Franconi, Enrico

Guagliardo, Paolo

Tessaris, Sergio

Trevisan, Marco

Recent research showed that adopting formal ontologies as a means for accessing heterogeneous data sources has many benefits, in that not only does it provide a uniform and flexible approach to integrating and describing such sources, but it can also support the final user in querying them, thus improving the usability of the integrated system. We introduce a framework that enables access to heterogeneous data sources by means of a conceptual schema and supports the users in the task of formulating a precise query over it. In de- scribing a specific domain, the ontology defines a vocabulary which is often richer than the logical schema of the underlying data and usually closer to the user’s own vocabulary. The ontology can thus be effectively exploited by the user in order to formulate a query that best captures their information need. The user is constantly guided and assisted in this task by an intuitive visual interface, whose intelligence is dynamically driven by reasoning over the ontology. The inferences drawn on the conceptual schema help the user in choosing what is more appropriate with respect to their information need, restricting the possible choices to only those parts of the ontology which are relevant and meaningful in a given context. The most powerful and innovative feature of our framework lies in the fact that not only do not users need to be aware of the underlying organisation of the data, but they are also not required to have any specific knowledge of the vocabulary used in the ontology. In fact, such knowledge can be gradually acquired by using the tool itself, gaining confidence with both the vocabulary and the ontology. Users may also decide to just explore the ontology without actually querying the information system, with the aim of discovering general information about the modelled domain. Another important aspect is that only queries that are logically consistent with the context and the constraints imposed by the ontology can be formulated, since contradictory or redundant pieces of information are not presented to the user at all. This makes user’s choices clearer and simpler, by ruling out irrelevant information that might be distracting and even generate confusion. Furthermore, it also eliminates the often frustrating and time-consuming process of finding the right combination of parts that together constitute a meaningful query. For this reason, the user is free to explore the ontology without the worry of making a “wrong” choice at some point and can concentrate on expressing their information need. Queries can be specified through a refinement process consisting in the iteration of few basic op- erations: the user first specifies an initial request starting with generic terms, then refines or deletes some of the previously added terms or introduces new ones, and iterates the process until the resulting query satisfies their information need. The available operations on the current query include addition, substitution and deletion of pieces of in- formation, and all of them are supported by the reasoning services running over the ontology. In this paper we summarise only the NL aspects of a tool based on those ideas, Quelo; for a complete picture of our ideas and of the tool re- fer to our papers (Franconi et al., 2011; Dongilli et al., 2004; Catarci et al., 2004; Catarci et al., 2005; Dongilli and Franconi, 2006; Franconi et al., 2010). Quelo relies on a web-based client- server architecture.....<br/

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     Edinburgh Research Explorer                                      A natural language ontology-driven query interfaceCitation for published version:Franconi, E, Guagliardo, P, Tessaris, S & Trevisan, M 2011, A natural language ontology-driven queryinterface. in WS 2 Workshop Extended Abstracts, 9th International Conference on Terminology and ArtificialIntelligence, TIA 2011. pp. 43-46.Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher's PDF, also known as Version of recordPublished In:WS 2 Workshop Extended Abstracts, 9th International Conference on Terminology and Artificial Intelligence, TIA2011General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 05. Apr. 2019A natural language ontology-driven query interfaceEnrico Franconi and Paolo Guagliardo and Sergio TessarisKRDB Research Centre, Free University of Bozen-Bolzano, Italylastname@inf.unibz.itMarco TrevisanCELI Language & Information Technology, Torino, Italytrevisan@celi.it1 MotivationsRecent research showed that adopting formal on-tologies as a means for accessing heterogeneousdata sources has many benefits, in that not onlydoes it provide a uniform and flexible approach tointegrating and describing such sources, but it canalso support the final user in querying them, thusimproving the usability of the integrated system.We introduce a framework that enables accessto heterogeneous data sources by means of a con-ceptual schema and supports the users in the taskof formulating a precise query over it. In de-scribing a specific domain, the ontology definesa vocabulary which is often richer than the logicalschema of the underlying data and usually closerto the user’s own vocabulary. The ontology canthus be effectively exploited by the user in orderto formulate a query that best captures their infor-mation need. The user is constantly guided and as-sisted in this task by an intuitive visual interface,whose intelligence is dynamically driven by rea-soning over the ontology. The inferences drawnon the conceptual schema help the user in choos-ing what is more appropriate with respect to theirinformation need, restricting the possible choicesto only those parts of the ontology which are rele-vant and meaningful in a given context.The most powerful and innovative feature of ourframework lies in the fact that not only do notusers need to be aware of the underlying organi-sation of the data, but they are also not requiredto have any specific knowledge of the vocabularyused in the ontology. In fact, such knowledge canbe gradually acquired by using the tool itself, gain-ing confidence with both the vocabulary and theontology. Users may also decide to just explorethe ontology without actually querying the infor-mation system, with the aim of discovering gen-eral information about the modelled domain.Another important aspect is that only queriesthat are logically consistent with the contextand the constraints imposed by the ontology canbe formulated, since contradictory or redundantpieces of information are not presented to theuser at all. This makes user’s choices clearer andsimpler, by ruling out irrelevant information thatmight be distracting and even generate confusion.Furthermore, it also eliminates the often frustrat-ing and time-consuming process of finding theright combination of parts that together constitutea meaningful query. For this reason, the user isfree to explore the ontology without the worry ofmaking a “wrong” choice at some point and canconcentrate on expressing their information need.Queries can be specified through a refinementprocess consisting in the iteration of few basic op-erations: the user first specifies an initial requeststarting with generic terms, then refines or deletessome of the previously added terms or introducesnew ones, and iterates the process until the result-ing query satisfies their information need. Theavailable operations on the current query includeaddition, substitution and deletion of pieces of in-formation, and all of them are supported by thereasoning services running over the ontology.In this paper we summarise only the NL as-pects of a tool based on those ideas, Quelo; fora complete picture of our ideas and of the tool re-fer to our papers (Franconi et al., 2011; Dongilliet al., 2004; Catarci et al., 2004; Catarci et al.,2005; Dongilli and Franconi, 2006; Franconi etal., 2010). Quelo relies on a web-based client-server architecture:1. the tool logic, responsible of “reasoning”over the ontology in order to provide only rel-evant information w.r.t. the current query;43WS 2 Workshop Extended Abstracts, 9th International Conference on Terminology and Artificial Intelligence, TIA 2011, pages 43–46Paris, 10 November 20112. the natural language generation (NLG) en-gine, that given a query and a lexicalisationmap for the ontology produces an Englishsentence; the lexicon is automatically gener-ated from the ontology;3. the user interface (GUI), that provides vi-sual access to the query and editing facilitiesfor it, allowing to interact with the reason-ing sub-system while benefiting from the ser-vices of the NLG engine.An online fully functional demonstrator of Quelois freely accessible at:http://krdbapp.inf.unibz.it:8080/quelo/2 Natural language aspectsThe natural language interface of the tool masksthe composition of a precise query as the com-position of English text describing the equiva-lent information need. Interfaces following thisparadigm are known as “menu-based natural lan-guage interfaces to databases” or “conceptual au-thoring” (see, most notably, (Hallett et al., 2007)).As we have seen before, the users of such systemsedit a query by composing fragments of generatednatural language provided by the system throughcontextual menus. In (Franconi et al., 2010) wedescribe how the natural language rendering of aquery is achieved.We start by defining a particular linear form ofthe query that satisfies certain constraints, neces-sary to represent the elements of the query usinga linear medium, that is, text. The constraints areenforced at the API level to ensure that differentgraphical user interfaces represent the query in ahomologous way. Moreover, a consistent orderingof the query elements needs to be preserved dur-ing the operations for query manipulation to avoidconfusing the end user. The linearised version ofthe query is then used as a guide for the languagegeneration performed by the tool’s NLG engine.The natural language interface (NLI) of the toolrelies on a natural language generation (NLG) sys-tem to produce the textual representation of thequery, following an idea presented in (Tennant etal., 1983) and refined in (Hallett et al., 2007).For the tool’s NLI to work with a specificknowledge base (KB) a lexicon and a templatemap must be provided for it. To ease the burdenof developing these resources from scratch, we letthe system generate them automatically. The func-tionality we implemented allows to produce all theresources necessary to configure our NLI for usewith a new KB, using as a source of data the on-tology itself.2.1 Natural Language Generation moduleNLG systems use techniques from artificial intel-ligence and computational linguistics to producehuman-readable texts out of machine-readabledata. The Query Tool uses NLG to represent thewhole query, along with all the elements that theuser can use to refine it, as English text. The gen-erated text is enriched with links that connect it tothe underlying logical form of the query. This al-lows the user to operate on the query simply byediting an English text.Unlike most NLG systems, ours is built to letthe user determine the structure of the generatedtext by inserting, replacing and removing snippetsof it. While in the classic NLG pipeline the infor-mation to be conveyed in the text and its order isdetermined by the document planning module, inthe Query Tool it is the user who decides both theinformation to be displayed and its arrangement.As the Query Tool is not tailored to any specificdomain, its NLG module is simple enough to beadopted in any context and it is not bundled withall the resources that are needed to generate textout-of-the-box. Therefore, in order to use it on aspecific knowledge base, the system must be pro-vided with a lexicon and a template map. The for-mer contains the words to be used in the generatedtext; the latter is the bridge between the naturallanguage and the knowledge representation lan-guage, associating each concept/role name with ageneration template. Each such template containsthe syntactic and lexical information necessary togenerate a fragment of text representing the asso-ciated concept or role.We selected the syntactic features available inthe templates, hence supported by the generator,in order to keep the system simple while still be-ing expressive enough. For this purpose, we col-lected and analysed a corpus of more than 12.000unique relation identifiers and we partitioned themaccording to the recurring syntactic patterns. Foreach class of the partition, we then proposeda common natural language representation tem-plate. The result of this study is a set of simple44but effective templates for representing most on-tology relations using natural language.During the first stage (microplanning) of thegeneration, linguistic information stored in thetemplate map and in the lexicon blends with thelogic information encoded in the query into a sin-gle structure, known in the NLG literature as textspecification and consisting of a list of syntactictrees with inflected lexemes on its leaves. TheNLG system operates on this structure to aggre-gate groups of adjacent syntactic structures intosingle more complex structures, and to select andreplace existing referring expressions with moreappropriate ones. These two tasks are known inthe literature as aggregation and referring expres-sions generation, respectively. At the same time,the system keeps track of which element of the textspecification is associated with which element (ei-ther a node tag or an edge tag) of the query. An as-sociation holds when the syntactic element is theresult of the instantiation of a template associatedwith the element of the query. These associationsare used for enriching the generated text with linksto the underlying query.The linearisation of the query simplifies the ef-fort required by the referring expressions genera-tion, as referring expressions that need to be re-worked always appear in subject position. Our al-gorithm replaces a subject with a pronoun when-ever the previous sentence had the same sub-ject, otherwise the subject is left unchanged. Al-though ambiguous expressions may occur, ambi-guity is not a crucial issue as these expressionsoriginate from user operations upon a selected el-ement, which always becomes the target of the re-ferring expression. Our aggregation module per-forms simple aggregation tasks such as aggregat-ing sentences with the same subject, eliding thesubject and parts of the verb if it is feasible.Once these operations are completed, the textspecification is ready to be transformed into the fi-nal text. This task, known as surface realisation,produces a list of text tokens, some of which areconnected to edge or node labels. This list is fi-nally fed to the GUI, that displays it to the user.Elements populating the menu for addition andsubstitution operations undergo a similar process-ing. To produce the textual representation of suchan element, the system makes a temporary copyof the portion of query affected by the operation.The operation is carried out on this portion and theresulting structure is fed to the generation pipelineused for entire queries. The outcome of this pro-cess is the text which will appear on the menu.2.2 Generation of lexicon and template mapFor the tool’s NLI to work with a specific knowl-edge base (KB) a lexicon and a template map mustbe provided for it. Devising these resources re-quires an understanding of both the domain ofinterest and basic linguistic notions such as verbtenses, noun genders and countability. We brieflydescribe here how the system can generate theseresources automatically. This technique followsan approach to domain independent generationproposed in (Sun and Mellish, 2006), after thelearning of a rich corpus of relations. The func-tionality we implemented allows to produce all theresources necessary to configure our NLI for usewith a new KB, using as a source of data the on-tology itself. It has to be noted that the processis not completely reliable, therefore system engi-neers must review the result and make the neces-sary corrections.The idea is based on the observation that KBsalready contain some form of linguistic informa-tion. In real-world ontologies, every concept andrelation has a unique identifier (ID), which mostof the times is not just an arbitrary string, but amnemonic chosen by the knowledge engineer todescribe the intended meaning of the identifiedconcept or relation. Moreover, within these IDs,certain syntactic patterns occur more frequentlythan others.In our approach, each relation ID is first tok-enized according to an algorithm that takes advan-tage of the naming conventions used by ontologyengineers. Second, the tokenized ID is fed to acustom part-of-speech tagger built around QTAG(Tufis and Mason, 1998). The resulting taggedtokenized ID is then lightly preprocessed beforebeing finally passed to a transformation rule, cho-sen among thirteen different ones, that produces atemplate for the template map of the NLG system.For the design of the transformation rules, weanalysed our corpus, containing more than 12.000relation IDs, in order to devise a partition of thedomain in terms of syntactic patterns. The classesdefined in this partition are s.t. to each relation ofthe same class can be applied a simple transfor-45mation in order to obtain a template. Each suchtransformation is also a uniform interpretation ofthe intended meaning of each relation ID in theclass. Some care is needed when giving a uni-form interpretation to syntactic patterns, as thereare situations in which the same syntactic pat-tern is to be interpreted differently. For instance,the relation IDs “country of nationality” and “lan-guage of country” share the same syntactic struc-ture, but the first relation should be read as “thecountry of nationality of X is Y”, while the secondas “the language of X is Y”. Each of the thirteenrules we defined corresponds to one class of thepartition, and together they can handle 93% of therelations of the average ontology.The system has been formally evaluated withsome ontologies (e.g., (Ordnance Survey, ; Drum-mond et al., )), contributing 64 unique relations intotal. It is now available online and it has beenused in many different contexts. From the IDsof these relations we automatically generated rela-tion templates, which were then inspected in orderto evaluate their usability in text generation. Theresult of the evaluation revealed that for 42 out of64 relations (65%) the generated template is suit-able for direct use with the Query Tool’s NLI. Theresult suggests that although the generation of thetemplate map is not totally reliable, it is neverthe-less useful in that it speeds up the work of sys-tems engineers, as they do not need to create thewhole map from scratch, but only have to reviewthe generated map and repair eventual errors. Thisimproves the portability of the Query Tool’s NLI,making it faster and easier to switch to a differentknowledge base.ReferencesTiziana Catarci, Paolo Dongilli, Tania Di Mascio, En-rico Franconi, Giuseppe Santucci, and Sergio Tes-saris. 2004. An ontology based visual tool for queryformulation support. In Proc. of the 16th Eur. Conf.on Artificial Intelligence (ECAI 2004).Tiziana Catarci, Paolo Dongilli, Tania Di Mascio, En-rico Franconi, Giuseppe Santucci, and Sergio Tes-saris. 2005. Usability evaluation tests in the Se-WAsIE (SEmantic Webs and AgentS in IntegratedEconomies) project. In Proceedings of the 11th In-ternational Conference on Human-Computer Inter-action (HCII 2005).Paolo Dongilli and Enrico Franconi. 2006. An Intel-ligent Query Interface with Natural Language Sup-port. In Proc. of the 19th Int. Florida ArtificialIntelligence Research Society Conference (FLAIRS2006), Melbourne Beach, Florida, USA, May.Paolo Dongilli, Enrico Franconi, and Sergio Tessaris.2004. Semantics driven support for query formula-tion. In Proc. of the 2004 Description Logic Work-shop (DL 2004).Nick Drummond, Matthew Horridge, Robert Stevens,Chris Wroe, and Sandra Sampaio. Pizza ontology.The University of Manchester.Enrico Franconi, Paolo Guagliardo, and Marco Tre-visan. 2010. An intelligent query interface basedon ontology navigation. In Proc. of the Workshopon Visual Interfaces to the Social and Semantic Web(VISSW 2010), February.Enrico Franconi, Paolo Guagliardo, Marco Trevisan,and Sergio Tessaris. 2011. Quelo: an ontology-driven query interface. In Proceedings of the 24thInternational Workshop on Description Logics (DL2011).Paolo Guagliardo. 2009. Theoretical foundationsof an ontology-based visual tool for query for-mulation support. Technical Report KRDB09-5, KRDB Research Centre, Free Universityof Bozen-Bolzano. http://www.inf.unibz.it/krdb/pub/TR/KRDB09-05.pdf, October.Catalina Hallett, Donia Scott, and Richard Power.2007. Composing questions through conceptual au-thoring. Computational Linguistics, 33(1):105–133.Ordnance Survey. Great britain’s national mappingagency. http://www.ordnancesurvey.co.uk/oswebsite/ontology/.Xiantang Sun and Chris Mellish. 2006. Domain in-dependent sentence generation from RDF represen-tations for the Semantic Web. In Proc. ECAI’06Combined Workshop on Language-Enhanced Edu-cational Technology and Development and Evalua-tion of Robust Spoken Dialogue Systems.Harry R. Tennant, Kenneth M. Ross, Richard M.Saenz, Craig W. Thompson, and James R. Miller.1983. Menu-based natural language understanding.In Proc. 21st Annual Meeting of the Association forComputational Linguistics, pages 151–158. Associ-ation for Computational Linguistics.Marco Trevisan. 2009. A portable menu-guided natu-ral language interface to knowledge bases. Master’sthesis, University of Groningen.Dan Tufis and Oliver Mason. 1998. Tagging Roma-nian texts: a case study for QTAG, a language in-dependent probabilistic tagger. Proc. 1st Int. Conf.on Language Resources and Evaluation (LREC’98),pages 589–596.46

A natural language ontology-driven query interface

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A natural language ontology-driven query interface

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