Abstract. Traditionally, Controlled Natural Languages (CNLs) are de-signed either to avoid ambiguity for human readers, or to facilitate auto-matic semantic analysis, so that texts can be transcoded to a knowledge representation language. CNLs of the second kind have recently been adapted to the requirements of knowledge formation in OWL for the Semantic Web. We suggest in this paper a variant approach based on automatic generation of texts in CNL (as opposed to automatic analy-sis), and argue that this provides the best of both worlds, allowing us to pursue human readability in addition to a precise mapping from texts to a formal language. 

Power, Richard

Rector, Alan

Scott, Donia

Stevens, Robert

English

Richard Power

Robert Stevens

Donia Scott

Alan Rector

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Editing OWL through generated CNL

Open Research Online

Open Research OnlineThe Open University’s repository of research publicationsand other research outputsEditing OWL through generated CNLConference or Workshop ItemHow to cite:Power, Richard; Stevens, Robert; Scott, Donia and Rector, Alan (2009). Editing OWL through generatedCNL. In: Workshop on Controlled Natural Language (CNL 2009), 8-10 Jun 2009, Marettimo Island, Italy.For guidance on citations see FAQs.c© 2009 The AuthorsVersion: Accepted ManuscriptLink(s) to article on publisher’s website:http://attempto.ifi.uzh.ch/site/cnl2009/Copyright and Moral Rights for the articles on this site are retained by the individual authors and/or other copyrightowners. For more information on Open Research Online’s data policy on reuse of materials please consult the policiespage.oro.open.ac.ukEditing OWL through generated CNLRichard Power1, Robert Stevens2, Donia Scott1, and Alan Rector21 Department of ComputingThe Open UniversityMilton Keynes, MK7 6AA, U.K.r.power@open.ac.uk2 School of Computer ScienceThe University of ManchesterManchester, M13 9PL, UKAbstract. Traditionally, Controlled Natural Languages (CNLs) are de-signed either to avoid ambiguity for human readers, or to facilitate auto-matic semantic analysis, so that texts can be transcoded to a knowledgerepresentation language. CNLs of the second kind have recently beenadapted to the requirements of knowledge formation in OWL for theSemantic Web. We suggest in this paper a variant approach based onautomatic generation of texts in CNL (as opposed to automatic analy-sis), and argue that this provides the best of both worlds, allowing us topursue human readability in addition to a precise mapping from texts toa formal language.1 IntroductionSeveral research groups have proposed interfaces for the Semantic Webbased on Controlled Languages [12, 13, 2, 8, 1]. These systems are de-signed for use by domain experts who wish to encode knowledge withouthaving to work directly in formalisms like OWL and RDF, which are de-signed for computer processing and data exchange rather than for easycomprehension by people. After some initial training, authors type insentences drawn from a restricted subset of English (or some other nat-ural language); the input is parsed and transformed into statements inOWL or some other Semantic Web formalism.Our purpose in this paper is to introduce an alternative approach inthe same tradition. We agree with the research groups cited above thatCNL-based interfaces for editing (or viewing) ontologies and other meta-data on the Semantic Web are plausible solutions to an urgent problem.Our distinctive proposal is to avoid any reliance on automatic interpre-tation of human-authored text. The CNL texts representing the contentof a knowledge base are produced not through human authoring butthrough automatic Natural Language Generation (NLG). This bringsseveral immediate advantages: for instance, it eliminates any possibilityof an interpretation error (by the system); it also eliminates any need totrain users to adhere to the CNL grammar. However, the idea is at firstsight paradoxical: if all texts are generated by the system, how does thehuman author convey the desired content?Over the last decade, our group has pioneered a method by which thiscan be done. The key idea is that the author specifies content by directmanipulation of a generated ‘feedback text’ which expresses both thecurrent content of the knowledge base and the options for extending it.By preserving the link between constituents of the feedback text and theformal representation of its meaning, the interface supports editing at thelevel of meaning, not text. The user can select spans linked to individualobjects or events, and perform operations like classification, or assigningvalues to properties, presented through menus (again expressed in theCNL) which pop up when a span is selected. This method, which we havecalled Wysiwym3 or more generically Conceptual Authoring, has beenemployed in several application domains [10, 3, 5, 4] and shown throughevaluation studies to be intuitive for subject-matter experts [6].Two advantages of using NLG have already been mentioned — no in-terpretation errors, minimal user training. A more subtle advantage isthat we can design the CNL with the sole purpose of favouring humanunderstanding: we no longer have to meet requirements resulting fromautomatic interpretation or human authoring. An NLG system could eas-ily support several CNL dialects, some based on other natural languages,allowing multilingual access to the same knowledge base. Different textscould be generated for editing and viewing, favouring precision and easeof manipulation in the first case, and fluency in the second.However, to achieve these benefits we need to overcome a limitation ofthe Conceptual Authoring systems implemented so far. Except for oneor two small experimental prototypes, they are all designed for editingABoxes (asserted facts about individuals) using classes from a fixed TBox(definitions and general statements about classes)4. The purpose of thispaper is to indicate how the theoretical model underlying ConceptualAuthoring can be adapted to cover knowledge bases embracing TBox aswell as ABox [11].2 Editing a knowledge baseConceptual Authoring was originally developed as a way of defining theinput for multilingual NLG, in applications where content could be for-mally represented by an ABox [10]. The method depends on a model forsystematically aligning a generated feedback text to a graph representingthe ABox, with nodes denoting individuals and arcs denoting relationsamong individuals. Events and propositions are treated as individuals, sothat for example the sentence ‘a doctor arrived’ might express an ABoxfragment with two individuals, one belonging to the class PastArrivaland one to the class MedicalDoctor, related by the property hasAgent:PastArrival(e1) & MedicalDoctor(e2) & hasAgent(e1, e2)3 What You See Is What You Meant4 Methods have been proposed for makingWysiwym open-ended by allowing users toadd new terms for atomic classes [7]; these need to be extended in order to supportfull TBox editing with open properties and complex classes.Editing begins by offering the user a place-holder text indicating a con-straint on the individual that embraces the whole sentence (e.g., it mustbe an event or situation). When this span is clicked, a menu pops up of-fering a list of options computed from the specific event/situation classesin the TBox, also expressed in CNL. Typically these options will includefurther place-holder texts constraining the participants in the event. Hereis a typical editing sequence:User action Feedback textChoose New Event [Something happened]Choose from pop-up menu [Someone] shaved [someone]Choose from pop-up menu A barber shaved [someone]Copy the barber individual A barber shaved [someone]Paste on to place-holder A barber shaved himselfThe important point to understand here is that all editing operationsare defined on the ABox graph, not on the text, which is used only asa means of presenting the ABox and the options for updating it. Theediting process depends on a model for aligning a text to a graph, inwhich text spans are mapped to nodes, and span-subspan relations aremapped to arcs.Can a similar process be devised for editing a knowledge base embrac-ing ABox and TBox? The simplest approach, in our view, is to adopt analignment model in which sentences denote axioms, and major sententialconstituents denote classes; editing can then be performed by selectingany span denoting a class, and replacing it by a span denoting anotherclass, chosen as before from a menu of options generated by the sys-tem. ABox assertions can be incorporated into this model by treatingindividuals as enumerated classes containing a single element [11]. As anillustration, consider a description logic with the following resources forconstructing classes and axioms:Description Syntax OWLatomic class AName ANameuniversal class ! Thingenumerated class {a} oneOf(a)exists restriction ∃R.C someValuesFrom(R,C)class inclusion C # D subClassOf(C,D)The author is allowed to add new terms to the knowledge base (individ-ual, class or property names), and must link them to words (drawn froma wide-coverage lexicon) of a syntactic category constrained by the CNL.In a very simple CNL, individuals could be expressed by proper names,classes by count nouns, and properties by transitive verbs. A fixed CNLgrammar for expressing the description language takes care of the rest.A knowledge base comprises a list of axioms; when the user invokes Newaxiom, a trivial axiom ! # ! is added, and can be edited by substitu-tion operations on the classes. Here is an example from the People+Petsdomain [9]:Logical form Feedback text! # ! Everything is a thingP # ! Every pet is a thingP # A Every pet is an animalObviously P and A here are arbitrary labels that would be replacedin a Semantic Web application by URIs meeting the W3C namespaceconventions. Here is a more complex sequence using both individualsand properties:Logical form Feedback text! # ! Everything is a thing{m} # ! Mary is a thing{m} # ∃R.! Mary owns one or more things{m} # ∃R.P Mary owns one or more petsIn these sequences, transitions from one line to the next are made byclicking on a word/phrase representing a class, and choosing from a menuof permissible substitutions. For instance, starting from ‘Everything is athing’, the user might click on ‘Everything’ and then choose ‘Every pet’from the following pop-up menu:MaryEvery animalEvery dogEvery petEverything that likes one or more thingsEverything that owns one or more things. . .This method raises an obvious problem of scale: for any non-trivial ontol-ogy, classes will have to be selected from thousands of alternatives, andsome kind of search mechanism will therefore be needed. One solutionalready used in Wysiwym applications [3, 6, 5] is a menu equipped witha text field through which users can narrow the focus by typing in somecharacters from the desired word or phrase. In an ontology editor thissearch mechanism could be enhanced by using the ontology itself in orderto pick options that are conceptual rather than orthographic neighbours— for instance on typing in ‘dog’ the user would obtain a focussed listcontaining ‘poodle’ and ‘pekingese’ as well as ‘doggerel’.3 ConclusionWe have described a new approach to editing hybrid knowledge basesusing controlled natural language. The novelty of this approach is thatit relies entirely on natural language generation, thereby avoiding possi-ble pitfalls of interpretation-based methods (e.g., interpretation errors,training effort) as well as bringing potential benefits (e.g., multilinguality,more flexibility in designing the CNL). However, our proposals have notyet been developed in detail or applied and evaluated for large-scale on-tologies5. It remains to be seen (a) whether the proposed editing methodis intuitive for users, (b) whether users are able (and willing) to define thenecessary lexical resources during ontology building, (c) whether such an5 These tasks are being addressed in SWAT (Semantic Web Authoring Tool), a jointproject between the Open University and the University of Manchester.editing tool will scale up to large ontologies and expressive DLs like OWLFull, and (d) whether the differences between interpretation-based andgeneration-based approaches are important or merely an implementationdetail.References1. Raffaella Bernardi, Diego Calvanese, and Camilo Thorne. Lite nat-ural language. In 7th Int. Workshop on Computational Semantics(IWCS-7), 2007.2. A. Bernstein and E. Kaufmann. GINO – a guided input naturallanguage ontology editor. In Proceedings of the 5th InternationalSemantic Web Conference, Athens, Georgia, 2006.3. Nadjet Bouayad-Agha, Richard Power, Donia Scott, and Anja Belz.PILLS: Multilingual generation of medical information documentswith overlapping content. In Proceedings of the Third InternationalConference on Language Resoures and Evaluation (LREC 2002),pages 2111–2114, Las Palmas, 2002.4. Paolo Dongilli. Discourse Planning Strategies for Complex ConceptDescriptions. In Proceedings of the 7th International Symposium onNatural Language Processing, Pattaya, Chonburi, Thailand, 2007.5. R. Evans, P. Piwek, L. Cahill, and N. Tipper. Natural Language Pro-cessing in CLIME, a Multilingual Legal Advisory System. Journalof Natural Language Engineering, 14(1):101–132, 2008.6. Catalina Hallett, Donia Scott, and Richard Power. Composingqueries through conceptual authoring. Computational Linguistics,33(1):105–133, 2007.7. F. Hielkema, C. Mellish, and P. Edwards. Using WYSIWYM tocreate an open-ended interface for the semantic grid. In Proceedingsof the 11th European Workshop on Natural Language Generation,Schloss Dagstuhl, 2007.8. K. Kaljurand and N. Fuchs. Verbalizing OWL in Attempto Con-trolled English. In Proceedings of OWL: Experiences and Directions,Innsbruck, Austria, 2007.9. Qing Lu and V. Haarslev. OntoKBEval : A Support Tool for DL-based Evaluation of OWL Ontologies. In Proceedings of the 2006 In-ternational Workshop on OWL: Experiences and Directions , Geor-gia, USA, 2006.10. R. Power and D. Scott. Multilingual authoring using feedbacktexts. In Proceedings of the 17th International Conference on Com-putational Linguistics and 36th Annual Meeting of the Associationfor Computational Linguistics, pages 1053–1059, Montreal, Canada,1998.11. Andrea Schaerf. Reasoning with individuals in concept languages.Data and Knowledge Engineering, 13:141–176, 1994.12. R. Schwitter and M. Tilbrook. Controlled natural language meetsthe semantic web. In Proceedings of the Australasian Language Tech-nology Workshop, pages 55–62, Macquarie University, 2004.13. C. Thompson, P. Pazandak, and H. Tennant. Talk to your semanticweb. IEEE Internet Computing, 9(6):75–78, 2005.

Traditionally, Controlled Natural Languages (CNLs) are designed either to avoid ambiguity for human readers, or to facilitate automatic semantic analysis, so that texts can be transcoded to a knowledge representation language. CNLs of the second kind have recently been adapted to the requirements of knowledge formation in OWL for the Semantic Web. We suggest in this paper a variant approach based on automatic generation of texts in CNL (as opposed to automatic analysis), and argue that this provides the best of both worlds, allowing us to pursue human readability in addition to a precise mapping from texts to a formal language

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Editing OWL through generated CNL

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