Nguyen

Generating Accessible Natural Language Explanations for OWLOntologies

Tu Anh [email protected]

Supervisors Richard PowerPaul PiwekSandra Williams

Department/Institute Computing Department

Status Full-time

Probation Viva Before

Starting date October 2009

Introduction

This research aims to develop a computational approach to generating accessible naturallanguage explanations for entailments in OWL ontologies. The purpose of it is to supportnon-specialists, people who are not expert in description logic and formal ontology lan-guages, in understanding why an inference or an inconsistency follows from an ontology.This would help to further improve the ability of users to successfully debug, diagnose andrepair their ontologies. The research is linked to the Semantic Web Authoring Tool (SWAT)project, the on-going project aiming to provide a natural language interface for ordinaryusers to encode knowledge on the semantic web. The research questions are:

• Do justifications for entailments in OWL ontologies conform to a relatively smallnumber of common abstract patterns for which we could generalise the problem togenerating explanations by patterns?

• For a certain entailment and its justification, how to produce an explanation in naturallanguage that is accessible for non-specialists?

An ontology is a formal, explicit specification of a shared conceptualisation [6]. An ontologylanguage is a formal language used to encode ontologies. The Web Ontology Language,OWL [8], is a widely used description logic based ontology language. Since OWL becamea W3C standard, there has been a remarkable increase in the number of people trying tobuild and use OWL ontologies. Editing environments such as Protege [15] and Swoop [13]were developed in order to support users with editing and creating OWL ontologies.

As ontologies have begun to be widely used in real world applications and more expressiveontologies have been required, there is a significant demand for editing environments thatprovide more sophisticated editing and browsing services for debugging and repairing. Inaddition to being able to perform standard description logic reasoning services namely sat-isfiability checking and subsumption testing, description logic reasoners such as FaCT++[22] and Pellet [20] can compute entailments (e.g., inferences) to improve the users com-prehension about their ontologies. However, without providing some kind of explanation,it can be very difficult for users to figure out why entailments are derived from ontologies.

The generation of justifications for entailments has proven enormously helpful for identi-fying and correcting mistakes or errors in ontologies. Kalyanpur and colleagues defined a

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justification for an entailment of an ontology as the precise subset of logical axioms fromthe ontology that are responsible for the entailment to hold [12]. Furthermore, he presenteda user study showing that the availability of justifications had a remarkable positive impacton the ability of users to debug and repair their ontologies [11]. Justifications have alsobeen recently used for debugging very large ontologies such as SNOMED [1], which size istoo large to be able to debug and repair manually.

There are several recent studies into capturing justifications for entailments in OWL ontolo-gies [12, 21, 9]. Nevertheless, OWL is a semantic markup language based on RDF and XML,languages that are oriented toward machine processability rather than human readability.Moreover, while a justification gathers together the axioms, or premises, sufficient for anentailment to hold, it is left up to the reader to work out how these premises interplay witheach other to give rise to the entailment in question. Therefore, many users may struggleto understand how a justification supports an entailment since they are either unfamiliarwith OWL syntax and semantics, or lack of knowledge about the logic underpinning theontology. In other words, the ability of users to work out how an entailment arises from ajustification currently depends on their understanding of OWL and description logic.

In recent years, the development of ontologies has been moving from “the realm of artificialintelligence laboratories to the desktops of domain experts”, who have insightful knowledgeof some domain but no expertise in description logic and formal ontology languages [14].It is for this reason that the desire to open up OWL ontologies to a wide non-specialistaudience has emerged. Obviously, the wide access to OWL ontologies depends on the devel-opment of editing environments that use some transparent medium; and natural language(e.g., English, Italian) text is an appropriate choice since it can be easily comprehended bythe public without training. Rector and colleagues observed common problems that usersfrequently encounter in understanding the logical meaning and inferences when workingwith OWL-DL ontologies, and expressed the need for a “pedantic but explicit” paraphraselanguage to help users grasp the accurate meaning of logical axioms in ontologies [18].

Several research groups have proposed interfaces to encode knowledge in semantics-basedControlled Natural Languages (CNLs) [19, 4, 10]. These systems allow users to input sen-tences conforming with a CNL then parse and tranform them into statements in formalontology languages. The SWAT project [16] introduces an alternative approach based onNatural Language Generation. In SWAT, users specify the content of an ontology by “di-rectly manipulating on a generated feedback text” rather than using text interpretation;therefore, “editing ontologies on the level of meaning, not text” [17].

Obviously, the above mentioned interfaces are designed for use by non-specialists to build upontologies without having to work directly on formal languages and description logic. How-ever, research on providing more advanced editing and browsing services on these interfacesto support the debugging and repairing process has not been investigated yet. Despite theusefulness of providing justifications in the form of sets of OWL axioms, understanding thereasons why entailments or inconsistencies are drawn from ontologies is still a key problemfor non-specialists. Even for specialists, having a more user-friendly view of ontology withaccessible explanations can be very helpful. Thus, this project seeks to develop a compu-tational approach to generating accessible natural language explanations for entailments inOWL ontologies in order to assist users in debugging and repairing their ontologies.

Methodology


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The research approach is to identify common abstract patterns of justifications for entail-ments in OWL ontologies. Having identified such patterns we will focus on generatingaccessible explanations in natural languages for most frequently used patterns. A prelim-inary study to work out the most common justification patterns has been carried out. Acorpus of eighteen real and published OWL ontologies of different expressivity has beencollected from the Manchester TONEs reposistory. In addition, the practical module devel-oped by Matthew Horridge based on the research on finding all justifications for OWL-DLontologies [12, 7] has been used. Justifications are computed then analysed to work out themost common patterns. Results from the study show that over the total 6772 justificationscollected, more than 70 percent of justifications belongs to the top 20 patterns. Study ona larger and more general ontology corpus will be carried out in next steps. Moreover, auser study is planned to investigate whether non-specialists perform better on a task whenreading accessible explanations rather than justifications in the form of OWL axioms.

The research on how to create explanations accessible for non-logicians is informed by studieson proof presentations. In Natural Deduction [5], how a conclusion is derived from a set ofpremises is represented as a series of intermediate statements linking from the premises tothe conclusion. While this approach makes it easy for users to understand how to derivefrom one step to the next, it might cause difficulty to understand how those steps linkedtogether to form the overall picture of the proof. Structured derivations [2], a top-downcalculational proof format that allows inferences to be presented at different levels of detail,seems to be an alternative approach for presenting proof. It was proposed by researchersas a method for teaching rigorous mathematical reasoning [3]. Research on whether usingstructured derivations would help to improve the accessibility of explanations as well aswhere and how intermediate inferences should be added is being investigated.

Conclusion

Since the desire to open up OWL ontologies to a wide non-specialist audience has emerged,several research groups have proposed interfaces to encode knowledge in semantics-basedCNLs. However, research on providing debugging and repairing services on these inter-faces has not been investigated yet. Thus, this research seeks to develope a computationalapproach to generating accessible explanations to help users in understanding why an entail-ment follows from a justification. Research work includes identifying common abstract jus-tification patterns and studying into generating explanations accessible for non-specialists.

References

[1] F. Baader and B. Suntisrivaraporn. Debugging SNOMED CT Using Axiom Pinpointingin the Description Logic EL+. In KR-MED, 2008.

[2] R. Back, J. Grundy, , and J. von Wright. Structured Calculational Proof. Technicalreport, The Australian National University, 1996.

[3] R.-J. Back and J. von Wright. A Method for Teaching Rigorous Mathematical Rea-soning. In ICTMT4, 1999.

[4] A. Bernstein and E. Kaufmann. GINO - A Guided Input Natural Language OntologyEditor. In ISWC, 2006.


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[5] G. Gentzen. Untersuchungen uber das logische Schließen. II. Mathematische Zeitschrift,39:405–431, 1935.

[6] T. R. Gruber. A translation approach to portable ontology specifications. KnowledgeAcquisition, 5:199–220, 1993.

[7] M. Horridge, B. Parsia, and U. Sattler. Laconic and Precise Justifications in OWL. InISWC, pages 323–338, 2008.

[8] I. Horrocks, P. F. Patel-Schneider, and F. van Harmelen. From SROIQ and RDF toOWL: The Making of a Web Ontology Language. J. Web Semantics, 1:7–26, 2003.

[9] Q. Ji, G. Qi, and P. Haase. A Relevance-Directed Algorithm for Finding Justificationsof DL Entailments. In ASWC, pages 306–320, 2009.

[10] K. Kaljurand and N. E. Fuchs. Verbalizing OWL in Attempto Controlled English. InOWLED, 2007.

[11] A. Kalyanpur. Debugging and repair of OWL ontologies. PhD thesis, University ofMaryland, 2006.

[12] A. Kalyanpur, B. Parsia, M. Horridge, and E. Sirin. Finding All Justifications of OWLDL Entailments. In ISWC, 2007.

[13] A. Kalyanpur, B. Parsia, E. Sirin, B. Cuenca-Grau, and J. A. Hendler. Swoop: A WebOntology Editing Browser. Journal of Web Semantics, 4:144–153, 2006.

[14] N. F. Noy and D. L. McGuinness. Ontology Development 101: A Guide to CreatingYour First Ontology. Technical report, Stanford University, 2001.

[15] N. F. Noy, M. Sintek, S. Decker, M. Crubezy, R. W. Fergerson, and M. A. Musen.Creating Semantic Web Contents with Protege-2000. IEEE Intell. Syst., 16:60–71,2001.

[16] R. Power. Towards a generation-based semantic web authoring tool. In ENLG, pages9–15, 2009.

[17] R. Power, R. Stevens, D. Scott, and A. Rector. Editing OWL through generated CNL.In CNL, 2009.

[18] A. Rector, N. Drummond, M. Horridge, J. Rogers, H. Knublauch, R. Stevens, H. Wang,and C. Wroe. OWL Pizzas: Practical Experience of Teaching OWL-DL: CommonErrors & Common Patterns. In EKAW, 2004.

[19] R. Schwitter and M. Tilbrook. Controlled Natural Language meets the Semantic Web.In ALTW, pages 55–62, 2004.

[20] E. Sirin, B. Parsia, B. C. Grau, A. Kalyanpur, and Y. Katz. Pellet: A practicalOWL-DL reasoner. Journal of Web Semantics, 5:51–53, 2007.

[21] B. Suntisrivaraporn, G. Qi, Q. Ji, and P. Haase. A Modularization-based Approach toFinding All Justifications for OWL DL Entailments. In ASWC, pages 1–15, 2008.

[22] D. Tsarkov and I. Horrocks. FaCT++ Description Logic Reasoner: System Description.In IJCAR, volume 4130, pages 292–297, 2006.


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Education

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