Nguyen

2010 CRC PhD Student Conference Generating Accessible Natural Language Explanations for OWL Ontologies Tu Anh Nguyen t.nguyen@open.ac.uk Supervisors Richard Power Paul Piwek Sandra 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 natural language explanations for entailments in OWL ontologies. The purpose of it is to support non-specialists, people who are not expert in description logic and formal ontology languages, 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 and repair 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 ordinary users to encode knowledge on the semantic web. The research questions are: • Do justifications for entailments in OWL ontologies conform to a relatively small number of common abstract patterns for which we could generalise the problem to generating explanations by patterns? • For a certain entailment and its justification, how to produce an explanation in natural language that is accessible for non-specialists? An ontology is a formal, explicit specification of a shared conceptualisation [6]. An ontology language 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 became a W3C standard, there has been a remarkable increase in the number of people trying to build and use OWL ontologies. Editing environments such as Prot´g´ [15] and Swoop [13] e e 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 expressive ontologies have been required, there is a significant demand for editing environments that provide more sophisticated editing and browsing services for debugging and repairing. In addition 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 identifying and correcting mistakes or errors in ontologies. Kalyanpur and colleagues defined a Page 65 of 125

2010 CRC PhD Student Conference justification for an entailment of an ontology as the precise subset of logical axioms from the ontology that are responsible for the entailment to hold [12]. Furthermore, he presented a user study showing that the availability of justifications had a remarkable positive impact on the ability of users to debug and repair their ontologies [11]. Justifications have also been recently used for debugging very large ontologies such as SNOMED [1], which size is too large to be able to debug and repair manually. There are several recent studies into capturing justifications for entailments in OWL ontologies [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 an entailment to hold, it is left up to the reader to work out how these premises interplay with each other to give rise to the entailment in question. Therefore, many users may struggle to understand how a justification supports an entailment since they are either unfamiliar with OWL syntax and semantics, or lack of knowledge about the logic underpinning the ontology. In other words, the ability of users to work out how an entailment arises from a justification currently depends on their understanding of OWL and description logic. In recent years, the development of ontologies has been moving from “the realm of artificial intelligence laboratories to the desktops of domain experts”, who have insightful knowledge of 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-specialist audience has emerged. Obviously, the wide access to OWL ontologies depends on the development 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 by the public without training. Rector and colleagues observed common problems that users frequently encounter in understanding the logical meaning and inferences when working with OWL-DL ontologies, and expressed the need for a “pedantic but explicit” paraphrase language to help users grasp the accurate meaning of logical axioms in ontologies [18]. Several research groups have proposed interfaces to encode knowledge in semantics-based Controlled 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 formal ontology languages. The SWAT project [16] introduces an alternative approach based on Natural Language Generation. In SWAT, users specify the content of an ontology by “directly 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 up ontologies without having to work directly on formal languages and description logic. How- ever, research on providing more advanced editing and browsing services on these interfaces to support the debugging and repairing process has not been investigated yet. Despite the usefulness of providing justifications in the form of sets of OWL axioms, understanding the reasons why entailments or inconsistencies are drawn from ontologies is still a key problem for non-specialists. Even for specialists, having a more user-friendly view of ontology with accessible explanations can be very helpful. Thus, this project seeks to develop a computational approach to generating accessible natural language explanations for entailments in OWL ontologies in order to assist users in debugging and repairing their ontologies. Methodology Page 66 of 125

2010 CRC PhD Student Conference The research approach is to identify common abstract patterns of justifications for entailments in OWL ontologies. Having identified such patterns we will focus on generating accessible 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. A corpus of eighteen real and published OWL ontologies of different expressivity has been collected from the Manchester TONEs reposistory. In addition, the practical module developed by Matthew Horridge based on the research on finding all justifications for OWL-DL ontologies [12, 7] has been used. Justifications are computed then analysed to work out the most common patterns. Results from the study show that over the total 6772 justifications collected, more than 70 percent of justifications belongs to the top 20 patterns. Study on a larger and more general ontology corpus will be carried out in next steps. Moreover, a user study is planned to investigate whether non-specialists perform better on a task when reading 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 studies on proof presentations. In Natural Deduction [5], how a conclusion is derived from a set of premises is represented as a series of intermediate statements linking from the premises to the conclusion. While this approach makes it easy for users to understand how to derive from one step to the next, it might cause difficulty to understand how those steps linked together to form the overall picture of the proof. Structured derivations [2], a top-down calculational 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 researchers as a method for teaching rigorous mathematical reasoning [3]. Research on whether using structured derivations would help to improve the accessibility of explanations as well as where 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-based CNLs. However, research on providing debugging and repairing services on these interfaces has not been investigated yet. Thus, this research seeks to develope a computational approach to generating accessible explanations to help users in understanding why an entailment follows from a justification. Research work includes identifying common abstract justification patterns and studying into generating explanations accessible for non-specialists. References [1] F. Baader and B. Suntisrivaraporn. Debugging SNOMED CT Using Axiom Pinpointing in the Description Logic EL+. In KR-MED, 2008. [2] R. Back, J. Grundy, , and J. von Wright. Structured Calculational Proof. Technical report, 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 Ontology Editor. In ISWC, 2006. Page 67 of 125

2010 CRC PhD Student Conference [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. Knowledge Acquisition, 5:199–220, 1993. [7] M. Horridge, B. Parsia, and U. Sattler. Laconic and Precise Justifications in OWL. In ISWC, pages 323–338, 2008. [8] I. Horrocks, P. F. Patel-Schneider, and F. van Harmelen. From SROIQ and RDF to OWL: 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 Justifications of DL Entailments. In ASWC, pages 306–320, 2009. [10] K. Kaljurand and N. E. Fuchs. Verbalizing OWL in Attempto Controlled English. In OWLED, 2007. [11] A. Kalyanpur. Debugging and repair of OWL ontologies. PhD thesis, University of Maryland, 2006. [12] A. Kalyanpur, B. Parsia, M. Horridge, and E. Sirin. Finding All Justifications of OWL DL Entailments. In ISWC, 2007. [13] A. Kalyanpur, B. Parsia, E. Sirin, B. Cuenca-Grau, and J. A. Hendler. Swoop: A Web Ontology Editing Browser. Journal of Web Semantics, 4:144–153, 2006. [14] N. F. Noy and D. L. McGuinness. Ontology Development 101: A Guide to Creating Your First Ontology. Technical report, Stanford University, 2001. [15] N. F. Noy, M. Sintek, S. Decker, M. Crub´zy, R. W. Fergerson, and M. A. Musen. e Creating Semantic Web Contents with Prot´g´-2000. IEEE Intell. Syst., 16:60–71, e e 2001. [16] R. Power. Towards a generation-based semantic web authoring tool. In ENLG, pages 9–15, 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: Common Errors & 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 practical OWL-DL reasoner. Journal of Web Semantics, 5:51–53, 2007. [21] B. Suntisrivaraporn, G. Qi, Q. Ji, and P. Haase. A Modularization-based Approach to Finding 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. Page 68 of 125

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