CrowdSourced Semantic Enrichment for Participatorye-Government ∗

Francesco Di MauroComputer Science Dept.

Univ. of Torinodimauro@di.unito.it

Paolo PasterisComputer Science Dept.

Univ. of Torinopasteris@di.unito.it

Maria Luisa SapinoComputer Science Dept.

Univ. of Torinomlsapino@di.unito.it

K.Selçuk CandanCIDSE

Arizona State Universitycandan@asu.edu

Giovanna Antonella DinoEarth Sciences Dept.

Univ. of Torinogiovanna.dino@unito.it

Piergiorgio RossettiEarth Sciences Dept.

Univ. of Torinopiergiorgio.rossetti@unito.it

ABSTRACTWhen making decisions impacting public utility and encour-aging and/or enforcing behavioral rules, public administra-tors need to rely on data and knowledge supporting theirchoices, which can be used to better inform those citizenswho will be affected by such decisions. Many open datarepositories exist and can be accessed and used by both de-cision makers and citizens. Similarly, semantic tagging isnow commonly used as a way to allow users provide theirown knowledge to be associated to data. In this paper,we present a novel participatory system which allows tra-ditional databases and semantic tagging modules coexistin the same knowledge base, and provides the users withquery enrichment functionalities to enable ontology-basedquery expansion. We describe CroSSE, our CrowdSourcedSemantic Enrichment query system architecture, define theenrichment specification language, and discuss a use case inwhich the proposed technology is being applied in a partici-patory e-government setting. The use case is in the contextof our SmartGround EU funded project, in which a rela-tional database platform is designed to collect data of inter-est concerning secondary raw materials from mines as wellas municipality waste. CroSSE semantic enrichment archi-tecture interacts with this platform to expand queries andresults on the basis of users’ domain knowledge.

CCS Concepts•Information systems → Information integration;Social tagging systems;

∗Partially supported by EU Horizon 2020 Research and In-novation Programme under Grant Agreement No 641988.

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DOI: http://dx.doi.org/10.1145/3012071.3012102

KeywordsCrowdsourced semantic enrichment, semantic tagging andquery expansion, ontologies

1. INTRODUCTIONWhen making decisions impacting public utility and en-

couraging and/or enforcing (possibly unpopular) behavioralrules, public administrators need to also rely on data andknowledge supporting their choices, which can be used tobetter inform those citizens who will be affected by such de-cisions. Often times, decision makers would need to conducthypothetical reasoning, to estimate the implications of ac-tions they are considering, or the impacts of new laws (suchas enforcing some prohibition, or setting new thresholds inthe definition of allowed activities) which they are planningto enact (“What would happen if no more than a certainamount of waste can be shipped to some specific landfill froma given region?”; “Would the available stocking site still besufficient?”; “What if some combination of elements in alandfill was considered dangerous, and its presence wouldtrigger a fine to the manager of the landfill?”; “How manylandfills would be charged high fines?”). We note that, ingeneral, assumptions, that may vary from user to user orfrom location to location, may be seen as defining the con-text within which the database has to be queried: “Assumingthat the presence of some combination of elements in a land-fill might pollute the air in a certain number of kilometersin the neighbourhood, what would be the estimated pollutedarea, given the available data about the waste deposits?”

To allow such decision making, we are developing a par-ticipatory system which allows users to provide their owndomain knowledge, and supports traditional databases andsemantic tagging modules that coexist in the same platform.Moreover, we provide the users with query enrichment func-tionalities to enable user-provided knowledge based queryexpansion, thus combining existing factual data and per-sonal knowledge. This can be exploited to promote digitalinteractions between institutions and citizens, facilitatingtheir involvement in crowd sourced governance processes.In this paper, we describe CroSSE, our CrowdSourcedSemantic Enrichment system architecture, define the enrich-ment specification language, and discuss a use case in whichthe proposed technology is actually applied. The use caseis in the context of our SmartGround EU funded project,

in which a database platform is designed to capture data ofinterest concerning secondary raw materials from mines aswell as municipality waste. The proposed semantic enrich-ment architecture, CroSSE, interacts with this database toexpand queries and personalize results on the basis of users’domain knowledge. The paper is organized as follows. InSection 2 we briefly survey related literature. Section 3 in-troduces the motivating scenario from which the explana-tory examples used along the paper will be taken, and givescontext to the system architecture described in Section 4.Section 5 describes the semantics of the enriched queries,while Section 6 concludes the paper.

2. RELATED WORKData Integration: In general, there are three types ofinformation-integration systems. In source-centric systems,the sources are defined in terms of the global schema andare referred to as local-as-view, or LAV, systems (Informa-tion Manifold [10], Emerac [15]). The LAV approach, whileflexible, assumes a consistent integrated view. An alter-native approach is to define the global schema in terms ofthe sources. This is called global-as-view, or GAV (HER-MES [1], SIMS [2], TSIMMIS [9]), and WEBBASE [6, 7],by Davulcu). In GAV systems, whenever a source changes oris added, the global schema needs to be modified. The thirdclass is a hybrid referred to as a GLAV system [18]. Orches-tra [17] and FICSR [5] are systems that focus on managingdisagreements that arise (at both schema and instance lev-els) during data sharing. In Orchestra, each participant hasa (locally) consistent database instance, containing the setof tuples (possibly originated from other participants) thatit accepts. FICSR creates a data structure that captures allinterpretations of a conflicting database and can provide dif-ferent views, ranked with the user’s individual assumptionsand preferences to different users. [19] offers a survey of dif-ferent DB integration techniques. Among them, mediatedquery systems enable a uniform data access solution by pro-viding a single point for read-only query of heterogeneousdata sources. In this approach, the global query processorsends sub-query to local/distributed sources and managesthe reconciliation of the results.Ontology Management and CrowdSourcing: [13] and[12], focus on crowdsourcing ontology verification and en-gineering, in the biomedical domain. They apply ontologi-cal verification to large biomedical ontologies, in which theclass hierarchy not only is the core structure, but is theonly semantic relationship created by ontology developers.Using a crowdsourcing method for ontology verification (inwhich workers answer computer-generated questions basedon ontology axioms) the hierarchy verification is subdividedin micro-tasks and the results are measured. So crowd-workers can collaborate with the domain experts, improvingthe quality of the enriched ontology, while reputation andaltruism are forms of incentive models.

In [4] the problem of ontology based information reuse isoriented to the realization of knowledge-based digital ecosys-tems. In particular, the authors present techniques based onlinguistic analysis that, starting from the vocabularies con-tained in each source ontology and relating them with theinitial (or proto) ontology, can facilitate the process of on-tology construction, automating the selection and reuse ofexisting data models. [16] presents the NeOn methodologyfor ontology engineering. Without a rigid framework, this

approach considers the ontological development as the con-struction of networks of ontologies, in which resources maybe managed by people in different organizations.Ontology Driven Query Formulation: The Ontology-Based Data Access (ODBA) is the focus of [8], devoted tothe understanding of how reasoning on the ontology affectsthe query answering process. ODBA can be implementedas a three level architecture consisting of the ontology, thedata sources, and the mapping between them. Answers toa query are not only a data structure that collects (in termsof data integration) the various sources, but also include se-mantically rich descriptions of the relevant concepts in thedomain of interest. [14] deals with ontology-driven queryformulation, in which the intensional description of a rela-tional database is mapped to a OWL-DL description, thelanguage in which the domain experts express their specificknowledge. On this common OWL-DL formalization, theuser may formulate ontological queries that are then trans-lated into the corresponding relational SQL statements.

Available ontologies can be used in web site managementand integration scenarios; in particular, [11] describes a SE-mantic portAL (SEAL) which presents a three-layer archi-tecture encompassing: (a) heterogeneous data sources (DB,XML, HTML); (b) a wrapper that aggregates the sources ina common data model; (c) integration modules (and specificmediators for the dynamic case) able to reconcile the datasources. The ontology can offer support to user query tar-geted to different sources, and the intensive use of schemainformation can facilitate the activity of integration, selec-tion and presentation requested by a web tool that is basedon a semantic conceptual model. The central aspect of thisfamily of semantic portals (and other similar system, likeSmartGround) is the help offered to a community of users,each one contributing to the global knowledge base whilealso consuming the common enriched knowledge.

3. MOTIVATING APPLICATIONWithin the context of the SmartGround1 European

project, we are developing a databank platform in whicha broad spectrum of data relevant to decision making in thecontext of waste management are collected. It is widely rec-ognized that large amounts of waste that - if properly recov-ered from industrial, mining and municipal landfills - couldprovide economic gains are instead regularly lost because ofpoor waste management practices. SmartGround aims toenable best practices in the context of reuse of waste ma-terials, through an innovative data collection strategy thatmakes easily accessible information about available wastematerials and their potential reuse opportunities.

The SmartGround databank aims at integrating the al-ready existing information from the national and inter-national databanks (national agencies, public bodies databases, European statistics). The information stored in theplatform might be seen as describing different real word sce-narios, each of them described by a consistent interpretationof the database. Due to the geographic distribution of suchdata sources, the different times at which data have beencollected, and different data recording standards and strate-gies, existing data sources are often heterogeneous, incom-plete and incomparable.

Decision makers will leverage the collected information

1http://www.smart-ground.eu/index.php

FACTUAL

KNOWLEDGE

Smart Ground DB Semantic Tagging

COMMON

Figure 1: Knowledge representation

Figure 2: RDF based knowledge statement insertionat CroSSE

and reason about the implications of the reality the col-lected data represent. They might also perform hypotheticalreasoning, possibly within different contexts representing,for example, the rules and constraints enforced in differentcountries. Moreover some users may enrich the informationstored in the databank with their own knowledge, to per-sonalise queries and reasoning tasks. For example the direc-tor of a specific laboratory might be interested in combiningthe information about the analysis on a landfill (informationstored in the database), with other information relevant toher but not stored in the database (for example, the dataand role in the laboratory of the person who has signed theanalysis report), and might be interested in querying thedatabase in the context of her personal knowledge.

To support such user participation, we are developing asemantic tagging module (see Section 4) in which users caninsert their own knowledge (and possibly share knowledgealready inserted and made available by other users) in theform of RDF statements [3], and a query engine which com-bines SPARQL queries on each user’s knowledge base andSQL queries for the data stored in the relational databank.

3.1 The Case for Semantic EnrichmentFigure 1 briefly sketches the structure of CroSSE plat-

form. We distinguish between (1) data, which are storedin the database and represent factual information shared bythe different partner institutions and taken as certain knowl-edge by all the users, and (2) personal, contextual knowl-edge, which reflects the users’ interpretation of the data, orthe available meta-knowledge that the users might want touse in combination with the stored data. While data arecommonly accepted as true, personal knowledge reflects theusers’ individual experience and know-how. The semantictagging interface provides users the option of inserting theirown knowledge (in the form of RDF statements, see Fig-ure 2) or accepting as their own (part of) the contextualknowledge already inserted by other users. The inserted

Figure 3: Sample fragment from the SmartGrounddatabase

(or acquired from other users’ statements) knowledge en-riches and extends the information already available in thedatabase and the conclusions that can be drawn from it.

Example 3.1. The schema of our SmartGround databaseincludes tables to collect information about the elements,minerals and/or chemical compounds that can be found indifferent mine landfills. While those waste items are de-scribed in terms of their chemical properties and of the avail-able amounts in the various considered landfills, the databaseschema does not capture, for example, information about thedifferent labs which conducted the analyses, and their inter-nal hierarchical organizations. Similarly, information aboutthe fact that some elements (maybe if co-located with someothers) might be considered as pollutant might depend onlocal (to the states or the regions) rules and regulations, fix-ing thresholds for acceptable amounts of specific elements inspace units. The semantic tagging module will allow inter-ested users to extend the knowledge base by annotating datawith such information. Once suitably tagged, the users canquery the database and obtain information about the exis-tence of pollutant elements (extracted from the database) insome areas, according to the analysis conducted by some spe-cific lab (information derived from the semantic knowledgebase). We refer to section 5 for the presentation of the queryformulation and semantics.

Notice that there is no centralized control on the cor-rectness and/or consistency of the crowdsourced knowledge.This is because we aim to give each user the freedom to ex-press her own beliefs, assumptions, or hypotheses about thedomain, and query the database within the context of suchadditional information not readily available in the database.

3.2 The SmartGround Use CaseIn order to illustrate the capabilities of CroSSE semantic

tagging module, we will refer in the following sections to anextremely simplified (and provisional) version of the Smart-Ground databank, which represents a sample data fragmentcapturing the knowledge of what is contained (in basic termsof elements, minerals and chemical compounds) in four minelandfills. Although simple, this database provided a usefulscenario to simulate example queries.

Figure 4: Sample fragment of supporting (partlyuser provided) knowledge base

Figure 5: Landfill and laboratories ontology

An ontology (expressed as RDF statements), formalizingthe common knowledge available in the system along withwhat each user may define as her knowledge, according toher domain of expertise, enriches the factual information inthe database. For example, Figure 4 provides classificationof the possible types of waste that can be found in a minelandfill. Some of these concepts can be a direct referenceto the database content (Mineral, Element, ChemicalCom-pound), while others can be part of the general commonknowledge (Metal) or can be user defined concepts (identi-fied in this example by a dashed frame, as HazardousWaste).The ontology concepts are connected by means of RDF prop-erties which can be predefined (e.g. isA, representing aparent-children relationship) or introduced (and potentiallyshared) by the users of the system (identified by dashed linesin this example, as oreAssemblage).

In the example scenario, the oreAssemblage property (ide-ally defined by a user who has a geology background knowl-edge) associates to a given element/mineral the set of whatother elements/minerals are usually found in the type ofrocks (and hence in the mining waste) from which the el-ement/mineral is extracted (e.g. Cobalt and Copper canusually be found along Nickel, see Figure 4). This informa-tion can thus be exploited to infer the content of a landfilleven when it is not directly specified in the database.

Figure 5 provides another example where the enrichedknowledgebase defines the relationship between the tech-nicians working in a certain lab and the landfills in thedatabase, where the signsReportFor is a user contributedproperty, defined to keep track of the information on whichtechnician has signed the analysis report for a given landfill.

Figure 6 illustrates the part of the ontology that keepstrack of which concepts, properties, and statements havebeen defined by which user. As can be seen in this exam-

Figure 6: Keeping track of users and their contribu-tions to the knowledgebase

DB

KB

SQL

Information

Storage

DB Wrapper

SQL

per

Data Exchange Interface

KB Wrapper

SPARQL SPA

KB

Data Exchange Interface

I f ti

SQM

Figure 7: The architecture of CroSSE

ple, elements can be shared among the users, such as theconcept HazardousWaste and the property oreAssemblagebeing shared by two users. Whenever a user interacts withthe system, only the elements of the ontology which arecommon, shared with or defined by her will be accounted inthe semantic enrichment process. It is important to noticethat the semantics associated to the elements created by theusers is unknown to the system.

4. SYSTEM ARCHITECTUREIn this section, we introduce the architecture of CroSSE

(as reported in Figure 7).

• The commonly shared factual information is stored ina relational database, DB. Figure 3 shows a fragmentof the SmartGround relational database, in which dataabout the different landfills and the elements, mineralsand chemical compounds contained in them are stored.

• The knowledge base, KB, contains a set of RDF state-ments. RDF statements may or may not be shared.Each statement is annotated with information aboutits “source”, the users who inserted it into the systemand the users who have chosen to accept this state-ment as theirs. Given the set of all possible users, U ,the knowledgebase can be logically seen as a mappingwhich associates each user to the set of RDF state-ments she believes in (either by contributing the state-ment directly, or by accepting other users statements):

KB : U → P(KB).

KB(u) = {s = 〈subj, pred, obj〉 | u believes in s}.

For each user, u, the knowledge she will rely on whilequerying the system will be DB ∪KB(u).

With a slight abuse of notation, in this paper we referto KB both as the mapping which associates each userto the statements she believes in and as the globalset of available RDF statements, with the intendedmeaning that KB =

⋃u∈U KB(u)

• The two information stores , DB and KB, need to in-teract, so that each user u can perceive their combina-tion as a single, integrated, data source. The semanticquery module, SQM has the following role:

– it submits SPARQL queries to KB, possibly withinput parameters resulting from an SQL query tothe relational DB;

– it submits SQL queries to DB, possibly with in-put parameters resulting from a SPARQL queryto KB;

• Wrappers provide interface functionalities between theSQM module and the two information stores, possiblygenerating local internal views (see Section 5) whichare used to produce the final answer to the user’s query.

5. QUERIES SEMANTIC ENRICHMENTIn this section, we describe the semantic enrichment pro-

cess, and present examples to highlight how enriched queriescan be used to combine the information stored in a databasewith ontological domain knowledge, exploiting both thecommon knowledge and the user provided notions.

5.1 Query Enrichment ProcessThe basic idea behind query enrichment consists in ex-

ploiting the ontology in order to identify semantic patternsnot directly recognizable in the knowledge contained in thedatabase, thus providing the user a more informative re-sult set, which contains data derived by the common/sharedknowledge along with the one that the user herself put in thesystem. The RDF formalism allows, through the SPARQLlanguage, to navigate the ontology by following the edgesof the graph, thus allowing the definition of complex pathsconnecting various concepts. In particular, the user canchoose one ore more attributes (from a relation involved inthe query or from the query result set) and run a SPARQLquery for any of its values, to obtain a set of replacements(which may or may not contain the initial value according tothe user preferences) to enrich the scope of the query or theresult set itself. The process involves the following steps:

• Step1: The user specifies one of the two supported en-richment modalities: (1) In the first modality, the userformulates an SQL query to the factual database DB,and asks the system to enrich the query by extending orreplacing the values of a subset of the output attributeswith corresponding values resulting from a SPARQLquery to KB. In this first case the enrichment onlyplays a role to introduce new/replace existing values inthe query output; (2) In the second enrichment modal-ity, through a SPARQL statement, the user specifiesconcepts/values that she believes should appear as val-ues for some database attributes (although this maynot be the case in DB). The user intention is to have, asthe result of her SQL query, the very same results shewould have if the specified enrichment values were in-deed stored in the specified database attributes. In this

case, the extension/value replacement is intended tohappen also during the “internal” phases of the queryprocessing, for example when evaluating join opera-tions between different relations.

• Step2: The extension/replacement requirements arespecified. This is obtained by defining a mapping be-tween relational attributes/attribute values from DBand concepts/concept instances from KB.

• Step3: The wrapper creates the temporary views whichare needed to evaluate the specified extended query, inthe required modality.

• Step4: The query is evaluated, results are returned andthe temporary extension views are released.

In the following subsections, we provide details of the abovesteps, and we describe how the user can enrich the result ofa given SQL query by extracting the information containedin the ontology through one or more SPARQL queries.

5.2 Query Enrichment SemanticsIn this subsection we formalize the semantics of the query

enrichment process.

5.2.1 Replacement SetLet QSQL be an SQL query on the database DB and

Aj in relation Ri be an attribute referred to in the query.For semantic enrichment, we allow the user to associate areplacement set for the attribute Ri.Aj

RS(Ri.Aj) = {〈a1, r1,1〉, . . . , 〈a1, r1,n1〉, . . . ,. . . , 〈am, rm,1〉, . . . , 〈am, rm,nm〉},

which specifies, for each constant ak ∈ dom(Aj), the set ofvalues {rk,1, . . . rk,n} to replace ak The replacement set isobtained through a SPARQL query QSPARQL interrogatingthe semantic knowledge base, KB, for replacements for val-ues in the domain of Ri.Aj . The replacement set for thevalues of Ri.Aj are passed back to the DB in the form of areplacement table

RTi,j(V, V′)

Here the attribute V contains the initial values and V ′ thecorresponding replacements. One important aspect to con-sider is that there is no guarantee that the values returnedby the SPARQL query belong to the same domain of theoriginal value, and this is particularly true for the RDF prop-erties which connect concepts of different classes. While thisdoes not represent a critical problem in the enriched SQLquery evaluation (these values will simply not match withany value in the involved relation), the user is expected topay particular attention both in the ontology enrichmentand in the SPARQL query definition in order to guaranteeoverall consistency.

Note that an SQL query to DB consists of differentclauses. The WHERE clause specifies the conditions nec-essary to be satisfied for returning a tuple, whereas the SE-LECT clause specifies the set of attributes to be returned.Enrichment can be applied to both of these clauses as dis-cussed next.

5.2.2 Enrichment by SELECT ClauseThis form of semantic enrichment directly operates on the

results of a given SQL query, where the operations are per-formed upon completion of the query evaluation process.

Table 1: Result sets for the Example 5.1Lname Ename Lname Ename Hazardc mercury c mercury yesc cobalt c cobalt no

(a) Original (b) Enriched

Let Q be a query with A1, . . . , Ar attributes specified in theSELECT clause. Let RES(A1, . . . , Ar) be the result tablecontaining a set of n result tuples:

RES(A1, . . . , Ar) = {〈a11, . . . , a1r〉, . . . , 〈an1 , . . . , anr 〉}

Let us also be given a replacement table RTi(V, V′) (ob-

tained through a SPARQL query QiSPARQL to the database)

for attribute Ai. Given this, we can have two distinct queryenrichment strategies: (a) In schema extension, the resulthas a new attribute combining information from the DBwith information from the KB:

RES′(A1, . . . , Ai, V′, . . . , Ar) = RES 1Ai=V RT

(b) in attribute replacement, however, the original valuesspecified in Ai are replaced with the corresponding valuesspecified in the replacement table:

RES′(A1, . . . , Ai−1, V′, Ai+1, . . . , Ar) =

ΠA1,...,Ai−1,V ′,Ai+1,...,Ar (RES 1Ai=V RT )

It is to be noticed that the first strategy makes sense onlyif a valid value for the additional attribute V ′ can be assignedto each tuple in the result set.

Example 5.1 (Schema Extension). Let us considerthe SmartGround scenario reported in Section 3.2 and letus suppose that the user is initially interested in the con-tent of the landfill ‘c’ in terms of elements, interrogating thesystem through an SQL query QSQL in the form

SELECT Lname,EnameFROM Landfill, EleContWHERE Landfill.Lname = EleCont.Lname AND

Landfill.Lname = ‘c’

The result of the query is reported in Table 1(a).Let us further assume that the user is interested in enrich-

ing the query result by specifying for each returned elementwhether it is potentially hazardous for the environment ornot. In order to do so, a new attribute Hazard is added inthe result set and the value of this result attribute is collectedthrough SPARQL queries to the knowledge base.

RT (Ename , Hazard ) =SELECT ?Ename ?HazardWHERE { ?Ename isA Element .BIND(EXISTS { ?Ename isA HazardousWaste}

as ?Hazard ) }

The result of the enriched query is reported in Table 1(b).

SELECT Lname,Ename,HazardFROM Landfill, EleCont,RTWHERE Landfill.Lname = EleCont.Lname AND

Landfill.Lname = ‘c’ ANDEleCont.Ename = RT.Ename

Example 5.2 (Attribute Replacement). Let us as-sume that the user is interested in the waste content (interms of both elements and minerals) of the landfill ‘b’:

Table 2: Result sets for Example 5.2Lname Waste Lname Wasteb nickel b nickelb zinc b zincb gold b gold

b fluoriteb barite

(a) Original (b) Enriched

RES(LName, Waste ) =SELECT Lname,Ename AS WasteFROM Landfill, EleContWHERE Landfill.Lname = EleCont.Lname AND

Landfill.Lname = ‘b’UNION

SELECT Lname,Mname AS WasteFROM Landfill,MinContWHERE Landfill.Lname = MinCont.Lname AND

Landfill.Lname = ‘b’

The result of this query is reported in Table 2(a). Now letus further assume that the user wants to know what otherelements (or minerals) could be found in the same landfillconsidering the knowledge on how they usually come com-bined in the types of rocks from which they are extractedduring mining operations (a knowledge expressed in the on-tology by the property oreAssemblage, as explained in sec-tion 3.2). This corresponds to the SPARQL query that col-lects for each element or mineral connected to waste resultsthrough the oreAssemblage property:

RT ( Waste1 , Waste2 ) =SELECT ?waste1 ?waste2WHERE {?waste1 oreAssemblage ?waste2}

The result of the enriched query

SELECT Lname,Waste2FROM RES,RTWHERE RES.Waste = RT.Waste1

is reported in Table 2(b). Since, in Figure 4 the Zinc ele-ment is connected to both Fluorite and Barite minerals, theresult set is enriched with two additional tuples, as reportedin Table 2(b). 3

5.2.3 Enrichment by WHERE ClauseAs we discussed earlier, enrichment process may also be

applied to WHERE clause, during the condition evaluation.In this case, depending on whether they appear in positiveor negative conditions, the replaced values contribute in thequery processing by extending or restricting the domains ofthe involved variable(s): Intuitively,

• positive conditions (such as equality) are to be consid-ered as satisfied (i.e. true) whenever they are satisfiedby at least one of the replacement values, while

• negative conditions (such as non-equality) are to beconsidered as satisfied (i.e. true) whenever they arenot satisfied by any of the replacement values.

Replacement of Constants.Let us consider an SQL query posed to the database, DB,

of the form

SELECT . . .FROM . . .WHERE . . . θ(A, ‘c’) . . .

which contains a condition involving an attribute A and aconstant ‘c’. Let RT be the replacement table for this at-tribute. Depending on the nature of the predicate θ, thecondition will be rewritten as follows:

• A = ‘c’: This condition will be rewritten as

A IN (SELECT V ′ FROM RT WHERE V = ‘c’ )

• A 6= ‘c’: This condition will be rewritten as

A NOT IN (SELECT V ′ FROM RT WHERE V = ‘c’ )

• A < ‘c’, A ≤ ‘c’: These conditions will be rewritten as

A < MAX (SELECT V ′ FROM RT WHERE V = ‘c’ )A ≤ MAX (SELECT V ′ FROM RT WHERE V = ‘c’ )

• A > ‘c’, A ≥ ‘c’: These conditions will be rewritten as

A > MIN (SELECT V ′ FROM RT WHERE V = ‘c’ )A ≥ MIN (SELECT V ′ FROM RT WHERE V = ‘c’ )

We next provide an example.

Example 5.3 (Constant Replacement). Supposethat, after extracting the waste content of landfill ‘b’ (Ex-ample 5.2), the user is now interested in the elements andthe minerals contained in all the landfills whose analysisreports were signed by technicians who work for the samelaboratory as the one who signed the report for ‘b’.

As illustrated in Figure 5, this information is formalized inthe ontology by means of the properties signsReportFor andemployedIn. The following SPARQL query identifies a pathon the ontology graph which, starting from ‘b’ navigates thehierarchy defined for the laboratories personnel to identifythe landfills which meet the specified criteria:

RT (Lname , Lname2) =SELECT ?landfill ?landfill1WHERE { ?landfill = b .

?tech1 filesReportFor ?landfill .?tech1 employedIn ?lab .?tech2 employedIn ?lab .?tech2 filesReportFor ?landfill1 }

The replacement table RT (Table 3(a)), containing the re-sults of the SPARQL query, is then exploited for the queryenrichment:

SELECT Landfill.Lname,EleCont.Ename AS wasteFROM Landfill, EleContWHERE Landfill.Lname = EleCont.Lname AND

Landfill.Lname IN (SELECT Lname2 FROM RT )UNIONSELECT Landfill.Lname,MinCont.Mname AS wasteFROM Landfill,MinContWHERE Landfill.Lname = MinCont.Mname AND

landfill.Lname IN (SELECT Lname2 FROM RT )

Table 3(b) contains the result of this enriched query. 3

Replacement of Variables.Consider an SQL query to the database, DB, of the form

SELECT . . .FROM . . .WHERE . . . θ(A,B) . . .

Table 3: RT table and result set for Example 5.3Lname Lname2 Lname wasteb b b goldb c b nickelb d b zinc

c cobaltc mercuryd irond zinc

(a) (b)

which contains a condition involving attributes A and B.Let RTB be the replacement table for attribute B. Depend-ing on the nature of the predicate θ, the condition will berewritten as follows:

• A = B: This condition will be rewritten as

A IN (SELECT V ′ FROM RTB WHERE V = B )

• A 6= B: This condition will be rewritten as

A NOT IN (SELECT V ′ FROM RTB WHERE V = B )

• A < B, A ≤ B: These conditions will be rewritten as

A < MAX (SELECT V ′ FROM RTB WHERE V = B )A ≤ MAX (SELECT V ′ FROM RTB WHERE V = B )

• A > B, A ≥ B: These conditions will be rewritten as

A > MIN (SELECT V ′ FROM RTB WHERE V = B )A ≥ MIN (SELECT V ′ FROM RTB WHERE V = B )

Let us next consider the case where both attributes involvedin the condition evaluation are provided with replacementtables, RTA and RTB . In this case, the condition will bewritten as follows:

• A = B: This condition will be rewritten as

IS NOTEMPTY((SELECT V ′ FROM RTA WHERE V = A)

INTERSECT(SELECT V ′ FROM RTB WHERE V = B ) )

• A 6= B: This condition will be rewritten as

IS EMPTY((SELECT V ′ FROM RTA WHERE V = A)

INTERSECT(SELECT V ′ FROM RTB WHERE V = B ) )

• A < B: This condition will be rewritten as

MIN (SELECT V ′ FROM RTA WHERE V = A)< MAX (SELECT V ′ FROM RTB WHERE V = B )

A ≤ B is also rewritten similarly.

• A > B: This condition will be rewritten as

MAX (SELECT V ′ FROM RTA WHERE V = A)> MIN (SELECT V ′ FROM RTB WHERE V = B )

A ≥ B is also rewritten similarly.

We next provide an example.

Table 4: Result for the SQL query of Example 5.4Lname1 Lname2 Ename

a d irond a ironb d zincd b zinc

Table 5: RT table for Example5.4Element Element1nickel nickelnickel cobaltnickel copperzinc zinczinc baritezinc fluorite

Example 5.4 (Variable Replacement). As an ex-ample, let us consider the case in which the user is interestedin selecting from the database the landfills that contain thesame elements. The following SQL query to the DB returnsthe required information, as shown in Table 4.

SELECT EleCont1.Lname AS Lname1, EleCont2.LnameAS Lname2 , EleCont1.Ename

FROM EleCont AS EleCont1, EleCont AS EleCont2WHERE EleCont1.Ename = EleCont2.EnameAND EleCont1.Lname 6= EleCont2.Lname

Let us further assume that the user is interested in ex-tending the result set by also including those elements that,although not listed in the DB as being contained in the con-sidered landfills, are declared as possibly contained in themin the knowledgebase, as stated by the oreAssemblage prop-erty in the crowdsourced knowledgebase:

RT( Element , Element1 ) =SELECT ?element ?element1WHERE { ?element oreAssemblage ?element1 }

RT (Element,Element1), is shown in Table 5.The SQL query satisfying the extension request is the fol-

lowing.

SELECT EleCont1.Lname AS Lname1, EleCont2.LnameAS Lname2, EleCont1.Ename

FROM EleCont AS EleCont1, EleCont AS EleCont2WHERE EleCont1.Ename IN (

SELECT RT.Element1 FROM RTWHERE RT.Element = EleCont2.Ename)

AND EleCont1.Lname 6= EleCont2.landfill

Table 6 presents the extended results, including the infor-mation extracted directly from the database, and the infor-mation extracted leveraging the ontological knowledge. 3

6. CONCLUSIONSIn this paper, we presented CroSSE, our Crowd Sourced

Semantic Enrichment query system architecture, defined theenrichment specification language, and discussed a use casein the context of our SmartGround EU funded project. Wefocused on a declarative presentation of CroSSE’s function-alities. As future work we are planning to focus on oper-ational optimization oriented issues, including the study ofhow the materialization of the replacement table RT can beavoided, for optimization purposes.

Table 6: Extended result set for Example 5.4Lname1 Lname2 Ename

c b cobalta b coppera d irond a ironb d zincd b zinc

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