Brian Davis

CLOnE: controlled language for ontology editing

This paper presents a controlled language for ontology editing and a software implementation, based partly on standard NLP tools, for processing that language and manipulating an ontology. The input sentences are analysed deterministically and compositionally with respect to a given ontology, which the software consults in order to interpret the inputs semantics; this allows the user to learn fewer syntactic structures since some of them can be used to refer to either classes or instances, for example. A repeated-measures, task-based evaluation has been carried out in comparison with a well-known ontology editor; our software received favourable results for basic tasks. The paper also discusses work in progress and future plans for developing this language and tool.

The caBIG terminology review process

The National Cancer Institute (NCI) is developing an integrated biomedical informatics infrastructure, the cancer Biomedical Informatics Grid (caBIG), to support collaboration within the cancer research community. A key part of the caBIG architecture is the establishment of terminology standards for representing data. In order to evaluate the suitability of existing controlled terminologies, the caBIG Vocabulary and Data Elements Workspace (VCDE WS) working group has developed a set of criteria that serve to assess a terminologys structure, content, documentation, and editorial process. This paper describes the evolution of these criteria and the results of their use in evaluating four standard terminologies: the Gene Ontology (GO), the NCI Thesaurus (NCIt), the Common Terminology for Adverse Events (known as CTCAE), and the laboratory portion of the Logical Objects, Identifiers, Names and Codes (LOINC). The resulting caBIG criteria are presented as a matrix that may be applicable to any terminology standardization effort.

RoundTrip Ontology Authoring

Controlled Language (CL) for Ontology Editing tools offer an attractive alternative for naive users wishing to create ontologies, but they are still required to spend time learning the correct syntactic structures and vocabulary in order to use the Controlled Language properly. This paper extends previous work (CLOnE) which uses standard NLP tools to process the language and manipulate an ontology. Here we also generate text in the CL from an existing ontology using template-based (or shallow) Natural Language Generation (NLG). The text generator and the CLOnE authoring process combine to form a RoundTrip Ontology Authoring environment: one can start with an existing imported ontology or one originally produced using CLOnE, (re)produce the Controlled Language, modify or edit the text as required and then turn the text back into the ontology in the CLOnE environment. Building on previous methodology we undertook an evaluation, comparing the RoundTrip Ontology Authoring process with a well-known ontology editor; where previous work required a CL reference manual with several examples in order to use the controlled language, the use of NLG reduces this learning curve for users and improves on existing results for basic ontology editing tasks.

Human Language Technologies

A tension exists between the increasingly rich semantic models in knowledge management systems and the continuing prevalence of human language materials in large organisations. The process of tying semantic models and natural language together is referred to as semantic annotation, which may also be char- acterized as the dynamic creation of bidirectional relationships between ontologies and unstructured and semi-structured documents. Information extraction (IE) takes unseen texts as input and produces fixed-format, unambiguous data as output. It involves processing text to identify selected infor- mation, such as particular named entities or relations among them from text docu- ments. Named entities include people, organizations, locations and so on, while relations typically include physical relations (located, near, part-whole, etc.), per- sonal or social relations (business, family, etc.), and membership (employ-staff, member-of-group, etc.). Ontology-based information extraction (OBIE) can be adapted specifically for semantic annotation tasks. An important difference between traditional IE and OBIE is the latters closely coupled use of an ontology as one of the systems resources - the ontology serves not only as a schema or list of classifications in the output, but also as input data - its structure affects the training and tagging processes. We present here two ontology-based developments for information extrac- tion. OBIE experiments demonstrate clearly that the integration of ontologies as a knowledge source within HLT applications leads to improved perform- ance. Another important finding is that computational efficiency of the under- lying machine learning methods is especially important for HLT tasks, as the system may need to train hundreds of classifiers depending on the size of the ontology.

Infrastructure for dynamic knowledge integration-Automated biomedical ontology extension using textual resources

We present a novel ontology integration technique that explicitly takes the dynamics and data-intensiveness of e-health and biomedicine application domains into account. Changing and growing knowledge, possibly contained in unstructured natural language resources, is handled by application of cutting-edge Semantic Web technologies. In particular, semi-automatic integration of ontology learning results into a manually developed ontology is employed. This integration bases on automatic negotiation of agreed alignments, inconsistency resolution and natural language generation methods. Their novel combination alleviates the end-user effort in the incorporation of new knowledge to large extent. This allows for efficient application in many practical use cases, as we show in the paper.

Semanta --- Semantic Email Made Easy

In this paper we present Semanta --- a fully-implemented system supporting Semantic Email Processes, integrated into the existing technical landscape and using existing email transport technology. By applying Speech Act Theory, knowledge about these processes can be made explicit, enabling machines to support email users with correctly interpreting, handling and keeping track of email messages, visualizing email threads and workflows, and extracting tasks and appointments from email messages. Whereas complex theoretical models and semantics are hidden beneath a simple user interface, the enabled functionalities are clear for the users to see and take advantage of. The systems evaluation proved that our experiment with Semanta has indeed been successful and that semantic technology can be applied as an extra layer to existing technology, thus bringing its benefits into everyday computer usage.

Multilingual Verbalisation of Modular Ontologies Using GF and lemon

This paper presents an approach to multilingual ontology verbalisation of controlled language based on the Grammatical Framework (GF) and the lemon model. It addresses specific challenges that arise when classes are used to create a consensus-based conceptual framework, in which many parties individually contribute instances. The approach is presented alongside a concrete case, in which ontologies are used to capture business processes by linguistically untrained stakeholders across business disciplines. GF is used to create multilingual grammars that enable transparent multilingual verbalisation. Capturing the instance labels in lemon lexicons reduces the need for GF engineering to the class level: The lemon lexicons with the labels of the instances are converted into GF grammars based on a mapping described in this paper. The grammars are modularised in accordance with the ontology modularisation and can deal with the different styles of label choosing that occur in practice.

SemEval-2017 Task 5: Fine-grained sentiment analysis on financial microblogs and news

Horizon 2020 ICT Program Project SSIX: Social Sentiment analysis financial IndeXes, has received funding from the European Union’s Horizon 2020 Research and Innovation Program ICT 2014 - Information and Communications Technologies under grant agreement No. 645425

Improving Email Conversation Efficiency through Semantically Enhanced Email

Despite persisting in popularity email is still plagued with information overload, hindering the workflow of data the user has to handle. While the revolutionization of the Web by the semantic Web is underway, we aspire to use the same technology to enhance electronic mail with useful semantics. In so doing we will tackle one of the largest flaws of the email communication genre - the lack of shared expectations about the form and content of the interaction. This can be attributed to the lack of explicit semantics covering the context and content of exchanged email messages. Earlier research showed that email content can be captured by applying speech act theory. We will refine and extend this work to develop an ontology for email speech acts and outline non-deterministic models to support the user in deciding the best course of action upon sending or receiving an email.

Dimensions of argumentation in social media

Mining social media for opinions is important to governments and businesses. Current approaches focus on sentiment and opinion detection. Yet, people also justify their views, giving arguments. Understanding arguments in social media would yield richer knowledge about the views of individuals and collectives. Extracting arguments from social media is difficult. Messages appear to lack indicators for argument, document structure, or inter-document relationships. In social media, lexical variety, alternative spellings, multiple languages, and alternative punctuation are common. Social media also encompasses numerous genres. These aspects can confound the extraction of well-formed knowledge bases of argument. We chart out the various aspects in order to isolate them for further analysis and processing.