Provenance for Natural Language Queries

Daniel DeutchTel Aviv University

danielde@post.tau.ac.il

Nave FrostTel Aviv University

navefrost@mail.tau.ac.il

Amir GiladTel Aviv University

amirgilad@mail.tau.ac.il

ABSTRACTMultiple lines of research have developed Natural Language(NL) interfaces for formulating database queries. We buildupon this work, but focus on presenting a highly detailedform of the answers in NL. The answers that we presentare importantly based on the provenance of tuples in thequery result, detailing not only the results but also theirexplanations. We develop a novel method for transformingprovenance information to NL, by leveraging the original NLquery structure. Furthermore, since provenance informationis typically large and complex, we present two solutions forits effective presentation as NL text: one that is based onprovenance factorization, with novel desiderata relevant tothe NL case, and one that is based on summarization. Wehave implemented our solution in an end-to-end system sup-porting questions, answers and provenance, all expressed inNL. Our experiments, including a user study, indicate thequality of our solution and its scalability.

1. INTRODUCTIONDeveloping Natural Language (NL) interfaces to database

systems has been the focus of multiple lines of research (seee.g. [35, 4, 34, 48]). In this work we complement these ef-forts by providing NL explanations to query answers. Theexplanations that we provide elaborate upon answers withadditional important information, and are helpful for under-standing why does each answer qualify to the query criteria.

As an example, consider the Microsoft Academic Searchdatabase (http://academic.research.microsoft.com) andconsider the NL query in Figure 1a. A state-of-the-art NLquery engine, NaLIR [35], is able to transform this NL queryinto the SQL query also shown (as a Conjunctive Query,which is the fragment that we focus on in this paper) in Fig-ure 1b. When evaluated using a standard database engine,the query returns the expected list of organizations. How-ever, the answers (organizations) in the query result lackjustification, which in this case would include the authorsaffiliated with each organization and details of the papers

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. To view a copyof this license, visit http://creativecommons.org/licenses/by-nc-nd/4.0/. Forany use beyond those covered by this license, obtain permission by emailinginfo@vldb.org.Proceedings of the VLDB Endowment, Vol. 10, No. 5Copyright 2017 VLDB Endowment 2150-8097/17/01.

return the organization of authors who published papersin database conferences after 2005

(a) NL Query

query(oname) :- org(oid, oname), conf(cid, cname),pub(wid, cid, ptitle, pyear), author(aid, aname, oid),domainConf(cid, did), domain(did, dname),writes(aid, wid), dname = ’Databases’, pyear > 2005

(b) CQ Q

Figure 1: NL Query and CQ Q

TAU is the organization of Tova M. who published’OASSIS...’ in SIGMOD in 2014

Figure 2: Answer For a Single Assignment

they have published (their titles, their publication venuesand publication years). Such additional information, corre-sponding to the notion of provenance [28, 7, 9, 25, 26] canlead to a richer answer than simply providing the names oforganizations: it allows users to also see relevant details ofthe qualifying organizations. Provenance information is alsovaluable for validation of answers: a user who sees an orga-nization name as an answer is likely to have a harder timevalidating that this organization qualifies as an answer, thanif she was presented with the full details of publications.

We propose a novel approach of presenting provenance in-formation for answers of NL queries, again as sentences inNatural Language. Continuing our running example, Fig-ure 2 shows one of the answers outputted by our system inresponse to the NL query in Figure 1a.

Our solution consists of the following key contributions.

Provenance Tracking Based on the NL Query Structure.A first key idea in our solution is to leverage the NL querystructure in constructing NL provenance. In particular, wemodify NaLIR1 so that we store exactly which parts of the NLquery translate to which parts of the formal query. Then,we evaluate the formal query using a provenance-aware en-gine (we use SelP [16]), further modified so that it storeswhich parts of the query “contribute” to which parts of theprovenance. By composing these two “mappings” (text-to-query-parts and query-parts-to-provenance) we infer whichparts of the NL query text are related to which provenance

1We are extremely grateful to Fei Li and H.V. Jagadish forgenerously sharing with us the source code of NaLIR, andproviding invaluable support.

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parts. Finally, we use the latter information in an “inverse”manner, to translate the provenance to NL text.

Factorization. A second key idea is related to the prove-nance size. In typical scenarios, a single answer may havemultiple explanations (multiple authors, papers, venues andyears in our example). A naıve solution is to formulate andpresent a separate sentence corresponding to each expla-nation. The result will however be, in many cases, verylong and repetitive. As observed already in previous work[8, 42], different assignments (explanations) may have sig-nificant parts in common, and this can be leveraged in afactorization that groups together multiple occurrences. Inour example, we can e.g. factorize explanations based onauthor, paper name, conference name or year. Importantly,we impose a novel constraint on the factorizations that welook for (which we call compatibility), intuitively capturingthat their structure is consistent with a partial order definedby the parse tree of the question. This constraint is neededso that we can translate the factorization back to an NLanswer whose structure is similar to that of the question.Even with this constraint, there may still be exponentiallymany (in the size of the provenance expression) compatiblefactorizations, and we look for the factorization with mini-mal size out of the compatible ones; for comparison, previouswork looks for the minimal factorization with no such “com-patibility constraint”. The corresponding decision problemremains coNP-hard (again in the provenance size), but wedevise an effective and simple greedy solution. We furthertranslate factorized representations to concise NL sentences,again leveraging the structure of the NL query.

Summarization. We propose summarized explanations byreplacing details of different parts of the explanation by theirsynopsis, e.g. presenting only the number of papers pub-lished by each author, the number of authors, or the overallnumber of papers published by authors of each organization.Such summarizations incur by nature a loss of informationbut are typically much more concise and easier for usersto follow. Here again, while provenance summarization hasbeen studied before (e.g. [3, 44]), the desiderata of a sum-marization needed for NL sentence generation are different,rendering previous solutions inapplicable here. We observea tight correspondence between factorization and summa-rization: every factorization gives rise to multiple possiblesummarizations, each obtained by counting the number ofsub-explanations that are “factorized together”. We providea robust solution, allowing to compute NL summarizationsof the provenance, of varying levels of granularity.

Implementation and Experiments. We have implementedour solution in a system prototype called NLProv [15], form-ing an end-to-end NL interface to database querying wherethe NL queries, answers and provenance information are allexpressed in Natural Language. We have further conductedextensive experiments whose results indicate the scalabilityof the solution as well as the quality of the results, the latterthrough a user study.

2. PRELIMINARIESWe provide here the necessary preliminaries on Natural

Language Processing, conjunctive queries and provenance.

return

object

verb mod

properties

nsubj

others

(a) Verb Mod.

return

object

non-verb mod

propertiesothers

(b) Non-Verb Mod.

Figure 3: Abstract Dependency Trees

2.1 From NL to Formal QueriesWe start by recalling some basic notions from NLP, as

they pertain to the translation process of NL queries to aformal query language. A key notion that we will use is thatof the syntactic dependency tree of NL queries:

Definition 2.1. A dependency tree T = (V,E, L) is anode-labeled tree where labels consist of two components, asfollows: (1) Part of Speech (POS): the syntactic role of theword [32, 36] ; (2) Relationship (REL): the grammatical re-lationship between the word and its parent in the dependencytree [37].

We focus on a sub-class of queries handled by NaLIR,namely that of Conjunctive Queries, possibly with compar-ison operators (=, >,<) and logical combinations thereof(NaLIR further supports nested queries and aggregation).The corresponding NL queries in NaLIR follow one of thetwo (very general) abstract forms described in Figure 3: anobject (noun) is sought for, that satisfies some properties,possibly described through a complex sub-sentence rootedby a modifier (which may or may not be a verb, a distinc-tion whose importance will be made clear later).

(oname, TAU)

(aname, Tova M.)

(ptitle, OASSIS...)

(cname, SIGMOD)

(pyear, 2014)

return

organizationPOS=NN, REL=dobj

ofPOS=IN, REL=prep

authorsPOS=NNS, REL=pobj

publishedPOS=VBD, REL=rcmod

in

conferencesPOS=NNS, REL=pobj

databasePOS=NN, REL=nn

afterPOS=IN, REL=prep

2005POS=CD, REL=pobj

paperswho

the

(a) Query Tree

organization

of

Tova M.

published

in

SIGMOD

in

2014

’OASSIS...’who

TAU (is the)

(b) Answer Tree

Figure 4: Question and Answer Trees

Example 2.2. Reconsider the NL query in Figure 1a; itsdependency tree is depicted in Figure 4a (ignore for now thearrows). The part-of-speech (POS) tag of each node reflectsits syntactic role in the sentence – for instance, “organiza-tion” is a noun (denoted “NN”), and “published” is a verb inpast tense (denoted “VBD”). The relation (REL) tag of each

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node reflects the semantic relation of its sub-tree with its par-ent. For instance, the REL of “of” is prep (“prepositionalmodifier”) meaning that the sub-tree rooted at “of” describesa property of “organization” while forming a complex sub-sentence. The tree in Figure 4a matches the abstract treein Figure 3b since “organization” is the object and “of” isa non-verb modifier (its POS tag is IN, meaning “preposi-tion or subordinating conjunction”) rooting a sub-sentencedescribing “organization”.

The dependency tree is transformed by NaLIR, based alsoon schema knowledge, to SQL. We focus in this work on NLqueries that are compiled into Conjunctive Queries (CQs).

Example 2.3. Reconsider our running example NL queryin Figure 1a; a counterpart Conjunctive Query is shown inFigure 1b. Some words of the NL query have been mappedby NaLIR to variables in the query, e.g., the word “orga-nization” corresponds to the head variable (oname). Ad-ditionally, some parts of the sentence have been compliedto boolean conditions based on the MAS schema, e.g., thepart “in database conferences” was translated to dname =′

Databases′ in the CQ depicted in Figure 1b. Figure 4ashows the mapping of some of the nodes in the NL querydependency tree to variables of Q (ignore for now the valuesnext to these variables).

The translation performed by NaLIR from an NL queryto a formal one can be captured by a mapping from (some)parts of the sentence to parts of the formal query.

Definition 2.4. Given a dependency tree T = (V,E, L)and a CQ Q, a dependency-to-query-mappingτ : V → V ars(Q) is a partial function mapping a subset ofthe dependency tree nodes to the variables of Q.

2.2 ProvenanceAfter compiling a formal query corresponding to the user’s

NL query, we evaluate it and keep track of provenance, to beused in explanations. To define provenance, we first recallthe standard notion of assignments for CQs.

Definition 2.5. An assignment α for a query Q ∈ CQwith respect to a database instance D is a mapping of therelational atoms of Q to tuples in D that respects relationnames and induces a mapping over variables/constants, i.e.if a relational atom R(x1, ..., xn) is mapped to a tuple R(a1,..., an) then we say that xi is mapped to ai (denoted α(xi) =ai, overloading notations) and we further say that the tuplewas used in α. We require that any variable will be mappedto a single value, and that any constant will be mapped toitself. We further define α(head(Q)) as the tuple obtainedfrom head(Q) by replacing each occurrence of a head variablexi by α(xi).

Assignments allow for defining the semantics of CQs: atuple t is said to appear in the query output if there existsan assignment α s.t. t = α(head(Q)). They will also beuseful in defining provenance below.

Example 2.6. Consider again the query Q in Figure 1band the database in Figure 6. The tuple (TAU) is an outputof Q when assigning the highlighted tuples to the atoms of Q.As part of this assignment, the tuple (2, TAU) (the second

(oname,TAU)·(aname,Tova M.)·(ptitle,OASSIS...)·(cname,SIGMOD)·(pyear,14’)+(oname,TAU)·(aname,Tova M.)·(ptitle,Querying...)·(cname,VLDB)·(pyear,06’)+(oname,TAU)·(aname,Tova M.)· (ptitle,Monitoring..)·(cname,VLDB)·(pyear,07’)+(oname,TAU)·(aname,Slava N.)·(ptitle,OASSIS...)·(cname, SIGMOD)·(pyear,14’)+(oname,TAU)·(aname,Tova M.)·(ptitle,A sample...)·(cname,SIGMOD)·(pyear,14’)+(oname,UPENN)·(aname,Susan D.)·(ptitle,OASSIS...)·(cname,SIGMOD)·(pyear,14’)

Figure 5: Value-level Provenance

tuple in the org table) and (4, Tova M., 2) (the second tupleof the author table) are assigned to the first and second atomof Q, respectively. In addition to this assignment, there are4 more assignments that produce the tuple (TAU) and oneassignment that produces the tuple (UPENN).

We next leverage assignments in defining provenance, in-troducing a simple value-level model. The idea is that as-signments capture the reasons for a tuple to appear in thequery result, with each assignment serving as an alternativesuch reason (indeed, the existence of a single assignmentyielding the tuple suffices, according to the semantics, for itsinclusion in the query result). Within each assignment, wekeep record of the value assigned to each variable, and notethat the conjunction of these value assignments is requiredfor the assignment to hold. Capturing alternatives throughthe symbolic “ + ” and conjunction through the symbolic“ · ”, we arrive at the following definition of provenance assum of products.

Definition 2.7. Let A(Q,D) be the set of assignmentsfor a CQ Q and a database instance D. We define the value-level provenance of Q w.r.t. D as∑

α∈A(Q,D)

Π{xi,ai|α(xi)=ai}(xi, ai)

.

Rel. orgoid oname1 UPENN2 TAU

Rel. authoraid aname oid3 Susan D. 14 Tova M. 25 Slava N. 2

Rel. pubwid cid ptitle pyear6 10 “OASSIS...” 20147 10 “A sample...” 20148 11 “Monitoring...” 20079 11 “Querying...” 2006

Rel. writesaid wid4 63 65 64 74 84 9

Rel. confcid cname10 SIGMOD11 VLDB

Rel. domainConfcid did10 1811 18

Rel. domaindid name18 Databases

Figure 6: DB Instance

Example 2.8. Re-consider our running example query andconsider the database in Figure 6. The value-level prove-nance is shown in Figure 5. Each of the 6 summands standsfor a different assignment (i.e. an alternative reason for thetuple to appear in the result). Assignments are represented

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as multiplication of pairs of the form (var, val) so that varis assigned val in the particular assignment. We only showhere variables to which a query word was mapped; these willbe the relevant variables for formulating the answer.

By composing the dependency-to-query-mapping (recallDefinition 2.4) from the NL query’s dependency tree to queryvariables, and the assignments of query variables to valuesfrom the database, we associate different parts of the NLquery with values. We will use this composition of map-pings throughout the paper as a means of assembling theNL answer to the NL query.

Example 2.9. Continuing our running example, considerthe assignment represented by the first monomial of Figure5. Further reconsider Figure 4a, and now note that eachnode is associated with a pair (var, val) of the variable towhich the node was mapped, and the value that this variablewas assigned in this particular assignment. For instance,the node “organization” was mapped to the variable onamewhich was assigned the value “TAU”.

3. FIRST STEP: A SINGLE ASSIGNMENTWe now start describing our transformation of provenance

to NL. We show it first for the case where the query has asingle assignment with respect to the input database. Thesolution will serve as a building block for the general case ofmultiple assignments.

3.1 Basic SolutionWe follow the structure of the NL query dependency tree

and generate an answer tree with the same structure byreplacing/modifying the words in the question with the val-ues from the result and provenance that were mapped usingthe dependency-to-query-mapping and the assignment. Yet,note that simply replacing the values does not always resultin a coherent sentence, as shown in the following example.

Example 3.1. Re-consider the dependency tree depictedin Figure 4a. If we were to replace the value in the orga-nization node to the value “TAU” mapped to it, the word“organization” will not appear in the answer although it isneeded to produce the coherent answer depicted in Figure 2.Without this word, it is unclear how to deduce the informa-tion about the connection between “Tova M.” and “TAU”.

We next account for these difficulties and present an algo-rithm that outputs the dependency tree of a detailed answer;we will further translate this tree to an NL sentence.

Recall that the dependency tree of the NL query followsone of the abstract forms in Figure 3. We distinguish be-tween two cases based on nodes whose REL (relationshipwith parent node) is modifier; in the first case, the clausebegins with a verb modifier (e.g., the node “published” inFig. 4a is a verb modifier) and in the second, the clausebegins with a non-verb modifier (e.g., the node “of” in Fig.4a is a non-verb modifier). Algorithm 1 considers these twoforms of dependency tree and provides a tailored solution foreach one in the form of a dependency tree that fits the cor-rect answer structure. It does so by augmenting the querydependency tree into an answer tree.

The algorithm operates as follows. We start with the de-pendency tree of the NL query, an empty answer tree TA,a dependency-to-query-mapping an assignment and a node

object from the query tree. We denote the set of all mod-ifiers by MOD and the set of all verbs by V ERB. Thealgorithm is recursive and handles several cases, dependingon object and its children in the dependency tree. If thenode object is a leaf (Line 2), we replace it with the valuemapped to it by dependency-to-query-mapping and the as-signment, if such a mapping exists. Otherwise (it is a leafwithout a mapping), it remains in the tree as it is. Second,if L(object).REL is a modifier (Line 5), we call the proce-dure Replace in order to replace its entire subtree with thevalue mapped to it and add the suitable word for equal-ity, depending on the type of its child (e.g., location, year,etc. taken from the pre-prepared table), as its parent (usingprocedure AddParent). The third case handles a situationwhere object has a non-verb modifier child (Line 9). We usethe procedure Adjust with a false flag to copy TQ into TA,remove the return node and add the value mapped to objectas its child in TA. The difference in the fourth case (Line 12)is the value of flag is now true. This means that instead ofadding the value mapped to object as its child, the Adjustprocedure replaces the node with its value. Finally, if objecthad a modifier child child (verb or non-verb), the algorithmmakes a recursive call for all of the children of child (Line16). This recursive call is needed here since a modifier nodecan be the root of a complex sub-tree (recall Example 2.2).

Algorithm 1: ComputeAnswerTree

input : A dependency tree TQ, an answer tree TA(empty in the first call), adependency-to-query-mapping τ , an assignmentα, a node object ∈ TQ

output: Answer tree with explanations TA

1 child ..= null;2 if object is a leaf then3 value = α(τ(object));4 Replace(object, value, TA);

5 else if L(object).REL is mod then6 value = α(τ(childTQ

(object)));

7 Replace(tree(object), value, TA);8 AddParent(TA, value);

9 else if object has a child v s.t. L(v).REL ∈MOD andL(v).POS /∈ V ERB then

10 Adjust(TQ, TA, τ, α, object, false);11 child ..= v;

12 else if object has a child v s.t. L(v).REL ∈MOD andL(v).POS ∈ V ERB then

13 Adjust(TQ, TA, τ, α, object, true);14 child ..= v;

15 if child 6= null then16 foreach u ∈ childrenTQ

(child) do17 ComputeAnswerTree(TQ, TA, τ, α, u);

18 return TA;

Example 3.2. Re-consider Figure 4a, and note the map-pings from the nodes to the variables and values as reflectedin the boxes next to the nodes. To generate an answer, wefollow the NL query structure, “plugging-in” mapped databasevalues. We start with “organization”, which is the firstobject node. Observe that “organization” has the child “of”which is a non-verb modifier, so we add “TAU” as its childand assign true to the hasMod variable. We then reachLine 15 where the condition holds and we make a recursive

580

call to the children of “of”, i.e., the node object is now “au-thors”. Again we consider all cases until reaching the fourth(Line 12). The condition holds since the node “published”is a verb modifier, thus we replace “authors” with “TovaM.”, mapped to it. Then, we make a recursive call for allchildren of “published” since the condition in Line 15 holds.The nodes “who” and “papers” are leaves so they satisfy thecondition in Line 2. Only “papers” has a value mapped to it,so it is replaced by this value (“OASSIS...”). However, thenodes “after” and “in” are modifiers so when the algorithmis invoked with object = “after” (“in”), the second condi-tion holds (Line 5) and we replace the subtree of these nodeswith the node mapped to their child (in the case of “after” itis “2014” and in the case of “in” it is “SIGMOD”) and weattach the node “in” as the parent of the node, in both casesas it is the suitable word for equality for years and locations.We obtain a tree representation of the answer (Fig. 4b).

So far we have augmented the NL query dependency treeto obtain the dependency tree of the answer. The last stepis to translate this tree to a sentence. To this end, we re-call that the original query, in the form of a sentence, wastranslated by NaLIR to the NL query dependency tree. Totranslate the dependency tree to a sentence, we essentially“revert” this process, further using the mapping of NL querydependency tree nodes to (sets of) nodes of the answer.

Example 3.3. Converting the answer tree in Figure 4bto a sentence is done by replacing the words of the NL querywith the values mapped to them, e.g., the word “authors” inthe NL query (Figure 1a) is replaced by “Tova M.” and theword “papers” is replaced by “OASSIS...”. The word “orga-nization” is not replaced (as it remains in the answer tree)but rather the words “TAU is the” are added prior to it, sinceits POS is not a verb and its REL is a modifier. Completingthis process, we obtain the answer shown in Figure 2.

3.2 Logical OperatorsLogical operators (and, or) and the part of the NL query

they relate to will be converted by NaLIR to a logical pred-icate which will be mapped by the assignment to one valuethat satisfies the logical statement. To handle these parts ofthe query, we augment Algorithm 1 as follows: immediatelyfollowing the first case (before the current Line 5), we adda condition checking whether the node object has a logicaloperator (“and” or “or”) as a child. If so, we call ProcedureHandleLogicalOps with the trees TQ and TA, the logical op-erator node as u, the dependency-to-query-mapping τ andthe assignment α. The procedure initializes a set S to storethe nodes whose subtree needs to be replaced by the valuegiven to the logical predicate (Line 2). Procedure HandleL-ogicalOps first locates all nodes in TQ that were mapped bythe dependency-to-query-mapping to the same query vari-able as the sibling of the logical operator (denoted by u).Then, it removes the subtrees rooted at each of their par-ents (Line 8), adds the value (denoted by val) from thedatabase mapped to all of them in the same level as theirparents (Line 9), and finally, the suitable word for equalityis added as the parent of val in the tree by the procedureAddParent (Line 10).

4. THE GENERAL CASEIn the previous section we have considered the case where

the provenance consists of a single assignment. In general,

Procedure HandleLogicalOps

input : A dependency tree TQ, TA, u ∈ VTA,

dependency-to-query-mapping τ and anassignment α

1 w ← parentTQ(u);

2 S ← {w};3 var ← τ(childrenTA

(w) \ u);4 val← α(τ(childrenTA

(w) \ u));5 for z ∈ siblingsTA

(w) do6 if z has child mapped by τ to var then7 S.Insert(z);

8 parentTA(w).childrenTA

().Remove(S);9 parentTA

(w).childrenTA().Insert(val);

10 AddParent(TA, val) ;

[TAU] ·

A

([Tova M.] ·

B

([VLDB] ·

([2006] · [Querying...]+ [2007] · [Monitoring...]))

+ [SIGMOD] · [2014] ·([OASSIS...] + [A Sample...]))

B

+ [Slava N.] · [OASSIS...] · [SIGMOD] · [2014])

A

+ [UPENN] · [Susan D.] · [OASSIS...] · [SIGMOD] · [2014]

(a) f1[TAU] ·

([SIGMOD] · [2014] ·([OASSIS...] ·

([Tova M.] + [Slava N.]))+ [Tova M.] · [A Sample...])

+ [VLDB] · [Tova M.] ·([2006] · [Querying...]

+ [2007] · [Monitoring...])+ [UPENN] · [Susan D.] · [OASSIS...] · [SIGMOD] · [2014]

(b) f2Figure 7: Provenance Factorizations

as illustrated in Section 2, it may include multiple assign-ments. We next generalize the construction to account forthis. Note that a naıve solution in this respect is to generatea sentence for each individual assignment and concatenatethe resulting sentences. However, already for the small-scaleexample presented here, this would result in a long and un-readable answer (recall Figure 5 consisting of six assign-ments). Instead, we propose two solutions: the first basedon the idea of provenance factorization [42, 8], and the sec-ond leveraging factorization to provide a summarized form.

4.1 NL-Oriented FactorizationWe start by defining the notion of factorization in a stan-

dard way (see e.g. [42, 17]).

Definition 4.1. Let P be a provenance expression. Wesay that an expression f is a factorization of P if f maybe obtained from P through (repeated) use of some of thefollowing axioms: distributivity of summation over multipli-cation, associativity and commutativity of both summationand multiplication.

Example 4.2. Re-consider the provenance expression inFigure 5. Two possible factorizations are shown in Figure 7,keeping only the values and omitting the variable names forbrevity (ignore the A,B brackets for now). In both cases, theidea is to avoid repetitions in the provenance expression, by

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taking out a common factor that appears in multiple sum-mands. Different choices of which common factor to takeout lead to different factorizations.

How do we measure whether a possible factorization issuitable/preferable to others? Standard desiderata [42, 17]are that it should be short or that the maximal number ofappearances of an atom is minimal. On the other hand, wefactorize here as a step towards generating an NL answer;to this end, it will be highly useful if the (partial) order ofnesting of value annotations in the factorization is consistentthe (partial) order of corresponding words in the NL query.We will next formalize this intuition as a constraint overfactorizations. We start by defining a partial order on nodesin a dependency tree:

Definition 4.3. Given an dependency tree T , we define≤T as the descendant partial order of nodes in T : for eachtwo nodes, x, y ∈ V (T ), we say that x ≤T y if x is a descen-dant of y in T .

Example 4.4. In our running example (Figure 4a) it holdsin particular that authors ≤ organization, 2005 ≤ authors,conferences ≤ authors and papers ≤ authors, but papers,2005 and conferences are incomparable.

Next we define a partial order over elements of a factoriza-tion, intuitively based on their nesting depth. To this end,we first consider the circuit form [6] of a given factorization:

Example 4.5. Consider the partial circuit of f1 in Figure8. The root, ·, has two children; the left child is the leaf“TAU” and the right is a + child whose subtree includes thepart that is “deeper” than “TAU”.

Given a factorization f and an element n in it, we denoteby levelf (n) the distance of the node n from the root ofthe circuit induced by f multiplied by (−1). Intuitively,levelf (n) is bigger for a node n closer to the circuit root.

·

+

·

sub-circuit

Tova M.

·

SIGMOD2014OASSISSlava N.

TAU

Figure 8: Sub-Circuit of f1

Our goal here is to define the correspondence between thelevel of each node in the circuit and the level of its “source”node in the NL query’s dependency tree (note that eachnode in the query corresponds to possibly many nodes inthe circuit: all values assigned to the variable in the differ-ent assignments). In the following definition we will omit thedatabase instance for brevity and denote the provenance ob-tained for a query with dependency tree T by provT . Recallthat dependency-to-query-mapping maps the nodes of thedependency tree to the query variables and the assignmentmaps these variables to values from the database (Defini-tions 2.4, 2.5, respectively).

Definition 4.6. Let T be a query dependency tree, letprovT be a provenance expression, let f be a factorizationof provT , let τ be a dependency-to-query-mapping and let

{α1, ...αn} be the set of assignments to the query. For eachtwo nodes x, y in T we say that x ≤f y if∀i ∈ [n] : levelf (αi(τ(x))) ≤ levelf (αi(τ(y))).

We say that f is T -compatible if each pair of nodes x 6=y ∈ V (T ) that satisfy x ≤T y also satisfy that x ≤f y.

Essentially, T -compatibility means that the partial orderof nesting between values, for each individual assignment,must be consistent the partial order defined by the structureof the question. Note that the compatibility requirementimposes constraints on the factorization, but it is in generalfar from dictating the factorization, since the order x ≤T yis only partial – and there is no constraint on the order ofeach two provenance nodes whose “origins” in the query areunordered. Among the T -compatible factorizations, we willprefer shorter ones.

Definition 4.7. Let T be an NL query dependency treeand let provT be a provenance expression for the answer.We say that a factorization f of provT is optimal if f isT -compatible and there is no T -compatible factorization f ′

of provT such that | f ′ |<| f | (| f | is the length of f).

The following example shows that the T -compatibilityconstraint still allows much freedom in constructing the fac-torization. In particular, different choices can (and some-times should, to achieve minimal size) be made for differentsub-expressions, including ones leading to different answersand ones leading to the same answer through different as-signments.

Example 4.8. Recall the partial order ≤T imposed by ourrunning example query, shown in part in Example 4.4. Itimplies that in every compatible factorization, the organiza-tion name must reside at the highest level, and indeed TAUwas “pulled out” first in Figure 8; similarly the author namemust be pulled out next. In contrast, since the query nodescorresponding to title, year and conference name are un-ordered, we may, within a single factorization, factor oute.g. the year in one part of the factorization and the con-ference name in another one. As an example, Tova M. hastwo papers published in VLDB (“Querying...” and “Mon-itoring”) so factorizing based on VLDB would be the bestchoice for that part. On the other hand, suppose that SlavaN. had two paper published in 2014; then we could factorizethem based on 2014. The factorization could, in that case,look like the following (where the parts taken out for Tovaand Slava are shown in bold):

[TAU] ·([Tova M.] ·([VLDB] ·

([2006] · [Querying...]+ [2007] · [Monitoring...]))

+ [SIGMOD] · [2014] ·([OASSIS...] + [A Sample...]))

+ ([Slava N.] ·([2014] ·

([SIGMOD] · [OASSIS...]+ [VLDB] · [Ontology...])))

The following example shows that in some cases, requiringcompatibility can come at the cost of compactness.

Example 4.9. Consider the query tree T depicted in Fig-ure 4a and the factorizations provT (the identity factoriza-tion) depicted in Figure 5, f1, f2 presented in Figure 7.

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provT is of length 30 and is 5-readable, i.e., the maximalnumber of appearances of a single variable is 5 (see [17]).f1 is of length 20, while the length of f2 is only 19. In ad-dition, both f1 and f2 are 3-readable. Based on those mea-surements f2 seems to be the best factorization, yet f1 isT -compatible with the question and f2 is not. For example,conferences ≤T authors but “SIGMOD” appears higherthan “Tova M.” in f2. Choosing a T -compatible factoriza-tion in f1 will lead (as shown below) to an answer whosestructure resembles that of the question, and thus translatesto a more coherent and fitting NL answer.

Note that the identity factorization is always T -compatible,so we are guaranteed at least one optimal factorization (butit is not necessarily unique). We next study the problem ofcomputing such a factorization.

4.2 Computing FactorizationsRecall that our notion of compatibility restricts the fac-

torizations so that their structure resembles that of the ques-tion. Without this constraint, finding shortest factorizationsis coNP-hard in the size of the provenance (i.e. a booleanexpression) [27]. The compatibility constraint does not re-duce the complexity since it only restricts choices relevantto part of the expression, while allowing freedom for arbi-trarily many other elements of the provenance. Also recall(Example 4.8) that the choice of which element to “pull-out”needs in general to be done separately for each part of theprovenance so as to optimize its size (which is the reason forthe hardness in [27] as well). In general:

Proposition 4.10. Given a dependency tree T , a prove-nance expression provT and an integer k, deciding whetherthere exists a T -compatible factorization of provT of size ≤ kis coNP-hard.

Greedy Algorithm. Despite the above result, the con-straint of compatibility does help in practice, in that we canavoid examining choices that violate it. For other choices, wedevise a simple algorithm that chooses greedily among them.More concretely, the input to Algorithm 2 is the query treeTQ (with its partial order ≤TQ), and the provenance provTQ .The algorithm output is a TQ-compatible factorization f .Starting from prov, the progress of the algorithm is made insteps, where at each step, the algorithm traverses the circuitinduced by prov in a BFS manner from top to bottom andtakes out a variable that would lead to a minimal expressionout of the valid options that keep the current factorizationT -compatible. Naturally, the algorithm does not guaranteean optimal factorization (in terms of length), but performswell in practice (see Section 5).

In more detail, we start by choosing the largest nodes ac-cording to ≤TQ which have not been processed yet (Line 2).Afterwards, we sort the corresponding variables in a greedymanner based on the number of appearances of each variablein the expression using the procedure sortByFrequentV ars(Line 3). In Lines 4–5, we iterate over the sorted variablesand extract them from their sub-expressions. This is donewhile preserving the ≤TQ order with the larger nodes, thusensuring that the factorization will remain TQ-compatible.We then add all the newly processed nodes to the setProcessed which contains all nodes that have already beenprocessed (Line 6). Lastly, we check whether there are nomore nodes to be processed, i.e., if the set Processed in-cludes all the nodes of TQ (denoted V (TQ), see the condition

in Line 7). If the answer is “yes”, we return the factoriza-tion. Otherwise, we make a recursive call. In each such call,the set Processed becomes larger until the condition in Line7 holds.

Algorithm 2: GreedyFactorization

input : TQ - the query tree, ≤TQ- the query partial

order, prov - the provenance, τ, α -dependency-to-query-mapping and assignmentfrom nodes in TQ to provenance variables,Processed - subset of nodes from V (TQ) whichwere already processed (initially, ∅)

output: f - TQ-compatible factorization of provTQ

1 f ← prov;2 Frontier ← {x ∈ V (TQ)|∀(y ∈V (TQ) \ Processed) s.t. x 6≤TQ

y};3 vars← sortByFrequentV ars({α(τ(x))|x ∈Frontier}, f);

4 foreach var ∈ vars do5 Take out var from sub-expressions in f not including

variables from {x|∃y ∈ Processed : x = α(τ(y))};6 Processed← Processed ∪ Frontier;7 if |Processed| = |V (TQ)| then8 return f ;

9 else10 return

GreedyFactorization(TQ, f, τ, α, Processed);

Example 4.11. Consider the query tree TQ depicted inFigure 4a, and provenance prov in Figure 5. As explainedabove, the largest node according to ≤TQ is organization,hence “TAU” will be taken out from the brackets multi-plying all summands that contain it. Afterwards, the nextnode according to the order relation will be author, there-fore we group by author, taking out “Tova M.”, “Slava N.”etc. The following choice (between conference, year and pa-per name) is then done greedily for each author, based onits number of occurrences. For instance, V LDB appearstwice for Tova.M. whereas each paper title and year ap-pears only once; so it will be pulled out. The polynomial[SlavaN.] · [OASSIS...] · [SIGMOD] · [2014] will remain un-factorized as all values appear once. Eventually, the algo-rithm will return the factorization f1 depicted in Figure 7,which is TQ-compatible and much shorter than the initialprovenance expression.

Proposition 4.12. Let f be the output of Algorithm 2for the input dependency tree TQ, then f is TQ-compatible.

Complexity. Denote the provenance size by n. The algo-rithm complexity is O(n2 · logn): at each recursive call, wesort all nodes in O(n · logn) (Line 3) and the we handle (inFrontier) at least one node (in the case of a chain graph) ormore. Hence, in the worst case we would have n recursivecalls, each one costing O(n · logn).

4.3 Factorization to Answer TreeThe final step is to turn the obtained factorization into

an NL answer. Similarly to the case of a single assignment(Section 3), we devise a recursive algorithm that leveragesthe mappings and assignments to convert the query depen-dency tree into an answer tree. Intuitively, we follow the

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structure of a single answer, replacing each node there byeither a single node, standing for a single word of the factor-ized expression, or by a recursively generated tree, standingfor some brackets (sub-circuit) in the factorized expression.

In more detail, the algorithm operates as follows. Weiterate over the children of root (the root of the current sub-circuit), distinguishing between two cases. First, for eachleaf child, p, we first (Line 4) assign to val the databasevalue corresponding to the first element of p under the as-signment α (recall that p is a pair (variable,value)). We thenlookup the node containing the value mapped to p’s variablein the answer tree TA and change its value to val in Lines5, 6 (the value of p). Finally, in Line 7 we reorder nodesin the same level according to their order in the factoriza-tion (so that we output a semantically correct NL answer).Second, for each non-leaf child, the algorithm performs arecursive call in which the factorized answer subtree is com-puted (Line 9). Afterwards, the set containing the nodesof the resulting subtree aside from the nodes of TA are at-tached to TF under the node corresponding to their LCAin TF (Lines 10 – 13). In this process, we attach the newnodes that were placed lower in the circuit in the most suit-able place for them semantically (based on TA), while alsomaintaining the structure of the factorization.

Algorithm 3: ComputeFactAnswerTree

input : α - an assignment to the NL query, TA - answerdependency tree based on α, root - the root ofthe circuit induced by the factorized provenance

output: TF - tree of the factorized answer

1 TF ← copy(TA);2 foreach p ∈ childrenf (root) do3 if p is a leaf then4 val← α(var(p));5 node← Lookup(var(p), α, TA);6 ReplaceV al(val, node, TF );7 Rearrange(node, TA, TF );

8 else9 T recF = ComputeFactAnswerTree(α, TA, p);

10 RecNodes = V (T recF ) \ V (TA);

11 parentrecF ← LCA(recNodes);

12 parentF ← Corresponding node to parentrecF in

TF ;13 Attach recNodes to TF under the parentF ;

14 return TF ;

Example 4.13. Consider the factorization f1 depicted inFigure 7, and the structure of single assignment answer de-picted in Figure 4b which was built based on Algorithm 1.Given this input, Algorithm 3 will generate an answer treecorresponding to the following sentence:

TAU is the organization ofTova M. who published

in VLDB’Querying...’ in 2006 and’Monitoring...’ in 2007

and in SIGMOD in 2014’OASSIS...’ and ’A sample...’

and Slava N. who published’OASSIS...’ in SIGMOD in 2014.

UPENN is the organization of Susan D. who published’OASSIS...’ in SIGMOD in 2014.

Note that the query has two results: “TAU” and “UPENN”.“UPENN” was produced with a single assignment, but there

are 5 different assignments producing “TAU”. We now fo-cus on this sub-circuit depicted in Figure 8. After initializingTF , in Lines 3 – 7 the algorithm finds the value TAU and thenode corresponding to it in TA (which originally containedthe variable organization). It then copies this node to TFand assigns it the value “TAU”. Next the algorithm handlesthe + node with a recursive call in Line 9. This node has thetwo sub-circuits rooted at the two · nodes (Line 8); one con-taining [authors, TovaM.] and the other [authors, SlavaN.].When traversing the sub-circuit containing “Slava N.”, thealgorithm simply copies the subtree rooted at the authorsnode with the values from the circuit and arranges the nodesin the same order as the corresponding variable nodes werein TA (Line 7) as they are all leaves on the same level. Thosevalues will be attached under the LCA “of” (Lines 9 – 13).The sub-circuit of “Tova M.” also has nested sub-circuits.Although the node paper appears before the nodes year andconference in the answer tree structure, the algorithm iden-tifies that f1 extracted the variables “VLDB”, “SIGMOD”and “2014”, so it changes their location so that they appearearlier in the final answer tree. Finally, recursive calls aremade with the sub-circuit containing [authors, TovaM.].

Why require compatibility? We conclude this partof the paper by revisiting our decision to require compati-ble factorizations, highlighting difficulties in generating NLanswers using non-compatible factorizations.

Example 4.14. Consider factorization f2 from Figure 7.“TAU” should be at the beginning of the sentence and fol-lowed by the conference names “SIGMOD” and “VLDB”.The second and third layers of f2 are composed of authornames (“Tova M.”, “Slava N.”), paper titles (“OASSIS”, “Asample...”, “Monitoring...”) and publication years (2007,2014). Changing the original order of the words such thatthe conference name “SIGMOD” and the publication year“2014” will appear before “Tova M.” breaks the sentencestructure in a sense. It is unclear how to algorithmicallytranslate this factorization into an NL answer, since we needto patch the broken structure by adding connecting phrases.One hypothetical option of patching f2 and transforming itinto an NL answer is depicted below. The bold parts of thesentence are not part of the factorization and it is not clearhow to generate and incorporate them into the sentence al-gorithmically. Even if we could do so, it appears that theresulting sentence would be quite convoluted:

TAU is the organization of authors who published inSIGMOD 2014

’OASSIS...’ which was published byTova M. and Slava N.

and Tova M. published ’A sample...’and Tova M. published in VLDB

’Querying...’ in 2014and ’Monitoring...’ in 2007.

UPENN is the organization of Susan D. who published’OASSIS...’ in SIGMOD in 2014

Observe that the resulting sentence is much less clear thanthe one obtained through our approach, even though it wasobtained from a shorter factorization f2; the intuitive reasonis that since f2 is not T -compatible, it does not admit astructure that is similar to that of the question, thus is notguaranteed to admit a structure that is coherent in NaturalLanguage. Interestingly, the sentence we would obtain insuch a way also has an edit distance from the question [18]that is shorter than that of our answer, demonstrating thatedit distance is not an adequate measure here.

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(A) [TAU] · Size([Tova M.],[Slava N.]) · Size([VLDB],[SIGMOD]) ·Size([Querying...],[Monitoring...],[OASSIS...],[A Sample...]) · Range([2006],[2007],[2014])

(B) [TAU]·([Tova M.] ·Size([VLDB],[SIGMOD]) ·Size([Querying...],[Monitoring...],[OASSIS...],[A Sample...]) · Range([2006],[2007],[2014])

[Slava N.] · [OASSIS...] · [SIGMOD] · [2014])Figure 9: Summarized Factorizations

(A) TAU is the organization of 2 authors who published4 papers in 2 conferences in 2006 - 2014.(B) TAU is the organization of Tova M. who published4 papers in 2 conferences in 2006 - 2014 and Slava N.who published ’OASSIS...’ in SIGMOD in 2014.

Figure 10: Summarized Sentences

4.4 From Factorizations to SummarizationsSo far we have proposed a solution that factorizes multiple

assignments, leading to a more concise answer. When thereare many assignments and/or the assignments involve multi-ple distinct values, even an optimal factorized representationmay be too long and convoluted for users to follow.

Example 4.15. Reconsider Example 4.13; if there aremany authors from TAU then even the compact representa-tion of the result could be very long. In such cases we needto summarize the provenance in some way that will preservethe “essence” of all assignments without actually specifyingthem, for instance by providing only the number of authors/-papers for each institution.

To this end, we employ summarization, as follows. First,we note that a key to summarization is understanding whichparts of the provenance may be grouped together. For that,we use again the mapping from nodes to query variables: wesay that two nodes are of the same type if both were mappedto the same query variable. Now, let n be a node in the cir-cuit form of a given factorization f . A summarization of thesub-circuit of n is obtained in two steps. First, we group thedescendants of n according to their type. Then, we summa-rize each group separately. The latter is done in our imple-mentation simply by either counting the number of distinctvalues in the group or by computing their range if the valuesare numeric. In general, one can easily adapt the solutionto apply additional user-defined “summarization functions”such as “greater / smaller than X” (for numerical values) or“in continent Y” for countries.

Example 4.16. Re-consider the factorization f1 from Fig-ure 7. We can summarize it in multiple levels: the highestlevel of authors (summarization “A”), or the level of papersfor each particular author (summarization “B”), or the levelof conferences, etc. Note that if we choose to summarize atsome level, we must summarize its entire sub-circuit (e.g. ifwe summarize for Tova. M. at the level of conferences, wecannot specify the papers titles and publication years).

Figure 9 presents the summarizations of sub-trees for the“TAU” answer, where “size” is a summarization operatorthat counts the number of distinct values and “range” is anoperator over numeric values, summarizing them as theirrange. The summarized factorizations are further convertedto NL sentences which are shown in Figure 10. Summa-rizing at a higher level results in a shorter but less detailedsummarization.

Fact. +Sentence

Fact. +Sentence

Parser(Augmented) NaLIR(Augmented) NaLIR

BuilderQuery Builder

NL Query

NL Query

DBDBSelP

Factorization GenerationSentence

GenerationFact. +MappingFact. +

Mapping

Results + Provenance + MappingResults + Provenance + Mapping

Query + MappingQuery + MappingDep.

TreeDep. Tree

SummarizationSentenceSentenceSentenceSentence Summarized SentenceSummarized Sentence

Figure 11: System Architecture

Table 1: NL queries

Num.Queries

1 Return the homepage of SIGMOD

2 Return the papers whose title contains ’OASSIS’

3 Return the papers which were published inconferences in database area

4 Return the authors who published papers inSIGMOD after 2005

5 Return the authors who published papers inSIGMOD before 2015 and after 2005

6 Return the authors who published papers indatabase conferences

7 Return the organization of authors who publishedpapers in database conferences after 2005

8 Return the authors from TAU who publishedpapers in VLDB

9 Return the area of conferences

10 Return the authors who published papers indatabase conferences after 2005

11 Return the conferences that presented paperspublished in 2005 by authors from organization

12 Return the years of papers publishedby authors from IBM

5. IMPLEMENTATION AND EXPERIMENTS

5.1 System ArchitectureNLProv is implemented in JAVA 8, extending NaLIR. Its

web UI is built using HTML, CSS and JavaScript. It runson Windows 8 and uses MySQL server as its underlyingdatabase management system (the source code is availablein [21]). Figure 11 depicts the system architecture. First,the user enters a query in Natural Language. This NL sen-tence is fed to the augmented NaLIR system which interpretsit and generates a formal query. This includes the followingsteps: a parser [37] generates the dependency tree for the NLquery. Then, the nodes of the tree are mapped to attributesin the tables of the database and to functions, to form a for-mal query. As explained, to be able to translate the resultsand provenance to NL, NLProv stores the mapping from thenodes of the dependency tree to the query variables. Once aquery has been produced, NLProv uses the SelP system [16]to evaluate it while storing the provenance, keeping trackof the mapping of dependency tree nodes to parts of theprovenance. The provenance information is then factorized(see Algorithm 2) and the factorization is compiled to anNL answer (Algorithm 3) containing explanations.

Finally, the factorized answer is shown to the user. Ifthe answer contains excessive details and is too difficult tounderstand, the user may choose to view summarizations.

5.2 User StudyWe have examined the usefulness of the system through

a user study, involving 15 non-expert users. We have pre-sented to each user 6 NL queries, namely No. 1–4, 6, and 7from Table 1 (chosen as a representative sample). We havealso allowed each user to freely formulate an NL query of

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Table 2: Sample use-cases and resultsQuery Single Assignment Multiple Assignments - SummarizedReturn the homepage of SIGMOD http://www.sigmod2011.org/ is

the homepage of SIGMODReturn the authors who publishedpapers in SIGMOD before 2015 andafter 2005

Tova M. published “Auto-completion...” in SIGMOD in2012

Tova M. published 10 papers in SIGMOD in 2006-2014

Return the authors from TAU whopublished papers in VLDB

Tova M. from TAU published“XML Repository...” in VLDB

Tova M. from TAU published 11 papers in VLDB

Return the authors who publishedpapers in database conferences

Tova M. “published Auto-completion...” in SIGMOD

Tova M. published 96 papers in 18 conferences

Return the organization of authorswho published papers in databaseconferences after 2005

TAU is the organization of TovaM. who published ‘OASSIS...’ inSIGMOD in 2014

TAU is the organization of 43 authors who pub-lished 170 papers in 31 conferences in 2006 - 2015

her choice, related to the MAS publication database [38].2 users have not provided a query at all, and for 5 usersthe query either did not parse well or involved aggregation(which is not supported), leading to a total of 98 success-fully performed tasks. For each of the NL queries, users wereshown the NL provenance computed by NLProv for cases ofsingle derivations, factorized and summarized answers formultiple derivations (where applicable). Multiple deriva-tions were relevant in 50 of the 98 cases. Examples of thepresented results are shown in Table 2.

We have asked users three questions about each case, ask-ing them to rank the results on a 1–5 scale where 1 is thelowest score: (1) is the answer relevant to the NL query?(2) is the answer understandable? and (3) is the answer de-tailed enough, i.e. supply all relevant information? (askedonly for answers including multiple assignments).

The results of our user study are summarized in Table 3.In all cases, the user scores were in the range 3–5, with thesummarized explanation receiving the highest scores on allaccounts. Note in particular the difference in understand-ability score, where summarized sentences ranked as signif-icantly more understandable than their factorized counter-parts. Somewhat surprisingly, summarized sentences wereeven deemed by users as being more detailed than factorizedones (although technically they are of course less detailed),which may be explained by their better clarity (users whoranked a result lower on understandability have also tendedto ranked it low w.r.t. level of detail).

Table 3: Users rankingCategory 3 4 5 AverageSingleRelevant 4 10 84 4.82Understandable 7 25 66 4.60

MultipleRelevant 0 7 43 4.86Understandable 4 13 33 4.58Detailed 3 7 40 4.74

SummarizedRelevant 2 2 46 4.88Understandable 3 3 44 4.82Detailed 2 5 43 4.82

5.3 ScalabilityAnother facet of our experimental study includes runtime

experiments to examine the scalability of our algorithms.Here again we have used the MAS database whose total sizeis 4.7 GB, and queries No. 1–12 from Table 1, running thealgorithm to generate NL provenance for each individual an-swer. The experiments were performed on a i7 processor and

Table 4: Computation time (sec.), for the MAS database

QueryQuery Eval.

TimeFact.Time

SentenceGen. Time

NLProvTime

4 0.9 0.038 0.096 0.1345 0.6 0.03 0.14 0.176 33 0.62 2.08 2.77 20.5 1.1 3.1 4.28 2.4 0.001 0.001 0.0029 0.01 0.011 0.001 0.01210 21.3 0.53 2.23 2.7611 53.7 3.18 6.46 9.6412 18.8 3.22 1.73 4.95

32GB RAM with Windows 8. As expected, when the prove-nance includes a single assignment per answer, the runtimeis negligible (this is the case for queries No. 1–3). We thusshow the results only for queries No. 4–12.

Table 4 includes, for each query, the runtime required byour algorithms to transform provenance to NL in factorizedor summarized form, for all query results (as explained inSection 4, we can compute the factorizations independentlyfor each query result). We show a breakdown of the execu-tion time of our solution: factorization time, sentence gener-ation time, and total time incurred by NLProv (we note thatthe time to compute summarizations given a factorizationwas negligible). For indication on the complexity level ofthe queries, we also report the time incurred by standard(provenance-oblivious) query evaluation, using the mySQLengine. We note that our algorithms perform quite wellfor all queries (overall NLProv execution has 16% overhead),even for fairly complex ones such as queries 7, 11, and 12.

Figure 12a (see next page) presents the execution timeof NL provenance computation for an increasing number ofassignments per answer (up to 5000, note that the maxi-mal number in the real data experiments was 4208). Theprovenance used for this set of experiments was such thatthe only shared value in all assignments was the result value,so the factorization phase is negligible in terms of executiontime, taking only about one tenth of the total runtime inthe multiple assignments case. Most computation time hereis incurred by the answer tree structuring. We observe thatthe computation time increased moderately as a function ofthe number of assignments (and is negligible for the case of asingle assignment). The execution time for 5K assignmentswith unique values was 1.5, 2, 1.9, 4.9, 0.006, 0.003, 2.6, 5.3,and 3.7 seconds resp. for queries 4–12. Summarization timewas negligible, less than 0.1 seconds in all cases.

For the second set of experiments, we have fixed the num-ber of assignments per answer at the maximum 5K andchanged only the domain of unique values from which prove-nance expressions were generated. The domain size per an-swer, per query variable varies from 0 to 5000 (this cannot

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exceed the number of assignments). Note that the runningtime increases as a function of the number of unique val-ues: when there are more unique values, there are morecandidates for factorization (so the number of steps of thefactorization algorithm increases), each factorization step isin general less effective (as there are more unique values fora fixed size of provenance, i.e. the degree of value shar-ing across assignments decreases), and consequently the re-sulting factorized expression is larger, leading to a largeroverhead for sentence generation. Indeed, as our breakdownanalysis (Figure 13) shows, the increase in running time oc-curs both in the factorization and in the sentence generationtime. Finally, Figure 12c shows the expected increase in thefactorized expression size w.r.t the number of unique values.

6. RELATED WORK

NL interfaces. Multiple lines of work (e.g. [34, 4, 35,48, 43, 47, 1]) have proposed NL interfaces to formulatedatabase queries, and additional works [20] have focused onpresenting the answers in NL, typically basing their trans-lation on the schema of the output relation. Among these,works such as [4, 35] also harness the dependency tree in or-der to make the translation form NL to SQL by employingmappings from the NL query to formal terms. The work of[33] has focused on the complementary problem of translat-ing SQL queries (rather than their answers or provenance)to NL. To our knowledge, no previous work has focused onformulating the provenance of output tuples in NL. This re-quires fundamentally different techniques (e.g. that of fac-torization and summarization, building the sentence basedon the input question structure, etc.) and leads to answersof much greater detail.

Provenance. The tracking, storage and presentation of prove-nance have been the subject of extensive research in thecontext of database queries, scientific workflows, and others(see e.g. [7, 28, 25, 9, 26, 14, 13, 24]) while the field ofprovenance applications has also been broadly studied (e.g.[16, 39, 45]). A longstanding challenge in this context is thecomplexity of provenance expressions, leading to difficultiesin presenting them in a user-comprehensible manner. Ap-proaches in this respect include showing the provenance ina graph form [46, 40, 29, 19, 14, 12, 2], allowing user controlover the level of granularity (“zooming” in and out [11]),or otherwise presenting different ways of provenance visu-alization [28]. Other works have studied allowing users toquery the provenance (e.g. [31, 30]) or to a-priori requestthat only parts of the provenance are tracked (see for ex-ample [16, 22, 23]). Importantly provenance factorizationand summarization have been studied (e.g., [8, 5, 42, 44]) asmeans for compact representation of the provenance. Usu-ally, the solutions proposed in these works aim at reducingthe size of the provenance but naturally do not account forits presentation in NL; we have highlighted the different con-siderations in context of factorization/summarization in oursetting. We note that value-level provenance was studied in[41, 10] to achieve a fine-grained understanding of the datalineage, but again do not translate the provenance to NL.

7. CONCLUSION AND LIMITATIONSWe have studied in this paper, for the first time to our

knowledge, provenance for NL queries. We have devisedmeans for presenting the provenance information again inNatural Language, in factorized or summarized form.

There are two main limitations to our work. First, a partof our solution was designed to fit NaLIR, and will need to bereplaced if a different NL query engine is used. Specifically,the “sentence generation” module will need to be adapted tothe way the query engine transforms NL queries into formalones; our notions of factorization and summarization areexpected to be easier to adapt to a different engine. Sec-ond, our solution is limited to Conjunctive Queries. Oneof the important challenges in supporting NL provenancefor further constructs such as union and aggregates is theneed to construct a concise presentation of the provenancein NL (e.g. avoiding repetitiveness in the provenance ofunion queries, summarizing the contribution of individualtuples in aggregate queries, etc.).

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Acknowledgements. This research was partially supported by

the Israeli Science Foundation (ISF, grant No. 1636/13), and by

ICRC - The Blavatnik Interdisciplinary Cyber Research Center.

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