatiotemporal model for query relaxation because XML data can be represented as a tree model which can flexibly arrange the spatiotemporal nodes at the specified position and mark the fuzziness of nodes on the path. In addition, after query relaxation, we present the results categorization algorithm to address the problem of information overload, and then return a navigation tree to the user. Finally, we launch a comprehensive set of experiments to demonstrate the effectiveness and efficiency of our proposed approach. Results of experiments demonstrate that our adaptive query relaxation and result categorization approach based on XML has higher recall and precision in spatiotemporal related query, and can capture the user’s needs and preferences effectively as well.

Relaxing Relationship Queries on Graph Data

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.

Relationship search over knowledge graphs

The recent explosive growth of knowledge graphs (KGs) has enhanced a wide range of Web applications. Among others, the established task of relationship search benefits from KGs since complex relationships that were hidden and distributed in multiple webpages are now explicitly available as subgraphs of an integrated KG. However, new challenges arose as to the efficiency of search over large KGs. In this article, we introduce our recent studies on relationship search, including search algorithms based on novel index structures, methods for relationship clustering to support result exploration, and query relaxation techniques to provide alternative results for failed searches. We also present emerging applications and discuss future research directions.

Relaxing relationship queries on graph data

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.

RESULTING APPROXIMATE QUERIES USING XML DATA

Daunting for users to formulate precise queries and search correct answers. Therefore, approximate matching is introduced to trot out the problem in respondent users’ queries, and this matching might be addressed by 1st reposeful the structure and content of a given question, so longing for answers that match the relaxed queries. Ranking and returning the foremost relevant results of a question became the foremost widespread paradigm in XML query process. However, the present proposals don't adequately take structures into consideration and those them so lack the strength to elegantly mix structures with contents to answer the relaxed queries. To handle this drawback, we have a tendency to 1st propose a classy framework of question relaxations for supporting approximate queries over XML information. The answers underlying this framework aren't compelled to strictly satisfy the given question formulation, instead they will be based on properties inferable from the initial question. we have a tendency to then develop a unique top-k retrieval approach.

Relaxing Relationship Queries on Graph Data

In many domains we have witnessed the need to search a large entity-relation graph for direct and indirect relationships between a set of entities specified in a query. A search result, called a semantic association (SA), is typically a compact (e.g., diameter-constrained) connected subgraph containing all the query entities. For this problem of SA search, efficient algorithms exist but will return empty results if some query entities are distant in the graph. To reduce the occurrence of failing query and provide alternative results, we study the problem of query relaxation in the context of SA search. Simply relaxing the compactness constraint will sacrifice the compactness of an SA, and more importantly, may lead to performance issues and be impracticable. Instead, we focus on removing the smallest number of entities from the original failing query, to form a maximum successful sub-query which minimizes the loss of result quality caused by relaxation. We prove that verifying the success of a sub-query turns into finding an entity (called a certificate) that satisfies a distance-based condition about the query entities. To efficiently find a certificate of the success of a maximum sub-query, we propose a best-first search algorithm that leverages distance-based estimation to effectively prune the search space. We further improve its performance by adding two fine-grained heuristics: one based on degree and the other based on distance. Extensive experiments over popular RDF datasets demonstrate the efficiency of our algorithm, which is more scalable than baselines.

Diversified top-k search with relaxed graph simulation

Graph pattern matching has been widely used in a broad spectrum of real-world applications, and it has been the subject of several investigations, mainly of its importance and use. In this context, different models along with their appropriate algorithms have been proposed. However, in addition to the excessive processing costs, most of the existing models suffer from the failing query problem due to their limitations on finding meaningful matches. Also, in some scenarios, the number of matches may be enormous, making the inspection a daunting task. In this work, we introduce a new model for graph pattern matching, called relaxed graph simulation (RGS), allowing the relaxation of queries to identify more significant matches and to avoid the empty-set answer problem. We then formalize and study the top-k matching problem based on two function classes, relevance and diversity, for ranking the matches with respect to the proposed model. We also formalize and investigate the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Nonetheless, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the RGS model. Our experimental results, on four real datasets, demonstrate both the effectiveness and the efficiency of the proposed approaches.

Query answering over uncertain RDF knowledge bases: explain and obviate unsuccessful query results

Several large uncertain knowledge bases (KBs) are available on the Web where facts are associated with a certainty degree. When querying these uncertain KBs, users seek high-quality results, i.e., results that have a certainty degree greater than a given threshold $$\alpha $$ . However, as they usually have only a partial knowledge of the KB contents, their queries may be failing i.e., they return no result for the desired certainty level. To prevent this frustrating situation, instead of returning an empty set of answers, our approach explains the reasons of the failure with a set of $$\alpha $$ minimal failing subqueries ( $$\alpha $$ MFSs) and computes alternative relaxed queries, called $$\alpha $$ maXimal succeeding subqueries ( $$\alpha $$ XSSs), that are as close as possible to the initial failing query. Moreover, as the user may not always be able to provide an appropriate threshold $$\alpha $$ , we propose three algorithms to compute the $$\alpha $$ MFSs and $$\alpha $$ XSSs for other thresholds, which also constitutes a relevant feedback for the user. Multiple experiments with the WatDiv benchmark show the relevance of our algorithms compared to a baseline method.

SemIndex+: A semantic indexing scheme for structured, unstructured, and partly structured data

While Information Retrieval (IR) systems have gained success in Web-style search engines in the past two decades, nonetheless, the DataBase (DB) paradigm remains prevalent in handling data in enterprise environments and digital libraries, and is gaining even more importance in the Semantic Web with the increasing need to handle partly structured (NoSQL) data. This paper describes SemIndex+, a semantic-aware indexing and querying framework that allows semantic search, result selection, and result ranking of structured (relational DB-style), unstructured (IR-style), and partly structured (NoSQL) data. Various weighting functions and a parallelized search algorithm have been developed for that purpose and are presented here. We provide a general keyword query model allowing the user to choose the results’ semantic coverage and expressiveness based on her needs. Different from alternative solutions involving query relaxation, query refinement, or query disambiguation, our approach incorporates semantics at the most basic data indexing level: providing more opportunities toward speedups and semantic coverage. An extensive experimental evaluation, comparing SemIndex+ with alternative methods, highlights our approach’s flexibility and effectiveness, which in turn impact efficiency (requiring less or more time following the user specified index and query semantic coverages).

Effective Searching of RDF Knowledge Graphs

RDF knowledge graphs are typically searched using triple-pattern queries. Often, triple-pattern queries will return too many or too few results, making it difficult for users to find relevant answers to their information needs. To remedy this, we propose a general framework for effective searching of RDF knowledge graphs. Our framework extends both the searched knowledge graph and triple-pattern queries with keywords to allow users to form a wider range of queries. In addition, it provides result ranking based on statistical machine translation, and performs automatic query relaxation to improve query recall. Finally, we also define a notion of result diversity in the setting of RDF data and provide mechanisms to diversify RDF search results using Maximal Marginal Relevance. We evaluate the effectiveness of our retrieval framework using various carefully-designed user studies on DBpedia, a large and real-world RDF knowledge graph.

Effective searching of RDF knowledge graphs

RDF knowledge graphs are typically searched using triple-pattern queries. Often, triple-pattern queries will return too many or too few results, making it difficult for users to find relevant answers to their information needs. To remedy this, we propose a general framework for effective searching of RDF knowledge graphs. Our framework extends both the searched knowledge graph and triple-pattern queries with keywords to allow users to form a wider range of queries. In addition, it provides result ranking based on statistical machine translation, and performs automatic query relaxation to improve query recall. Finally, we also define a notion of result diversity in the setting of RDF data and provide mechanisms to diversify RDF search results using Maximal Marginal Relevance. We evaluate the effectiveness of our retrieval framework using various carefully-designed user studies on DBpedia, a large and real-world RDF knowledge graph.

Adaptive Query Relaxation and Result Categorization Based on Data Distribution and Query Context

To address the empty and/or many answer problem of Web database query, this paper proposes a general framework to enable automatically query relaxation and result categorization. The framework consists of two processing parts. The first is query relaxation. In this part, each specified attribute is assigned a weight by measuring the query value distribution in the database. The rarely distribution of the query value of the attribute indicates the attribute may important for the user. The original query is then rewritten as a relaxed query by expanding each specified attribute according to its weight. The second part is result categorization. In this step, we first speculate how much the user cares about all attributes (including specified and unspecified attributes) under the query context by using the KL-divergence. Then, the categorizing attribute in each level of the tree can be determined according to its importance for the user. The most important attribute should be the categorizing attribute for the first level of the navigational tree. Lastly, the navigational tree is generated automatically and presented to the user so that the user can easily select the relevant tuples matching his/her needs. Experimental results demonstrated that the query relaxation method can achieve the Precision of 78% and 75% for UsedCarDB (used car dataset) and HouseDB (real estate dataset), respectively, and the result categorization method can also achieve the lowest total and averaged navigational costs than the existing categorization methods.

RDF approximate queries based on semantic similarity

In this paper, we propose a query relaxation approach to handle the problem of empty or too little answers returned from RDF query. We apply RDF entailment to triple patterns in the original query to get more general answers. We propose the notion of semantic similarity degree so that the returned answers are semantically close to the original query. We present how to compute the semantic similarity degree of a relaxed query with respect to the original query. With the semantic similarity degrees, we can choose the relaxed queries that are semantically close to the original query. On this basis, we give the query relaxation algorithm. We verify our approach by experiments. It is shown that our approach of RDF query relaxation based on the semantic similarity degree has some good performances and is feasible.

Exploratory querying of extended knowledge graphs

Knowledge graphs (KGs) are important assets for search, analytics, and recommendations. However, querying a KG to explore entities and discover facts is difficult and tedious, even for users with skills in SPARQL. First, users are not familiar with the structure and labels of entities, classes and relations. Second, KGs are bound to be incomplete, as they capture only major facts about entities and their relationships and miss out on many of the more subtle aspects. We demonstrate TriniT, a system that facilitates exploratory querying of large KGs, by addressing these issues of "vocabulary" mismatch and KG incompleteness. TriniT supports query relaxation rules that are invoked to allow for relevant answers which are not found otherwise. The incompleteness issue is addressed by extending a KG with additional text-style token triples obtained by running Open IE on Web and text sources. The query language, relaxation methods, and answer ranking are extended appropriately. The demo shows automatic query relaxation and has support for interactively adding user-customized relaxations. In both situations, the demo provides answer explanations and offers additional query suggestions.

SPARQLByE

Semantic Web technologies such as RDF and its query language, SPARQL, offer the possibility of opening up the use of public datasets to a great variety of ordinary users. But a key obstacle to the use of open data is the unfamiliarity of users with the structure of data or with SPARQL. To deal with these issues, we introduce a system for querying RDF data by example. At its core is a technique for reverse-engineering SPARQL queries by example. We demonstrate how reverse engineering along with other techniques, such as query relaxation, enables our system, SPARQLByE, to guide users who are unfamiliar with both the dataset and with SPARQL to the desired query and result set.

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.