Danica Damljanovic

Natural language interfaces to ontologies: combining syntactic analysis and ontology-based lookup through the user interaction

With large datasets such as Linked Open Data available, there is a need for more user-friendly interfaces which will bring the advantages of these data closer to the casual users. Several recent studies have shown user preference to Natural Language Interfaces (NLIs) in comparison to others. Although many NLIs to ontologies have been developed, those that have reasonable performance are domain-specific and tend to require customisation for each new domain which, from a developers perspective, makes them expensive to maintain. We present our system FREyA, which combines syntactic parsing with the knowledge encoded in ontologies in order to reduce the customisation effort. If the system fails to automatically derive an answer, it will generate clarification dialogs for the user. The users selections are saved and used for training the system in order to improve its performance over time. FREyA is evaluated using Mooney Geoquery dataset with very high precision and recall.

FREyA: an interactive way of querying linked data using natural language

Natural Language Interfaces are increasingly relevant for information systems fronting rich structured data stores such as RDF and OWL repositories, mainly because of the conception of them being intuitive for human. In the previous work, we developed FREyA, an interactive Natural Language Interface for querying ontologies. It uses syntactic parsing in combination with the ontology-based lookup in order to interpret the question, and involves the user if necessary. The users choices are used for training the system in order to improve its performance over time. In this paper, we discuss the suitability of FREyA to query the Linked Open Data. We report its performance in terms of precision and recall using the MusicBrainz and DBpedia datasets.

A natural language query interface to structured information

Accessing structured data such as that encoded in ontologies and knowledge bases can be done using either syntactically complex formal query languages like SPARQL or complicated form interfaces that require expensive customisation to each particular application domain. This paper presents the QuestIO system - a natural language interface for accessing structured information, that is domain independent and easy to use without training. It aims to bring the simplicity of Googles search interface to conceptual retrieval by automatically converting short conceptual queries into formal ones, which can then be executed against any semantic repository. QuestIO was developed specifically to be robustwith regard to language ambiguities, incomplete or syntactically ill-formed queries, by harnessing the structure of ontologies, fuzzy stringmatching, and ontology-motivated similarity metrics.

Linked data-based concept recommendation: comparison of different methods in open innovation scenario

Concept recommendation is a widely used technique aimed to assist users to chose the right tags, improve their Web search experience and a multitude of other tasks. In finding potential problem solvers in Open Innovation (OI) scenarios, the concept recommendation is of a crucial importance as it can help to discover the right topics, directly or laterally related to an innovation problem. Such topics then could be used to identify relevant experts. We propose two Linked Data-based concept recommendation methods for topic discovery. The first one, hyProximity, exploits only the particularities of Linked Data structures, while the other one applies a well-known Information Retrieval method, Random Indexing, to the linked data. We compare the two methods against the baseline in the gold standard-based and user study-based evaluations, using the real problems and solutions from an OI company.

Improving habitability of natural language interfaces for querying ontologies with feedback and clarification dialogues

Abstract Natural Language Interfaces (NLIs) are a viable, human-readable alternative to complex, formal query languages like SPARQL, which are typically used for accessing semantically structured data (e.g. RDF and OWL repositories). However, in order to cope with natural language ambiguities, NLIs typically support a more restricted language. A major challenge when designing such restricted languages is habitability–how easily, naturally and effectively users can use the language to express themselves within the constraints imposed by the system. In this paper, we investigate two methods for improving the habitability of a Natural Language Interface: feedback and clarification dialogues. We model feedback by showing the user how the system interprets the query, thus suggesting repair through query reformulation. Next, we investigate how clarification dialogues can be used to control the query interpretations generated by the system. To reduce the cognitive overhead, clarification dialogues are coupled with a learning mechanism. Both methods are shown to have a positive effect on the overall performance and habitability.

Random indexing for finding similar nodes within large RDF graphs

We propose an approach for searching large RDF graphs, using advanced vector space models, and in particular, Random Indexing (RI). We first generate documents from an RDF Graph, and then index them using RI in order to generate a semantic index, which is then used to find similarities between graph nodes. We have experimented with large RDF graphs in the domain of life sciences and engaged the domain experts in two stages: firstly, to generate a set of keywords of interest to them, and secondly to judge on the quality of the output of the Random Indexing method, which generated a set of similar terms (literals and URIs) for each keyword of interest.

Towards Enhanced Usability of Natural Language Interfaces to Knowledge Bases

Many Natural Language Interfaces (NLIs) to knowledge bases have been developed in order to provide easy access to structured data for casual users. However, those that have reasonable performance are domain-specific and tend to require customisation for each new domain, which, from a developer’s perspective, makes them expensive to maintain and unattractive for practical applications spanning different domains. This paper explores how the performance of existing NLI systems to knowledge bases can be improved without the extra cost of extensive customisation. Additionally, usability of NLIs to knowledge bases is explored from two aspects: that of the developer who is customising the system and that of the end-user who is querying it. We discuss existing methods for increasing the usability of NLI systems and their impact on the overall retrieval performance.

CA manager framework: creating customised workflows for ontology population and semantic annotation

We present the Content Augmentation Manager Framework for creating various adapted workflows for ontology population and semantic annotation based on Semantic Web recommendations and UIMA precepts. This framework supports ontology population from text semi-automatically, by allowing easy plug-in of various types of components including information extraction tools, customised domain ontologies, and diverse semantic repositories. Our evaluation reveals that the framework offers flexibility, without compromising on precision and recall of the constituting components.

Transition of legacy systems to semantically enabled applications: TAO method and tools

Despite expectations being high, the industrial take-up of Semantic Web technologies in developing services and applications has been slower than expected. One of the main reasons is that many legacy systems have been developed without considering the potential of the Web in integrating services and sharing resources. Without a systematic methodology and proper tool support, the migration from legacy systems to Semantic Web Service-based systems can be a tedious and expensive process, which carries a significant risk of failure. There is an urgent need to provide strategies, allowing the migration of legacy systems to Semantic Web Services platforms, and also tools to support such strategies. In this paper we propose a methodology and its tool support for transitioning these applications to Semantic Web Services, which allow users to migrate their applications to Semantic Web Services platforms automatically or semi-automatically. The transition of the GATE system is used as a case study.

Using virtual documents to move information retrieval and knowledge management closer together

While Information Retrieval approaches typically rely on a bag-of-word approach and are therefore fairly shallow, Knowledge Management is based on a deep semantic representation. Both allow users to sift through huge volumes of information and to identify the relevant document or answer for a particular information need. Obviously, these areas go beyond this simple information access scenario. Nevertheless, it is also a fact that these areas form fairly separate communities. We propose an idea of how to make use of techniques coming from both ends of the spectrum and combine them in methods that are more powerful than each of them individually. Our idea is based on turning structured information into virtual documents built to preserve the concepts and relations inherent in the semantically rich data before we apply Information Retrieval methods.