Implicit Queries Research Articles

Sequential Model-Based Diagnosis by Systematic Search (Abstract Reprint)

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions. As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain. To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided. As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with any monotonic knowledge representation language, can interact with a multitude of diagnosis engines and logical reasoners, and allows for a quality optimization of queries based on any of the common criteria in the literature. We extensively study the performance of the novel technique using a benchmark of real-world diagnosis problems. Our findings are that our approach enables the computation of optimal queries with hardly any delay, independently of the size and complexity of the considered benchmark problem. Moreover, it proves to be highly scalable, and it outperforms the state-of-the-art method in the domain of our benchmarks by orders of magnitude in terms of computation time while always returning a qualitatively as good or better query.

Sequential model-based diagnosis by systematic search

Model-based diagnosis aims at identifying the real cause of a system's malfunction based on a formal system model and observations of the system behavior. To discriminate between multiple fault hypotheses (diagnoses), sequential diagnosis approaches iteratively pose queries to an oracle to acquire additional knowledge about the diagnosed system. Depending on the system type, queries can capture, e.g., system tests, probes, measurements, or expert questions.As the determination of optimal queries is NP-hard, state-of-the-art sequential diagnosis methods rely on a myopic one-step-lookahead analysis which has proven to constitute a particularly favorable trade-off between computational efficiency and diagnostic effectivity. Yet, this solves only a part of the problem, as various sources of complexity, such as the reliance on costly reasoning services and large numbers of or not explicitly given query candidates, remain.To deal with such issues, existing approaches often make assumptions about the (i) type of diagnosed system, (ii) formalism to describe the system, (iii) inference engine, (iv) type of query to be of interest, (v) query quality criterion to be adopted, or (vi) diagnosis computation algorithm to be employed. Moreover, they (vii) often cannot deal with large or implicit query spaces or with expressive logics, or (viii) require inputs that cannot always be provided.As a remedy, we propose a novel one-step lookahead query computation technique for sequential diagnosis that overcomes the said issues of existing methods. Our approach (1) is based on a solid theory, (2) involves a systematic search for optimal queries, (3) can operate on implicit and huge query spaces, (4) allows for a two-stage optimization of queries (wrt. their number and cost), (5) is designed to reduce expensive logical inferences to a minimum, and (6) is generally applicable. The latter means that it can deal with any type of diagnosis problem as per Reiter's theory, is applicable with any monotonic knowledge representation language, can interact with a multitude of diagnosis engines and logical reasoners, and allows for a quality optimization of queries based on any of the common criteria in the literature.We extensively study the performance of the novel technique using a benchmark of real-world diagnosis problems. Our findings are that our approach enables the computation of optimal queries with hardly any delay, independently of the size and complexity of the considered benchmark problem. Moreover, it proves to be highly scalable, and it outperforms the state-of-the-art method in the domain of our benchmarks by orders of magnitude in terms of computation time while always returning a qualitatively as good or better query.

An implicit aspect modelling framework for diversity focused query expansion

Diversified Query Expansion aims to present the user with a diverse list of query expansions so as to better communicate their intent to the retrieval system. Current diversified expansion techniques either make use of external knowledge sources to explicitly model the various aspects and their relationships underlying the user query or implicitly model query aspects. However these techniques assume query aspects to be independent of each other. We propose a unified framework that produces diversified query expansions in a completely implicit manner while also considering the relationships between query aspects. In particular, the framework identifies query aspects and their relationships by making use of the semantic properties of context phrases that occur within the top-ranked retrieved documents for the supplied user query, and maps them onto a Mutating Markov Chain model to generate a diverse ordering of query aspects. We test our framework against a set of ambiguous and faceted queries used in the NTCIR-12 IMine-2 Task and through an extensive empirical analysis, we show that our framework consistently outperforms existing implicit diversified query expansion algorithms. The utility of our algorithm truly comes up in the second set of experiments where we generate diversified query expansions for a retrieval engine indexing documents from specific scientific domains. Even in such a niche scenario our algorithm consistently provides robust results and performs better than other implicit approaches.

Search result diversification on attributed networks via nonnegative matrix factorization

Search result diversification is an effective way to tackle query ambiguity and enhance result novelty. In the context of large information networks, diversifying search result is also critical for further design of applications such as link prediction and citation recommendation. In previous work, this problem has mainly been tackled in a way of implicit query intent. To further enhance the performance on attributed networks, we propose a novel search result diversification approach via nonnegative matrix factorization. Our approach encodes latent query intents as well as nodes as representation vectors by a novel nonnegative matrix factorization model, and the diversity of the results accounts for the query relevance and the novelty w.r.t. these vectors. To learn the representation vectors of nodes, we derive the multiplicative updating rules to train the nonnegative matrix factorization model. We perform a comprehensive evaluation on our approach with various baselines. The results show the effectiveness of our proposed solution, and verify that attributes do help improve diversification performance.

Metatation

In the domain of literary criticism, many critics practice close reading , annotating by hand while performing a detailed analysis of a single text. Often this process employs the use of external resources to aid analysis. In this article, we present a study and subsequent tool design focused on leveraging a critic’s annotations as implicit interactions for initiating context-specific computational support that automatically searches external resources. We observed 14 poetry critics performing a close reading, revealing a set of cognitive practices supported through free-form annotation that have not previously been discussed in this context. We used guidelines derived from our study to design a tool, Metatation, which uses a pen-and-paper system with a peripheral display to utilize reader annotations as underspecified interactions to augment close reading. By turning paper-based annotations into implicit queries, Metatation provides relevant supplemental information in a just-in-time manner and acts as a bridge between close and distant reading.

Keyword Extraction Using Particle Swarm Optimization

Without formal structure data are those that have no prearranged form or structure and are full of textual data. Typical unstructured systems include emails, reports, telephone or messaging conversations, etc. The main goal of this work is to extract the keywords from a conversation using particle swarm optimization. Keywords are grouped together under their classification and then suggested to the user. In existing work, using diverse keyword extraction, to find topic modelling information, representation of the main topics of transcript and diverse keyword selection. It maximizes the coverage of topics that are automatically recognized in transcript of conversation fragment. Once a set of keywords is extracted, it is clustered according to their user queries and recommended to the user. At the end of result, a single implicit query cannot improve user's satisfaction with the recommended documents. So, swarm intelligence technique is to be applied, it will minimize redundancy in a short list of Keywords and provide accurate query result compared to greedy algorithm.

SMe: explicit & implicit constrained-space probabilistic threshold range queries for moving objects

This paper studies the constrained-space probabilistic threshold range query (CSPTRQ) for moving objects, where objects move in a constrained-space (i.e., objects are forbidden to be located in some specific areas), and objects' locations are uncertain. We differentiate two forms of CSPTRQs: explicit and implicit ones. Specifically, for each moving object o, we model its location uncertainty as a closed region, u, together with a probability density function. We also model a query range, R, as an arbitrary polygon. An explicit query can be reduced to a search (over all the u) that returns a set of tuples in form of (o, p) such that p ? pt, where p is the probability of o being located in R, and 0≤pt ≤ 1 is a given probabilistic threshold. In contrast, an implicit query returns only a set of objects (without attaching the specific probability information), whose probabilities being located in R are higher than pt. The CSPTRQ is a variant of the traditional probabilistic threshold range query (PTRQ). As objects moving in a constrained-space are common, clearly, it can also find many applications. At the first sight, our problem can be easily tackled by extending existing methods used to answer the PTRQ. Unfortunately, those classical techniques are not well suitable for our problem, due to a set of new challenges. Another method used to answer the constrained-space probabilistic range query (CSPRQ) can be easily extended to tackle our problem, but a simple adaptation of this method is inefficient, due to its weak pruning/validating capability. To solve our problem, we develop targeted solutions that are easy-to-understand and also easy-to-implement. Our central idea is to swap the order of geometric operations and to compute the appearance probability in a multi-step manner. We demonstrate the efficiency and effectiveness of the proposed methods through extensive experiments. Meanwhile, from the experimental results, we further perceive the difference between explicit and implicit queries; this finding is interesting and also meaningful especially for the topics of other types of probabilistic threshold queries.

Model exchange with the NeuroML model database

The Neural Open Markup Language project, NeuroML, is an international, collaborative initiative to develop a language for describing and sharing complex, multiscale neuron and neuronal network models [1]. The project focuses on the key objects that need to be exchanged among software applications used by computational neuroscientists. Examples of these objects include descriptions of neuronal morphology, the dynamics of ion channels and synaptic mechanisms, and the connectivity patterns of networks of model neurons. This modular approach brings additional benefits: not only can entire models be published and exchanged in this format, but each individual object or component, such as a specific calcium channel or excitatory synapse, can be shared and re-implemented in a different model. The use of a standardized description language based on XML also facilitates the development of tools that promote simulator interoperability. The NeuroML Model Database (NeuroML-db.org) is a relational database that provides a means for exchanging multiscale NeuroML model descriptions and their components. There are several existing, curated model databases that include neuroscience models in multiple formats such as ModelDB [2] and the BioModels Database [3], which provide excellent resources for sharing diverse, published neuroscience models in multiple formats. In contrast, NeuroML-db is more focused, including only NeuroML models. This emphasis allows the database design and search to take advantage of this specific format, and in particular, allows for efficient searches over sub-components of models. In addition, the NeuroML Model Database can be used to search over public models developed as part of the Open Source Brain (OSB) initiative, which provides a software infrastructure for the collaborative development and evaluation of models [4]. NeuroML is the preferred model description format for OSB, and OSB models that are described using NeuroML version 2.0 can be flagged for automatic inclusion in NeuroML-db. An overarching design goal for the database is to provide a simple keyword search interface. Because many search terms involve terminology from neuroscience, we complement a metadata-based keyword search with a mechanism that can provide information about the biological meaning of the keywords, where this semantic information is available through an existing ontology for neuroscience called NeuroLex [5]. Through implicit query reformulation, this semantic information is used to expand the search results. For example, if the query is a single brain region, all cell and network models for cells and networks from that region and its sub-regions are provided to the user.

Content based Image Retrieval using Implicit and Explicit Feedback with Interactive Genetic Algorithm

There are various new applications of genetic algorithms to information retrieval, mostly with respect to relevance feedback. However, they are yet to be evaluated in a way that allows them to be compared with each other and with other relevance feedback techniques. There is always need to efficiently store and retrieve image data to perform assigned tasks and to make a decision. This paper presents a new image retrieval framework with two types of relevance feedback i.e., implicit feedback in combination with explicit feedback. This paper employs Interactive Genetic Approach to discover a combination of descriptors that better characterizes the user perception of image resemblance. This approach provides better management and retrieval of images than the keyword-based approach. However, most of the conventional methods do not have the capability to effectively incorporate human interaction and emotion into retrieving images. In order to solve this problem we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). In this approach we used two tier architecture of implicit and explicit feedback with IGA. Therefore, this system facilitates the search for the image not only with explicit queries, but also with implicit queries.

Can eyes reveal interest? Implicit queries from gaze patterns

We study a new research problem, where an implicit information retrieval query is inferred from eye movements measured when the user is reading, and used to retrieve new documents. In the training ...

Fixpoint and While Temporal Query Languages

The article investigates the expressive power of implicit temporal query languages. These languages are designed while assuming temporal data modelled as sequences of data. So far, two styles of implicit temporal query languages are known: t-while like languages are based on a temporal extension of while with left and right moves; μtl like languages are a fixpoint extension of tl. This article focusses on comparing the expressive power of t-while style languages and μtl languages and provides complementary results with respect to Abiteboul et al. (1999, J. Comput. System Sci.,58, 54–68). The main contribution is the proof of the equivalence of the three following temporal languages: the non-inflationary variant of μtl, the language t-while and more surprisingly the language t-fixpoint.

Semantic lenses: Seamless augmentation of web pages with context information from implicit queries

We propose a novel method to support analytical processes by visually integrating context information directly into web pages. The context information is obtained from knowledge bases or services that provide additional information about certain concepts and instances of a knowledge domain. The approach performs implicit queries on the underlying information sources using the key phrases of the web page and visually presents the results to the user in a seamless way. The technique aims at improving the integration of different information sources into the user's task. The method has two phases, an augmentation and an interaction phase. In the augmentation phase, for each key phrase the availability of additional context information is checked. All phrases for which context information is available are highlighted by adding special markups and scripts to the web page. In the interaction phase, the user can click on a highlighted phrase to receive the associated context information. The information is displayed directly in the web page in an overlay that we call a semantic lens.

A human-oriented image retrieval system using interactive genetic algorithm

Content-based image retrieval has been actively studied in several fields. This provides more effective management and retrieval of images than the keyword-based approach. However, most of the conventional methods lack the capability to effectively incorporate human intuition and emotion into retrieving images. It is difficult to obtain satisfactory results when the user wants the image that cannot be explicitly described or can be requested only based on impression. In order to solve this problem and supplement the lack of the user's expression capability, we have developed an image retrieval system based on human preference and emotion by using an interactive genetic algorithm (IGA). This system extracts the feature from images by wavelet transform, and provides a user-friendly means to retrieve an image from a large database when the user cannot clearly define what the image must be. Therefore, this facilitates the search for the image not only with explicit queries, but also with implicit queries such as "cheerful impression," "gloomy impression," and so on. A thorough experiment with a 2000 image database shows the usefulness of the proposed system.

Some results on time-varying and relativised cellular automata*

Cellular automata (CA) are parallel language recognition devices. In this paper, the notion of a time-varying CA (TVCA) is introduced. In a TVCA, the transition function can vary with time, and the variation is controlled by a prespecified language. This language can be viewed as an oracle to which the TVCA makes implicit queries; thus TVCA provide a mechanism for defining relativised CA. In this paper, the recognition capabilities of CA and TVCA are compared. TVCA as relativised CA are formalised, and the power of this implicit query mechanism is compared with the traditional oracle access in Turing machines.

Predictions from play.

IN a series of studies of curiosity and exploration in young children1–6, it was found that 3 to 5 year olds could be placed in one of three categories, according to their responses to a new toy. Usually, when a child is confronted with a new toy, he first inspects and investigates it; then, when he is familiar with it, he “plays” with it. These two categories of behaviour have been more formally characterized as “specific” and “diversive exploration” respectively7, and their distinctive behavioural features have been described4,5. Essentially, in specific exploration or investigation the implicit query seems to be “What can this object do?”; in diversive exploration or play it is “What can I do with this object?”