Query Formula Research Articles

Attribute-Based Expressive and Ranked Keyword Search Over Encrypted Documents in Cloud Computing

Attribute-based keyword search (ABKS) has been proposed to realize fine-grained access control and provide search service in cloud computing. However, most ABKS schemes focus on single or conjunctive keyword search, while the recent Boolean keyword search schemes only support monotonic query formula mainly involving AND, OR and threshold operators. How to support more expressive Boolean query formulas and return the corresponding accurate search results to users have become challenges for practical ABKS over ciphertexts. In this paper, we introduce an attribute-based expressive and ranked keyword search scheme over encrypted documents named ABERKS, which allows authorized users to submit expressive Boolean query formulas involving AND, OR, NOT and threshold operators. ABERKS utilizes a non-monotonic access tree structure to construct the query formula, and further leverages extended Boolean model to rank the search results. Specifically, the users are able to define the weights in the query formula, and get the relevance score of each matched ciphertext if the attributes and keywords are both satisfied. We prove the security of ABERKS against chosen keyword attack under selective ciphertext policy model and against keyword guessing attack, and also conduct extensive experiments to show the efficiency and practicality of ABERKS.

ARQMath

The Answer Retrieval for Questions on Math (ARQMath) evaluation was run for the first time at CLEF 2020. ARQMath is the first Community Question Answering (CQA) shared task for math, retrieving existing answers from Math Stack Exchange (MSE) that can help to answer previously unseen math questions. ARQMath also introduces a new protocol for math formula search, where formulas are evaluated in context using a query formula's associated question post, and posts associated with each retrieved formula. Over 70 topics were annotated for each task by eight undergraduate students supervised by a professor of mathematics. A formula index is provided in three formats: LATEX, Presentation MathML, and Content MathML, avoiding the need for participants to extract these themselves. In addition to detailed relevance judgments, tools are provided to parse MSE data, generate question threads in HTML, and evaluate retrieval results. To make comparisons with participating systems fairer, nDCG' (i.e., nDCG for assessed hits only) is used to compare systems for each task. ARQMath will continue in CLEF 2021, with training data from 2020 and baseline systems for both tasks to reduce barriers to entry for this challenging problem domain.

Efficient Encrypted Data Search With Expressive Queries and Flexible Update

Outsourcing encrypted data to cloud servers that has become a prevalent trend among Internet users to date. There is a long list of advantages on data outsourcing, such as the reduction cost of local data management. How to securely operate encrypted data (remotely), however, is the top-rank concern over data owner. Liang <i>et al.</i> proposed a novel encrypted cloud-based data share and search system without loss of privacy. The system allows users to flexibly search and share encrypted data as well as updating keyword field. However, the search complexity of the system is of extreme inefficiency, <inline-formula><tex-math notation="LaTeX">$O(n d)$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$d$</tex-math></inline-formula> is the total number of system files and <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> is the size of query formula. This article, for the first time, leverages the “oblivious cross search” technology in public key searchable encryption context to reduce the search complexity to <i>only</i> <inline-formula><tex-math notation="LaTeX">$O(nf(w))$</tex-math></inline-formula> , where <inline-formula><tex-math notation="LaTeX">$f(w)$</tex-math></inline-formula> is the number of files embedded with the “least frequent keyword” <inline-formula><tex-math notation="LaTeX">$w$</tex-math></inline-formula> . The new scheme maintains efficient encrypted data share and keyword field update as well. This article further revisits the security models for payload security, keyword privacy and search token privacy (i.e., search pattern privacy) and meanwhile, presents security and efficiency analysis for the new scheme.

Inference control of open relational queries under closed-world semantics based on theorem proving

Relational database systems may serve to evaluate an open query under closed-world semantics. The evaluation returns an explicit output relation complemented with an often implicit statement about the completeness of that relation. The output relation is formed from all those tuples that both fit the format and satisfy the properties expressed in the query. Using first-order logic for specifying formal semantics, the output relation can be seen as a set of (ground) sentences obtained from the query formula by suitable substitutions of free variables by constants. A statement about the completeness of a relation can also explicitly be formalized as a sentence of first-order logic. Inference control for enforcing a confidentiality policy has to inspect and to possibly distort not only the sentences representing the tuples of the output relation but also the completeness sentences. Previously designed and formally verified control procedures employ theorem-proving for such inspections while iteratively considering candidates for those sentences and determining termination conditions, respectively. In this article, we outline an implementation of these control procedures and treat improvements of their runtime efficiency, in particular to overcome shortcomings of the underlying theorem prover, which is repeatedly called with an input comprising a completeness sentence of increasing size. The improvements are obtained by an equivalent rewriting of completeness sentences, exploiting the active domain or introducing new constants for combinations of the original constants, respectively, as well as by optimizing the number of such calls. Besides theoretical complexity considerations, we also present practical evaluations for some examples. These examples include queries that—without control—would return the whole underlying database relations and—with control—can be used for confidentiality-preserving data publishing.

Progression of Decomposed Situation Calculus Theories

In many tasks related to reasoning about consequences of a logical theory, it is desirable to decompose the theory into a number of components with weakly-related or independent signatures. This facilitates reasoning when signature of a query formula belongs to only one of the components. However, an initial theory may be subject to change due to execution of actions affecting features mentioned in the theory. Having once computed a decomposition of a theory, one would like to know whether a decomposition has to be computed again for the theory obtained from taking into account the changes resulting from execution of an action. In the paper, we address this problem in the scope of the situation calculus, where change of an initial theory is related to the well-studied notion of progression. Progression provides a form of forward reasoning; it relies on forgetting values of those features which are subject to change and computing new values for them. We prove new results about properties of decomposition components under forgetting and show when a decomposition can be preserved in progression of an initial theory.

Reasoning with Inconsistent Ontologies

Reasoning with inconsistent ontologies involves using an inconsistency reasoner to get meaningful answers from inconsistent ontologies. This paper introduces an improved inconsistency reasoner, which selects consistent subsets using minimal inconsistent sets and a resolution method, to improve the run-time performance of the reasoning processing. A minimal inconsistent set contains a minimal explanation for the inconsistency of a given ontology. Thus, it can replace the consistency checking operation, which is executed frequently in existing approaches. When selecting subsets of the inconsistent ontology, formulas which can be directly or indirectly resolved with the negation of the query formula are selected because only those formulas affect the consequences of the reasoner. Therefore, the complexity of the reasoning processing is significantly reduced. Tests show that the run-time performance of the inconsistency reasoner is significantly improved.

Research on Information Retrieval System that Supports Keyword Selection based on Generalized Concept and Coverage

It is not easy for a user of an Information Retrieval (IR) system to select an appropriate keyword set to represent his or her specific information need. Therefore, many IR systems can modify keyword sets by estimating the user's particular requirement. Even though such IR systems have better retrieval performance, the complicated estimation process entailed by a large number of keywords makes it difficult for a user to understand how the system behaves. Therefore, we used a thesaurus for query expansion. To select an appropriate keyword set, we proposed two concepts: ``adaptive generalization'', which estimates an appropriate generalization level of the given keywords by using relevant document information, and ``purpose-oriented concept structure modification'', which selects relevant keywords from a predefined synonym set in a thesaurus. Because query expansion based on a thesaurus aims to find new keywords that are complementary to the initial keywords, we proposed to use this method to construct a Boolean query formula to represent the user's information need. We proposed a new IR system called ``appropriate Boolean query reformulation for IR with adaptive generalization'' (ABRIR-AG) to support Boolean query formation. In ABRIR-AG, we reformulated a user-given Boolean query by using a small number of relevant documents. Finally, to evaluate its effectiveness, we evaluated ABRIR-AG by using a large-scale test collection containing WWW documents.

Automatically constructing multi-relationship fuzzy concept networks for document retrieval

Although the knowledge bases incorporated in existing information retrieval systems can enhance retrieval effectiveness, many of them are built by domain experts. It is obvious that the construction of such knowledge bases requires a large amount of human effort. In this paper, an intelligent fuzzy information retrieval system with an automatically constructed knowledge base is presented; the knowledge base is represented by a multi-relationship fuzzy concept network. The multi-relationship fuzzy concept network can describe four kindsof context-independent and context-dependent fuzzy relationships, i.e., "fuzzy positive association" relationship, "fuzzy negative association" relationship, "fuzzygeneralization" relationship, and "fuzzy specialization" relationship between concepts. The users of the fuzzy information retrieval system can submit a fuzzy contextual query which specifies the search context in the query formula. The fuzzy information retrieval system retrieves documents whose contents are relevant to the user's query by some kinds of fuzzy relationships for the specified search context of the user's query. The proposed fuzzy information retrieval method is more intelligent and more flexible than the existing methods due to the fact that it can construct multi-relationship fuzzy concept networks automatically and it can provide contextual search capability to allow the users to specify fuzzy contextual queries in a more intelligent and flexible manner.