Approaches to ontology development by non ontology experts

Untrained users in the development of ontologies are challenged by the formal representation languages that underlie the most common ontology editing tools. To reduce that barrier, many efforts have gone in the creation of Controlled Languages (CL) translatable into ontology structures. However, CLs fall short of addressing a more profound problem: the selection of the most appropriate ontology modelling component for a certain modelling problem, regardless of the underlying representation paradigm. With the aim of approaching non ontology expert's difficulties in selecting the most appropriate modelling solution, we propose a Natural Language (NL) guided approach based on a repository of Lexico-Syntactic Patterns associated to consensual modelling solutions, i.e., Ontology Design Patterns. By relying on this repository, untrained users can formulate in NL what they want to model in the ontology, and obtain the corresponding design pattern for the modelling issue

Natural Language-based Approach for Helping in the Reuse of Ontology Design Patterns

Experiments in the reuse of Ontology Design Patterns (ODPs) have revealed that users with different levels of expertise in ontology modelling face difficulties when reusing ODPs. With the aim of tackling this problem we propose a method and a tool for supporting a semi-automatic reuse of ODPs that takes as input formulations in natural language (NL) of the domain aspect to be modelled, and obtains as output a set of ODPs for solving the initial ontological needs. The correspondence between ODPs and NL formulations is done through Lexico-Syntactic Patterns, linguistic constructs that convey the semantic relations present in ODPs, and which constitute the main contribution of this paper. The main benefit of the proposed approach is the use of non-restricted NL formulations in various languages for obtaining ODPs. The use of full NL poses challenges in the disambiguation of linguistic expressions that we expect to solve with user interaction, among other strategies

OWL to English: a tool for generating organised easily-navigated hypertexts from ontologies

It has frequently been observed that domain experts are not necessarily ontology experts, and that the production of ontologies would be aided if they could read and edit axioms in natural language. The SWAT Tools Verbaliser is available, via a web interface, for verbalising OWL ontologies as texts in a controlled fragment of English. Taking as input any OWL ontology, the verbaliser creates a lexicon containing entries for all the entities in the input, and uses it to generate an English sentence corresponding to each logical axiom. These sentences are then organised into a document structure similar to that of an encyclopaedia, with an entry providing a definition, typology and examples for each entity. The output is either organised, easily-navigable English text encoded in XML, or a copy of the input OWL in which each entity is annotated with its description entry. The generated texts have been evaluated in a number of ways which are briefly presented here

Human Reasoning and Description Logics: Applying Psychological Theory to Understand and Improve the Usability of Description Logics

Description Logics (DLs) are now the most commonly used ontology languages, in part because of the development of the Web Ontology Language (OWL) standards. Yet it is accepted that DLs are difficult to comprehend and work with, particularly for ontology users who are not computer scientists. The Manchester OWL Syntax (MOS) was developed to make DLs more accessible, by using English keywords in place of logic symbols or formal language. Nevertheless, DLs continue to present difficulties, even when represented in MOS. There has been some investigation of what features cause difficulties, specifically in the context of understanding how an entailment (i.e. an inference) follows from a justification (i.e. a minimal subset of the ontology that is sufficient for the entailment to hold), as is required when debugging an ontology. However, there has been little attempt to relate these difficulties to how people naturally reason and use language. This dissertation draws on theories of reasoning from cognitive psychology, and also insights from the philosophy of language, to understand the difficulties experienced with DLs and to make suggestions to mitigate those difficulties. The language features investigated were those known to be commonly used, both on the basis of analyses reported in the literature and after a survey of ontology users. Two experimental studies investigated participants’ ability to reason with DL statements. These studies demonstrate that insights from psychology and the philosophy of language can be used both to understand the difficulties experienced and to make proposals to mitigate those difficulties. The studies suggest that people reason using both the manipulation of syntax and the representation of semantics with mental models; both approaches can lead to errors. Particular difficulties were associated with: functional object properties; negated conjunction; the interaction of negation and the existential or universal restrictions; and nested restrictions. Proposals to mitigate these difficulties include the adoption of new language keywords; tool enhancement, e.g. to provide syntactically alternative expressions; and the introduction during training both of De Morgan’s Laws for conjunction and disjunction, and their analogues for existential and universal restrictions. A third study then investigated the effectiveness of the proposed new keywords; finding that these keywords could mitigate some of the difficulties experienced. Apart from the immediate applicability of these results to DLs, the approach taken in this dissertation could be extended widely to computer languages, including languages for interacting with databases and with Linked Data. Additionally, based on the experience of the three studies, the dissertation makes some methodological recommendations which are relevant to a range of human-computer interaction studies

“Hidden semantics”: what can we learn from the names in an ontology?

Despite their flat, semantics-free structure, ontology identifiers are often given names or labels corresponding to natural language words or phrases which are very dense with information as to their intended referents. We argue that by taking advantage of this information density, NLG systems applied to ontologies can guide the choice and construction of sentences to express useful ontological information, solely through the verbalisations of identifier names, and that by doing so, they can replace the extremely fussy and repetitive texts produced by ontology verbalisers with shorter and simpler texts which are clearer and easier for human readers to understand. We specify which axioms in an ontology are “defining axioms” for linguistically-complex identifiers and analyse a large corpus of OWL ontologies to identify common patterns among all defining axioms. By generating texts from ontologies, and selectively including or omitting these defining axioms, we show by surveys that human readers are typically capable of inferring information implicitly encoded in identifier phrases, and that texts which do not make such “obvious” information explicit are preferred by readers and yet communicate the same information as the longer texts in which such information is spelled out explicitly

Improving comprehension of Knowledge Representation languages: a case study with Description Logics

Knowledge representation languages are frequently difficult to understand, particularly for those not trained in formal logic. This is the case for Description Logics, which have been adopted for knowledge representation on the Web and in a number of application areas. This work looks at the difficulties experienced with Description Logics; and in particular with the widely-used Manchester OWL Syntax, which employs natural language keywords. The work comprises three studies. The first two identify a number of difficulties which users experience, e.g. with negated intersection, functional properties, the use of subproperties and restrictions. Insights from cognitive psychology and the study of language are applied to understand these difficulties. Whilst these difficulties are in part inherent in reasoning about logic, and Description Logics in particular, they are made worse by the syntax. In the third study, alternative syntactic constructs are proposed which demonstrate some improvement in accuracy and efficiency of comprehension. In addition to proposing alternative syntactic constructs, the work makes some suggestions regarding training and support systems for Description Logics