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arxiv: 1906.10322 · v1 · pith:EL5K6PFZnew · submitted 2019-06-25 · 💻 cs.DB

Example-Driven Query Intent Discovery: Abductive Reasoning using Semantic Similarity

Anna Fariha , Alexandra Meliou This is my paper

Pith reviewed 2026-05-25 16:26 UTC · model grok-4.3

classification 💻 cs.DB
keywords query by exampleabductive reasoningsemantic similarityquery intent discoverydatabase interfacesprobabilistic modelsselect project join queries
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The pith

SQuID infers the intended database query from user examples by treating discovery as probabilistic abduction over semantic similarities.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper establishes that query intent discovery can be solved by framing it as finding the most likely query explanation through abductive reasoning that factors in semantic similarities present in the data. This approach matters because non-expert users struggle with rigid query languages and prior example-driven methods often return queries that are too broad by focusing only on structural matches. SQuID supports a wider set of queries than earlier techniques, including those with aggregation and intersection, while an abduction-ready database precomputes the needed semantic properties to keep inference fast. Evaluation across three real-world datasets shows the method produces results that better match user intent than machine learning baselines or existing query reverse engineering systems.

Core claim

SQuID expresses query intent discovery using a probabilistic abduction model that infers a select-project-join query, with optional group-by aggregation and intersection, as the most likely explanation for the provided examples. The approach relies on an abduction-ready database that precomputes semantic properties and related statistics to support real-time performance in an open-world setting.

What carries the argument

Abduction-ready database that precomputes semantic properties and statistics to enable efficient probabilistic abduction for inferring the intended query from examples.

If this is right

  • The system can automatically generate a broader class of queries than prior QBE techniques, including those with group-by and intersection operators.
  • Real-time query formulation becomes possible without requiring users to know the schema details.
  • The probabilistic model produces queries that more closely reflect user intent than methods based only on structural similarity.
  • Performance and accuracy hold across multiple real-world datasets when compared to machine learning and query reverse engineering baselines.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • The same precomputation strategy could reduce latency in other intent-inference tasks that currently require on-the-fly semantic analysis.
  • Extending the abduction model to handle additional operators such as unions or nested subqueries would test whether the core approach generalizes beyond the current query class.
  • If semantic statistics can be maintained incrementally, the system might support dynamic databases without full recomputation.

Load-bearing premise

The precomputed semantic properties and statistics in the abduction-ready database are sufficient to enable accurate inference of user intent in an open-world setting.

What would settle it

If a new dataset shows that queries produced by SQuID match user intent less often than queries from purely structural similarity methods, while the precomputed statistics remain unchanged, the advantage of the semantic abduction model would be falsified.

Figures

Figures reproduced from arXiv: 1906.10322 by Alexandra Meliou, Anna Fariha.

Figure 1
Figure 1. Figure 1: Excerpt of two relations of the CS Academics database. [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Partial schema of the IMDb database. The schema con [PITH_FULL_IMAGE:figures/full_fig_p002_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: provides a summary exposition of prior work, and contrasts with our contri￾butions. We detail this classification and metrics in Appendix F and discuss the related work in Section 8. Legend query class QBE: Query by Example QRE: Query Reverse Engineering additional DX: Data Exploration requirements KG: Knowledge Graph !: with significant restrictions join projection selection aggregation semi-join implicit… view at source ↗
Figure 4
Figure 4. Figure 4: SQUID’s operation includes an offline module, which constructs an abduction-ready database (αDB) and precomputes statistics of semantic properties. During normal operation, SQUID’s query intent discovery module interacts with the αDB to identify the semantic context of the provided examples and ab￾duces the most likely query intent. Complex intent. A user’s information need is often more com￾plex than what… view at source ↗
Figure 5
Figure 5. Figure 5: A genre value (e.g., genre=Comedy) is a basic semantic property of a movie (through the movietogenre relation). A person is associated with movie entities (through the castinfo relation); aggregates of basic semantic properties of movies are derived se￾mantic properties of person, e.g., the number of comedy movies a person appeared in. The αDB stores the derived property in the new relation persontogenre. … view at source ↗
Figure 6
Figure 6. Figure 6: Sample database with example tuples [PITH_FULL_IMAGE:figures/full_fig_p005_6.png] view at source ↗
Figure 8
Figure 8. Figure 8: Two cases for derived semantic property filters. Top two [PITH_FULL_IMAGE:figures/full_fig_p006_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Average abduction time over the benchmark queries in [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: SQUID achieves high accuracy with few examples (typically ∼ 5) in most benchmark queries. IQ1 IQ2 IQ3 IQ4 IQ5 IQ6 IQ7 IQ8 IQ9 IQ10 IQ11 IQ12 IQ13 IQ14 IQ15 IQ16 Benchmark Queries 10−3 10−1 101 Time (s) Actual SQuID (a) IMDb DQ1 DQ2 DQ3 DQ4 DQ5 Benchmark Queries 10−3 10−2 10−1 100 Time (s) Actual SQuID (b) DBLP [PITH_FULL_IMAGE:figures/full_fig_p009_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: SQUID rarely produces queries that are slower than the original with respect to query runtime. DBLP datasets as the number of provided examples increases, av￾eraged over all benchmark queries in each dataset. Since SQUID retrieves semantic properties and computes context for each exam￾ple, the runtime increases linearly with the number of examples, which is what we observe in practice [PITH_FULL_IMAGE:fi… view at source ↗
Figure 12
Figure 12. Figure 12: Effect of disambiguation on IMDb IQ6: In many movies where Clint Eastwood was a director, he was also an actor. SQUID needs to observe sufficient examples to dis￾cover that the property role:Actor is not intended, so recall con￾verges more slowly. IQ10: SQUID performs poorly for this query. The query looks for actors appearing in more than 10 Russian movies that were re￾leased after 2010. While SQUID disc… view at source ↗
Figure 13
Figure 13. Figure 13: Precision, recall, and f-score for (a) Funny actors (b) [PITH_FULL_IMAGE:figures/full_fig_p010_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Both systems achieve perfect f-score on Adult (not [PITH_FULL_IMAGE:figures/full_fig_p010_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: SQUID produces queries with significantly fewer predicates than TALOS and is more accurate on both IMDb and DBLP. SQUID is almost always faster on IMDb, but TALOS is faster on DBLP. simpler queries, close in the number of predicates to the original query, while TALOS queries contain more than 100 predicates in 20% of the cases. SQUID is faster than TALOS when the input cardinality is low (∼100 tuples), an… view at source ↗
Figure 16
Figure 16. Figure 16: (a) PU-learning needs a large fraction ( [PITH_FULL_IMAGE:figures/full_fig_p011_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Source of datasets IMDb & variations IMDb bd-IMDb DB size 633 MB DB size 1926 MB #Relations 15 #Relations 15 Precomputed DB size 2310 MB Precomputed DB size 5971 MB Precomputation time 150 min Precomputation time 370 min person 6,150,949 person 12,301,898 Rel. movie 976,719 Rel. movie 1,953,438 Card. castinfo 14,915,325 Card. castinfo 59,661,300 bs-IMDb sm-IMDb DB size 1330 MB DB size 75 MB #Relations 15 … view at source ↗
Figure 18
Figure 18. Figure 18: Description of IMDb, DBLP, and Adult datasets [PITH_FULL_IMAGE:figures/full_fig_p015_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Benchmark queries for the IMDb dataset ID Task J S #Result DQ1 Authors who collaborated with both U Washington and Microsoft Research Redmond 5 2 30 DQ2* Authors with at least 10 SIGMOD and at least 10 VLDB publications 8 4 52 DQ3 SIGMOD publications in 2010 – 2012 3 3 468 DQ4 Publications Jiawei Han, Xifeng Yan, and Philip S. Yu published together 7 3 15 DQ5 Publications between USA and Canada 5 2 336 * … view at source ↗
Figure 20
Figure 20. Figure 20: Benchmark queries for the DBLP dataset Parameter Default value Description ρ 0.1 Base filter prior parameter. γ 2 Domain coverage penalty parameter. τa 5 Association strength threshold. τs 2.0 Skewness threshold [PITH_FULL_IMAGE:figures/full_fig_p016_20.png] view at source ↗
Figure 22
Figure 22. Figure 22: Benchmark queries for the Adult dataset Semi-join is a special type of join which is particularly useful for QBE systems. A system is considered to support semi-join if it allows inclusion of relations in the output query that have no 16 [PITH_FULL_IMAGE:figures/full_fig_p016_22.png] view at source ↗
Figure 23
Figure 23. Figure 23: Effect of different values for ρ for few benchmark queries of the IMDb dataset 5 15 # Examples 0.0 0.5 1.0 F-score IQ2 γ = 10 γ = 5 γ = 2 γ = 0 5 15 # Examples IQ3 5 15 # Examples IQ4 5 15 # Examples IQ11 5 15 # Examples IQ16 [PITH_FULL_IMAGE:figures/full_fig_p017_23.png] view at source ↗
Figure 24
Figure 24. Figure 24: Effect of different values for γ for few benchmark queries of the IMDb dataset 3 5 7 9 # Examples 11 13 15 0.00 0.25 0.50 0.75 1.00 F-score τa = 0 τa = 5 [PITH_FULL_IMAGE:figures/full_fig_p017_24.png] view at source ↗
Figure 25
Figure 25. Figure 25: Effect of different values for τa for IQ5 (IMDb) 3 5 7 9 # Examples 11 13 15 0.00 0.25 0.50 0.75 1.00 F-score τs = N/A τs = 0 τs = 2 τs = 4 [PITH_FULL_IMAGE:figures/full_fig_p017_25.png] view at source ↗
Figure 26
Figure 26. Figure 26: Effect of different values for τs for IQ1 (IMDb) attribute projected in the input schema (e.g., in Example 1.1, no attribute of research appears in the input tuples, but Q2 includes research). While knowledge graph based systems do not directly support semi-join as defined in the relational database setting, they support same expressivity through alternative mechanism. Implicit property refers to the the … view at source ↗
read the original abstract

Traditional relational data interfaces require precise structured queries over potentially complex schemas. These rigid data retrieval mechanisms pose hurdles for non-expert users, who typically lack language expertise and are unfamiliar with the details of the schema. Query by Example (QBE) methods offer an alternative mechanism: users provide examples of their intended query output and the QBE system needs to infer the intended query. However, these approaches focus on the structural similarity of the examples and ignore the richer context present in the data. As a result, they typically produce queries that are too general, and fail to capture the user's intent effectively. In this paper, we present SQuID, a system that performs semantic similarity-aware query intent discovery. Our work makes the following contributions: (1) We design an end-to-end system that automatically formulates select-project-join queries in an open-world setting, with optional group-by aggregation and intersection operators; a much larger class than prior QBE techniques. (2) We express the problem of query intent discovery using a probabilistic abduction model, that infers a query as the most likely explanation of the provided examples. (3) We introduce the notion of an abduction-ready database, which precomputes semantic properties and related statistics, allowing SQuID to achieve real-time performance. (4) We present an extensive empirical evaluation on three real-world datasets, including user-intent case studies, demonstrating that SQuID is efficient and effective, and outperforms machine learning methods, as well as the state-of-the-art in the related query reverse engineering problem.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 1 minor

Summary. The paper presents SQuID, an end-to-end system for example-driven query intent discovery that uses probabilistic abduction over semantic similarity to infer the most likely SPJ query (with optional group-by aggregation and intersection) explaining user-provided examples. It introduces the abduction-ready database, which precomputes semantic properties and statistics to support real-time inference in an open-world setting. The central claims are that this approach handles a larger query class than prior QBE work, achieves efficiency and effectiveness on three real-world datasets, and outperforms both machine learning baselines and state-of-the-art query reverse engineering methods, as shown via quantitative results and user-intent case studies.

Significance. If the empirical results and the sufficiency of the precomputed properties hold, the work would advance query-by-example interfaces by incorporating semantic context rather than relying solely on structural similarity, enabling more accurate intent discovery for non-expert users over complex schemas. The explicit use of probabilistic abduction, the larger supported query class, the real-time performance via precomputation, and the inclusion of user studies on real datasets are concrete strengths that would strengthen the contribution if the open-world claims are substantiated.

major comments (2)
  1. [abduction-ready database definition and system architecture] The section introducing the abduction-ready database and its precomputed semantic properties: the central claim of outperformance in an open-world setting depends on these precomputations capturing sufficient context to identify the most likely query explanation; the manuscript provides no analysis or test cases showing robustness when user intent involves semantics or distributions absent from the precomputed properties, which directly risks invalidating the superiority over ML and reverse-engineering baselines.
  2. [empirical evaluation] Empirical evaluation section: the abstract and claims assert quantitative outperformance on three datasets with specific metrics, yet the provided description contains no tables, result values, or experimental protocol details (e.g., how examples were generated, exact metrics, or statistical significance), preventing assessment of whether the data actually supports the efficiency and effectiveness assertions.
minor comments (1)
  1. [Abstract] The abstract would benefit from a single sentence summarizing the key quantitative findings (e.g., runtime or accuracy deltas) to allow readers to gauge the strength of the empirical claims without reading the full evaluation section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on our manuscript. We address the two major comments point-by-point below, clarifying our approach and committing to revisions where they strengthen the presentation of the abduction-ready database and empirical results.

read point-by-point responses

  1. Referee: [abduction-ready database definition and system architecture] The section introducing the abduction-ready database and its precomputed semantic properties: the central claim of outperformance in an open-world setting depends on these precomputations capturing sufficient context to identify the most likely query explanation; the manuscript provides no analysis or test cases showing robustness when user intent involves semantics or distributions absent from the precomputed properties, which directly risks invalidating the superiority over ML and reverse-engineering baselines.

    Authors: We agree that an explicit analysis of robustness to semantics or distributions absent from the precomputed properties would strengthen the claims. The abduction-ready database precomputes semantic similarities via embeddings and aggregate statistics chosen to generalize across common intents in the open-world setting. Our evaluation on three real-world datasets demonstrates outperformance over ML and reverse-engineering baselines, providing indirect evidence that the precomputations suffice for the tested cases. To directly address the concern, we will add a dedicated subsection on assumptions, limitations, and potential failure modes, including additional synthetic test cases where user intent relies on uncaptured properties. revision: yes

  2. Referee: [empirical evaluation] Empirical evaluation section: the abstract and claims assert quantitative outperformance on three datasets with specific metrics, yet the provided description contains no tables, result values, or experimental protocol details (e.g., how examples were generated, exact metrics, or statistical significance), preventing assessment of whether the data actually supports the efficiency and effectiveness assertions.

    Authors: The full manuscript contains a complete empirical evaluation section (Section 5) with tables reporting precision, recall, F1, runtime, and user-study metrics across the three datasets, plus details on example generation (real user queries plus controlled synthetic generation) and statistical significance via paired t-tests. We acknowledge that the protocol description could be more self-contained. We will revise the section to expand the experimental protocol subsection, include all metric definitions and generation procedures inline, and ensure tables are referenced with full context so the results can be assessed independently. revision: yes

Circularity Check

0 steps flagged

No significant circularity detected; derivation is self-contained with external validation.

full rationale

The paper presents SQuID as a new end-to-end system using a probabilistic abduction model over an introduced abduction-ready database to infer SPJ queries from examples. Central claims rest on empirical outperformance versus ML baselines and prior query reverse engineering methods across three real-world datasets, with no equations or steps shown reducing by construction to fitted inputs, self-definitions, or self-citation chains. The approach is externally benchmarked rather than internally forced, satisfying the criteria for a non-circular finding.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 1 invented entities

The approach rests on the domain assumption that abductive reasoning over semantic similarity can recover user intent and on the invented concept of an abduction-ready database for efficiency.

axioms (1)
  • domain assumption Probabilistic abduction can infer the most likely query as explanation for provided examples.
    Central modeling choice stated in contribution (2).
invented entities (1)
  • abduction-ready database no independent evidence
    purpose: Precomputes semantic properties and statistics to enable real-time query inference.
    New construct introduced in contribution (3) to support system performance.

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Reference graph

Works this paper leans on

72 extracted references · 72 canonical work pages · cited by 2 Pith papers

  1. [1]

    SQ UID employs an offline module that performs several pre-computation steps to make the database abduction-ready

    OFFLINE ABDUCTION PREPARATION In this section, we discuss system considerations to perform query intent discovery efficiently. SQ UID employs an offline module that performs several pre-computation steps to make the database abduction-ready. The abduction-ready database ( αDB) augments the original database with derived relations that store associations acr...

    work page

  2. [2]

    In this section, we describe how SQUID resolves ambiguity in the provided examples, how it derives their semantic context, and how it finally abduces the intended query

    QUERY INTENT DISCOVERY During normal operation, SQ UID receives example tuples from a user, consults the αDB, and infers the most likely query intent (Definition 2.1). In this section, we describe how SQUID resolves ambiguity in the provided examples, how it derives their semantic context, and how it finally abduces the intended query. 6.1 Entity and Contex...

    work page 1994

  3. [3]

    115 fun- niest actors

    EXPERIMENTS In this section, we present an extensive experimental evaluation of SQ UID over three real-world datasets, with a total of 41 bench- mark queries of varying complexities. Our evaluation shows that SQUID is scalable and effective, even with a small number of example tuples. Our evaluation extends to qualitative case stud- ies over real-world us...

    work page 2000

  4. [4]

    appearing in more than K comedies

    RELATED WORK Query-by-Example (QBE) was an early effort to assist users without SQL expertise in formulating SQL queries [67]. Existing QBE systems [51, 48] identify relevant relations and joins in situa- tions where the user lacks schema understanding, but are limited to project-join queries. These systems focus on the common structure of the example tup...

    work page

  5. [5]

    We presented SQ UID, a sys- tem that performs query intent discovery effectively and efficiently, even with few examples in most cases

    SUMMARY AND FUTURE DIRECTIONS In this paper, we focused on the problem of query intent discov- ery from a set of example tuples. We presented SQ UID, a sys- tem that performs query intent discovery effectively and efficiently, even with few examples in most cases. The insights of our work rely on exploiting the rich information present in the data to dis- ...

    work page

  6. [6]

    Abouzied, D

    A. Abouzied, D. Angluin, C. Papadimitriou, J. M. Hellerstein, and A. Silberschatz. Learning and verifying quantified boolean queries by example. InPODS, pages 49–60, 2013

    work page 2013

  7. [7]

    Agarwal, A

    S. Agarwal, A. Sureka, N. Mittal, R. Katyal, and D. Correa. DBLP records and entries for key computer science conferences, 2016

    work page 2016

  8. [8]

    Agrawal, S

    S. Agrawal, S. Chaudhuri, and G. Das. DBXplorer: a system for keyword-based search over relational databases. InICDE, pages 5–16, 2002

    work page 2002

  9. [9]

    Arenas, G

    M. Arenas, G. I. Diaz, and E. V . Kostylev. Reverse engineering SPARQL queries. In WWW, pages 239–249, 2016

    work page 2016

  10. [10]

    Arieli, M

    O. Arieli, M. Denecker, B. V . Nuffelen, and M. Bruynooghe. Coherent integration of databases by abductive logic programming. J. Artif. Intell. Res., 21:245–286, 2004

    work page 2004

  11. [11]

    Barcel ´o and M

    P. Barcel ´o and M. Romero. The Complexity of Reverse Engineering Problems for Conjunctive Queries. In ICDT, volume 68, pages 7:1–7:17, 2017

    work page 2017

  12. [12]

    C. A. Becker. Semantic context effects in visual word recognition: An analysis of semantic strategies. Memory & cognition, 8(6):493–512, 1980

    work page 1980

  13. [13]

    Bekker and J

    J. Bekker and J. Davis. Positive and unlabeled relational classification through label frequency estimation. InILP, pages 16–30, 2017

    work page 2017

  14. [14]

    Bekker and J

    J. Bekker and J. Davis. Estimating the class prior in positive and unlabeled data through decision tree induction. In AAAI, pages 2712–2719, 2018

    work page 2018

  15. [15]

    Bekker and J

    J. Bekker and J. Davis. Learning from positive and unlabeled data: A survey. CoRR, abs/1811.04820, 2018

    work page arXiv 2018

  16. [16]

    L. E. Bertossi and B. Salimi. Causes for query answers from databases: Datalog abduction, view-updates, and integrity constraints. Int. J. Approx. Reasoning, 90:226–252, 2017

    work page 2017

  17. [17]

    Bonifati, R

    A. Bonifati, R. Ciucanu, and S. Staworko. Learning join queries from user examples. TODS, 40(4):24:1–24:38, 2016

    work page 2016

  18. [18]

    Bonifati, U

    A. Bonifati, U. Comignani, E. Coquery, and R. Thion. Interactive mapping specification with exemplar tuples. In SIGMOD, pages 667–682, 2017

    work page 2017

  19. [19]

    Chapelle, B

    O. Chapelle, B. Schlkopf, and A. Zien. Semi-Supervised Learning. The MIT Press, 1st edition, 2010

    work page 2010

  20. [20]

    G. DeJong. Prediction and substantiation: A new approach to natural language processing. Cognitive Science, 3(3):251–273, 1979

    work page 1979

  21. [21]

    Deutch and A

    D. Deutch and A. Gilad. Qplain: Query by explanation. In ICDE, pages 1358–1361, 2016

    work page 2016

  22. [22]

    G. I. Diaz, M. Arenas, and M. Benedikt. SPARQLByE: Querying RDF data by example. PVLDB, 9(13):1533–1536, 2016

    work page 2016

  23. [23]

    Dimitriadou, O

    K. Dimitriadou, O. Papaemmanouil, and Y . Diao. Aide: An active learning-based approach for interactive data exploration. TKDE, 28(11):2842–2856, 2016

    work page 2016

  24. [24]

    Drosou and E

    M. Drosou and E. Pitoura. Ymaldb: Exploring relational databases via result-driven recommendations. VLDBJ, 22(6):849–874, 2013

    work page 2013

  25. [25]

    Eirinaki, S

    M. Eirinaki, S. Abraham, N. Polyzotis, and N. Shaikh. Querie: Collaborative database exploration. TKDE, 26(7):1778–1790, 2014

    work page 2014

  26. [26]

    Elkan and K

    C. Elkan and K. Noto. Learning classifiers from only positive and unlabeled data. In SIGKDD, pages 213–220. ACM, 2008

    work page 2008

  27. [27]

    J. Fan, G. Li, and L. Zhou. Interactive sql query suggestion: Making databases user-friendly. In ICDE, pages 351–362, 2011

    work page 2011

  28. [28]

    Fariha, S

    A. Fariha, S. M. Sarwar, and A. Meliou. SQuID: Semantic similarity-aware query intent discovery. In SIGMOD, pages 1745–1748, 2018

    work page 2018

  29. [29]

    X. Ge, Y . Xue, Z. Luo, M. A. Sharaf, and P. K. Chrysanthis. Request: A scalable framework for interactive construction of exploratory queries. In Big Data, pages 646–655, 2016

    work page 2016

  30. [30]

    Getoor and B

    L. Getoor and B. Taskar. Introduction to statistical relational learning, 2007

    work page 2007

  31. [31]

    J. Gray, S. Chaudhuri, A. Bosworth, A. Layman, D. Reichart, M. Venkatrao, F. Pellow, and H. Pirahesh. Data cube: A relational aggregation operator generalizing group-by, cross-tab, and sub-totals. Data mining and knowledge discovery, 1(1):29–53, 1997

    work page 1997

  32. [32]

    J. Han, K. Zheng, A. Sun, S. Shang, and J. R. Wen. Discovering neighborhood pattern queries by sample answers in knowledge base. In ICDE, pages 1014–1025, 2016

    work page 2016

  33. [33]

    Hristidis and Y

    V . Hristidis and Y . Papakonstantinou. DISCOVER: keyword search in relational databases. In VLDB, pages 670–681, 2002

    work page 2002

  34. [34]

    Jayaram, A

    N. Jayaram, A. Khan, C. Li, X. Yan, and R. Elmasri. Querying knowledge graphs by example entity tuples. TKDE, 27(10):2797–2811, 2015

    work page 2015

  35. [35]

    C. Ji, X. Zhou, L. Lin, and W. Yang. Labeling images by integrating sparse multiple distance learning and semantic context modeling. In ECCV (4), volume 7575 of Lecture Notes in Computer Science, pages 688–701. Springer, 2012

    work page 2012

  36. [36]

    Jiang and A

    L. Jiang and A. Nandi. SnapToQuery: Providing interactive feedback during exploratory query specification.PVLDB, 8(11):1250–1261, 2015

    work page 2015

  37. [37]

    A. C. Kakas. Abduction. In Encyclopedia of Machine Learning and Data Mining, pages 1–8. Springer, 2017

    work page 2017

  38. [38]

    D. V . Kalashnikov, L. V . S. Lakshmanan, and D. Srivastava. FastQRE: Fast query reverse engineering. In SIGMOD, pages 337–350, 2018

    work page 2018

  39. [39]

    S. S. Khan and M. G. Madden. A survey of recent trends in one class classification. InAICS, pages 188–197. Springer, 2009

    work page 2009

  40. [40]

    T. Khot, S. Natarajan, and J. W. Shavlik. Relational one-class classification: A non-parametric approach. InAAAI, pages 2453–2459, 2014

    work page 2014

  41. [41]

    Khoussainova, Y

    N. Khoussainova, Y . Kwon, M. Balazinska, and D. Suciu. SnipSuggest: Context-aware autocompletion for SQL. PVLDB, 4(1):22–33, 2010

    work page 2010

  42. [42]

    H. Li, C. Chan, and D. Maier. Query from examples: An iterative, data-driven approach to query construction. PVLDB, 8(13):2158–2169, 2015

    work page 2015

  43. [43]

    L. Lim, H. Wang, and M. Wang. Semantic queries by example. In EDBT, pages 347–358, 2013

    work page 2013

  44. [44]

    B. Liu, Y . Dai, X. Li, W. S. Lee, and S. Y . Philip. Building text classifiers using positive and unlabeled examples. In ICDM, volume 3, pages 179–188. Citeseer, 2003

    work page 2003

  45. [45]

    L. M. Manevitz and M. Yousef. One-class svms for document classification.JMLR, 2(Dec):139–154, 2001

    work page 2001

  46. [46]

    C. D. Manning, P. Raghavan, and H. Sch ¨utze. Introduction to information retrieval. Cambridge University Press, 2008

    work page 2008

  47. [47]

    T. Menzies. Applications of abduction: knowledge-level modelling. Int. J. Hum.-Comput. Stud., 45(3):305–335, 1996. 13

    work page 1996

  48. [48]

    Metzger, R

    S. Metzger, R. Schenkel, and M. Sydow. QBEES: query-by-example entity search in semantic knowledge graphs based on maximal aspects, diversity-awareness and relaxation. J. Intell. Inf. Syst., 49(3):333–366, 2017

    work page 2017

  49. [49]

    Mordelet and J.-P

    F. Mordelet and J.-P. Vert. A bagging svm to learn from positive and unlabeled examples. Pattern Recognition Letters, 37:201–209, 2014

    work page 2014

  50. [50]

    Mottin, M

    D. Mottin, M. Lissandrini, Y . Velegrakis, and T. Palpanas. Exemplar queries: A new way of searching. VLDBJ, 25(6):741–765, 2016

    work page 2016

  51. [51]

    Neelakantan, B

    A. Neelakantan, B. Roth, and A. McCallum. Compositional vector space models for knowledge base completion. In ACL, pages 156–166, 2015

    work page 2015

  52. [52]

    Panev, N

    K. Panev, N. Weisenauer, and S. Michel. Reverse engineering top-k join queries. In BTW, pages 61–80, 2017

    work page 2017

  53. [53]

    Psallidas, B

    F. Psallidas, B. Ding, K. Chakrabarti, and S. Chaudhuri. S4: Top-k spreadsheet-style search for query discovery. In SIGMOD, pages 2001–2016, 2015

    work page 2001

  54. [54]

    L. Qian, M. J. Cafarella, and H. V . Jagadish. Sample-driven schema mapping. In SIGMOD, pages 73–84, 2012

    work page 2012

  55. [55]

    A. D. Sarma, A. G. Parameswaran, H. Garcia-Molina, and J. Widom. Synthesizing view definitions from data. InICDT, pages 89–103, 2010

    work page 2010

  56. [56]

    Y . Shen, K. Chakrabarti, S. Chaudhuri, B. Ding, and L. Novik. Discovering queries based on example tuples. In SIGMOD, pages 493–504, 2014

    work page 2014

  57. [57]

    Srinivasan

    A. Srinivasan. The aleph manual

    work page

  58. [58]

    W. C. Tan, M. Zhang, H. Elmeleegy, and D. Srivastava. Reverse engineering aggregation queries. PVLDB, 10(11):1394–1405, 2017

    work page 2017

  59. [59]

    W. C. Tan, M. Zhang, H. Elmeleegy, and D. Srivastava. REGAL+: reverse engineering SPJA queries. PVLDB, 11(12):1982–1985, 2018

    work page 1982

  60. [60]

    Q. T. Tran, C. Chan, and S. Parthasarathy. Query reverse engineering. VLDBJ, 23(5):721–746, 2014

    work page 2014

  61. [61]

    Vallet, M

    D. Vallet, M. Fern ´andez, P. Castells, P. Mylonas, and Y . Avrithis. Personalized information retrieval in context. In MRC, pages 16–17, 2006

    work page 2006

  62. [62]

    C. Wang, A. Cheung, and R. Bodik. Synthesizing highly expressive sql queries from input-output examples. In PLDI, pages 452–466, 2017

    work page 2017

  63. [63]

    R. C. Wang and W. W. Cohen. Language-independent set expansion of named entities using the web. In ICDM, pages 342–350, 2007

    work page 2007

  64. [64]

    Y . Y . Weiss and S. Cohen. Reverse engineering spj-queries from examples. In PODS, pages 151–166, 2017

    work page 2017

  65. [65]

    http://wordgrabbag.com, 2018

    Word grabbag. http://wordgrabbag.com, 2018

    work page 2018

  66. [66]

    Xiang and J

    R. Xiang and J. Neville. Pseudolikelihood EM for within-network relational learning. In ICDM, pages 1103–1108, 2008

    work page 2008

  67. [67]

    H. Yu, J. Han, and K. C. Chang. PEBL: positive example based learning for web page classification using SVM. In SIGKDD, pages 239–248, 2002

    work page 2002

  68. [68]

    Z. Zeng, M. Lee, and T. W. Ling. Answering keyword queries involving aggregates and GROUPBY on relational databases. In EDBT, pages 161–172, 2016

    work page 2016

  69. [69]

    Zhang, H

    M. Zhang, H. Elmeleegy, C. M. Procopiuc, and D. Srivastava. Reverse engineering complex join queries. In SIGMOD, pages 809–820, 2013

    work page 2013

  70. [70]

    Zhang and Y

    S. Zhang and Y . Sun. Automatically synthesizing sql queries from input-output examples. In ASE, pages 224–234, 2013

    work page 2013

  71. [71]

    Zhang, Y

    X. Zhang, Y . Chen, J. Chen, X. Du, K. Wang, and J. Wen. Entity set expansion via knowledge graphs. In SIGIR, pages 1101–1104, 2017

    work page 2017

  72. [72]

    if the in- put database is changed in a certain way, would the output table change in this way?

    M. M. Zloof. Query-by-example: The invocation and definition of tables and forms. InPVLDB, pages 1–24, 1975. APPENDIX A. DOMAIN SELECTIVITY IMPACT We use the notion of domain coverage of a filterφ⟨A,V,θ⟩ to de- note the fraction of values of A’s domain that V covers. As an ex- ample, for attribute age, suppose that the domain consists of values in the range...

    work page 1975