Amir Gilad

Needle in a Hyperstack

Abstract This study examines the process of searching for information on the Internet by (a) assessing participants’ success in finding specific information on the Web, (b) identifying the characteristics of the information search process (e.g., search duration, number of steps used), and (c) identifying the search strategies used and assessing their effectiveness. Subjects were 54 graduate students who were asked to accomplish three relatively simple search tasks: (a) find a picture of the Mona Lisa, (b) find a complete text of Robinson Crusoe or David Copperfield, and (c) find a recipe of an apple pie that was accompanied by a photograph. All search processes performed by each participant were recorded and fully logged by tracking software. Findings showed that overall success in searching information was low. About 46% of the search tasks were not accomplished successfully, and only 15% of the students succeeded in all three tasks. Further analysis reveals nine different search strategies used by the students. The distribution of their usage and effectiveness are presented and discussed.

Selective provenance for datalog programs using top-k queries

Highly expressive declarative languages, such as datalog, are now commonly used to model the operational logic of data-intensive applications. The typical complexity of such datalog programs, and the large volume of data that they process, call for result explanation. Results may be explained through the tracking and presentation of data provenance, and here we focus on a detailed form of provenance (how-provenance), defining it as the set of derivation trees of a given fact. While informative, the size of such full provenance information is typically too large and complex (even when compactly represented) to allow displaying it to the user. To this end, we propose a novel top-k query language for querying datalog provenance, supporting selection criteria based on tree patterns and ranking based on the rules and database facts used in derivation. We propose an efficient novel algorithm based on (1) instrumenting the datalog program so that, upon evaluation, it generates only relevant provenance, and (2) efficient top-k (relevant) provenance generation, combined with bottom-up datalog evaluation. The algorithm computes in polynomial data complexity a compact representation of the top-k trees which may be explicitly constructed in linear time with respect to their size. We further experimentally study the algorithm performance, showing its scalability even for complex datalog programs where full provenance tracking is infeasible.

selP: Selective tracking and presentation of data provenance

Highly expressive declarative languages, such as Datalog, are now commonly used to model the operational logic of data-intensive applications. The typical complexity of such Datalog programs, and the large volume of data that they process, call for the tracking and presentation of data provenance. Provenance information is crucial for explaining and justifying the Datalog program results. However, the size of full provenance information is in many cases too large (and its concise representations are too complex) to allow its presentation to the user. To this end, we propose a demonstration of selP, a system that allows the selective presentation of provenance, based on user-specified top-k queries. We will demonstrate the usefulness of selP using a real-life program and data, in the context of Information Extraction.

QPlain: Query by explanation

To assist non-specialists in formulating database queries, multiple frameworks that automatically infer queries from a set of input and output examples have been proposed. While highly useful, a shortcoming of the approach is that if users can only provide a small set of examples, many inherently different queries may qualify. We observe that additional information about the examples, in the form of their explanations, is useful in significantly focusing the set of qualifying queries. We propose to demonstrate QPlain, a system that learns conjunctive queries from examples and their explanations. We capture explanations of different levels of granularity and detail, by leveraging recently developed models for data provenance. Explanations are fed through an intuitive interface, are compiled to the appropriate provenance model, and are then used to derive proposed queries. We will demonstrate that it is feasible for non-specialists to provide examples with meaningful explanations, and that the presence of such explanations result in a much more focused set of queries which better match user intentions.

NLProv: natural language provenance

We propose to present NLProv: an end-to-end Natural Language (NL) interface for database queries. Previous work has focused on interfaces for specifying NL questions, which are then compiled into queries in a formal language (e.g. SQL). We build upon this work, but focus on presenting a detailed form of the answers in Natural Language. The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only which are the results but also their explanations. We develop a novel method for transforming provenance information to NL, by leveraging the original NL question structure. Furthermore, since provenance information is typically large, we present two solutions for its effective presentation as NL text: one that is based on provenance factorization with novel desiderata relevant to the NL case, and one that is based on summarization.

Provenance for natural language queries

Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form of the answers in NL. The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations. We develop a novel method for transforming provenance information to NL, by leveraging the original NL query structure. Furthermore, since provenance information is typically large and complex, we present two solutions for its effective presentation as NL text: one that is based on provenance factorization, with novel desiderata relevant to the NL case, and one that is based on summarization. We have implemented our solution in an end-to-end system supporting questions, answers and provenance, all expressed in NL. Our experiments, including a user study, indicate the quality of our solution and its scalability.

Questpro: queries in SPARQL through provenance

We propose to demonstrate QuestPro, a prototype interactive system aimed at allowing non-expert users to specify SPARQL queries. Notably, QuestPro makes an extensive use of provenance in deriving the SPARQL queries, in two ways. First, we ask users to provide example output nodes along with explanations that are then treated as the provenance of the underlying query, guiding the system’s search for a fitting query. We have designed an intuitive interface through which users can gradually build their explanations while understanding the connections between the different objects. The system then generates a set of candidate queries and uses provenance to explain each candidate, prompting user feedback to choose between them. We will demonstrate the usability of QuestPro using an ontology of academic publications, engaging the audience in the interactive process while explaining the under-the-hood model and algorithms. PVLDB Reference Format: Efrat Abramovitz, Daniel Deutch, Amir Gilad. QuestPro: Queries in SPARQL Through Provenance. PVLDB, 11 (12): 1994-1997, 2018. DOI: https://doi.org/10.14778/3229863.3236243

NLproveNAns: natural language provenance for non-answers

Natural language (NL) interfaces to databases allow users without technical background to query the database and get the results. Users of such systems may be surprised by the absence of certain expected results. To this end, we propose to demonstrate NLProveNAns, a system that allows non-expert users to view explanations for non-answers of interest. The explanations are shown in an intuitive manner, by highlighting parts of the original NL query that are intuitively “responsible” for the absence of the expected result. Our solution builds upon and combines recent advancements in Natural Language Interfaces to Databases and models for why-not provenance. In particular, the systems can provide explanations in one of two flavors corresponding to two different why-not provenance models: a short explanation based on the frontier picky model, and a detailed explanation based on the why-not polynomial model. PVLDB Reference Format: Daniel Deutch, Nave Frost, Amir Gilad, Tomer Haimovich. NLProveNAns: Natural Language Provenance for Non-Answers. PVLDB, 11 (12): 1986-1989, 2018. DOI: https://doi.org/10.14778/3229863.3236241

Efficient provenance tracking for datalog using top-k queries

Highly expressive declarative languages, such as datalog, are now commonly used to model the operational logic of data-intensive applications. The typical complexity of such datalog programs, and the large volume of data that they process, call for result explanation. Results may be explained through the tracking and presentation of data provenance, defined here as the set of derivation trees of a given fact. While informative, the size of such full provenance information is typically too large and complex (even when compactly represented) to allow displaying it to the user. To this end, we propose a novel top-k query language for querying datalog provenance, supporting selection criteria based on tree patterns and ranking based on the rules and database facts used in derivation. We propose an efficient novel algorithm that computes in polynomial data complexity a compact representation of the top-k trees which may be explicitly constructed in linear time with respect to their size. We further experimentally study the algorithm performance, showing its scalability even for complex datalog programs where full provenance tracking is infeasible.

Natural Language Explanations for Query Results

Multiple lines of research have developed Natural Language (NL) interfaces for formulating database queries. We build upon this work, but focus on presenting a highly detailed form of the answers in NL. The answers that we present are importantly based on the provenance of tuples in the query result, detailing not only the results but also their explanations. We develop a novel method for transforming provenance information to NL, by leveraging the original NL query structure. Furthermore, since provenance information is typically large and complex, we present two solutions for its effective presentation as NL text: one that is based on provenance factorization, with novel desiderata relevant to the NL case, and one that is based on summarization.