An Interactive Platform for Multilingual Linguistic Resource Enrichment

The world is extremely diverse and its diversity is obvious in the cultural differences and the large number of spoken languages being used all over the world. In this sense, we need to collect and organize a huge amount of knowledge obtained from multiple resources differing from one another in many aspects. A possible approach for doing that is to think of designing effective tools for construction and maintenance of linguistic resources based on well-defined knowledge representation methodologies capable of dealing with diversity and the continuous evolvement of human knowledge. In this paper, we present a linguistic resource management platform which allows for knowledge organization in a language-independent manner and provides the appropriate mapping from a language independent concept to one or more language specific lexicalization. The paper explains the knowledge representation methodology used in constructing the platform together with the iterative process followed in designing and implementing the first version of the platform, named UKC-1 and the updated refined version, named UKC-2

Adaptive Semantic Annotation of Entity and Concept Mentions in Text

The recent years have seen an increase in interest for knowledge repositories that are useful across applications, in contrast to the creation of ad hoc or application-specific databases. These knowledge repositories figure as a central provider of unambiguous identifiers and semantic relationships between entities. As such, these shared entity descriptions serve as a common vocabulary to exchange and organize information in different formats and for different purposes. Therefore, there has been remarkable interest in systems that are able to automatically tag textual documents with identifiers from shared knowledge repositories so that the content in those documents is described in a vocabulary that is unambiguously understood across applications. Tagging textual documents according to these knowledge bases is a challenging task. It involves recognizing the entities and concepts that have been mentioned in a particular passage and attempting to resolve eventual ambiguity of language in order to choose one of many possible meanings for a phrase. There has been substantial work on recognizing and disambiguating entities for specialized applications, or constrained to limited entity types and particular types of text. In the context of shared knowledge bases, since each application has potentially very different needs, systems must have unprecedented breadth and flexibility to ensure their usefulness across applications. Documents may exhibit different language and discourse characteristics, discuss very diverse topics, or require the focus on parts of the knowledge repository that are inherently harder to disambiguate. In practice, for developers looking for a system to support their use case, is often unclear if an existing solution is applicable, leading those developers to trial-and-error and ad hoc usage of multiple systems in an attempt to achieve their objective. In this dissertation, I propose a conceptual model that unifies related techniques in this space under a common multi-dimensional framework that enables the elucidation of strengths and limitations of each technique, supporting developers in their search for a suitable tool for their needs. Moreover, the model serves as the basis for the development of flexible systems that have the ability of supporting document tagging for different use cases. I describe such an implementation, DBpedia Spotlight, along with extensions that we performed to the knowledge base DBpedia to support this implementation. I report evaluations of this tool on several well known data sets, and demonstrate applications to diverse use cases for further validation

User Interfaces to the Web of Data based on Natural Language Generation

We explore how Virtual Research Environments based on Semantic Web technologies support research interactions with RDF data in various stages of corpus-based analysis, analyze the Web of Data in terms of human readability, derive labels from variables in SPARQL queries, apply Natural Language Generation to improve user interfaces to the Web of Data by verbalizing SPARQL queries and RDF graphs, and present a method to automatically induce RDF graph verbalization templates via distant supervision

Validating the ontolex-lemon lexicography module with K dictionaries'' multilingual data

The OntoLex-lemon model has gradually acquired the status of de-facto standard for the representation of lexical information according to the principles of Linked Data (LD). Exposing the content of lexicographic resources as LD brings both benefits for their easier sharing, discovery, reusability and enrichment at a Web scale, as well as for their internal linking and better reuse of their components. However, with lemon being originally devised for the lexicalization of ontologies, a 1:1 mapping between its elements and those of a lexicographic resource is not always attainable. In this paper we report our experience of validating the new lexicog module of OntoLex-lemon, which aims at paving the way to bridge those gaps. To that end, we have applied the module to represent lexicographic data coming from the Global multilingual series of K Dictionaries (KD) as a real use case scenario of this module. Attention is drawn to the structures and annotations that lead to modelling challenges, the ways the lexicog module tackles them, and where this modelling phase stands as regards the conversion process and design decisions for KD's Global series

A model for information retrieval driven by conceptual spaces

A retrieval model describes the transformation of a query into a set of documents. The question is: what drives this transformation? For semantic information retrieval type of models this transformation is driven by the content and structure of the semantic models. In this case, Knowledge Organization Systems (KOSs) are the semantic models that encode the meaning employed for monolingual and cross-language retrieval. The focus of this research is the relationship between these meanings’ representations and their role and potential in augmenting existing retrieval models effectiveness. The proposed approach is unique in explicitly interpreting a semantic reference as a pointer to a concept in the semantic model that activates all its linked neighboring concepts. It is in fact the formalization of the information retrieval model and the integration of knowledge resources from the Linguistic Linked Open Data cloud that is distinctive from other approaches. The preprocessing of the semantic model using Formal Concept Analysis enables the extraction of conceptual spaces (formal contexts)that are based on sub-graphs from the original structure of the semantic model. The types of conceptual spaces built in this case are limited by the KOSs structural relations relevant to retrieval: exact match, broader, narrower, and related. They capture the definitional and relational aspects of the concepts in the semantic model. Also, each formal context is assigned an operational role in the flow of processes of the retrieval system enabling a clear path towards the implementations of monolingual and cross-lingual systems. By following this model’s theoretical description in constructing a retrieval system, evaluation results have shown statistically significant results in both monolingual and bilingual settings when no methods for query expansion were used. The test suite was run on the Cross-Language Evaluation Forum Domain Specific 2004-2006 collection with additional extensions to match the specifics of this model

Mapping Events and Abstract Entities from PAROLE-SIMPLE-CLIPS to ItalWordNet

In the few last years, due to the increasing importance of the web, both computational tools and resources need to be more and more visible and easily accessible to a vast community of scholars, students and researchers. Furthermore, high quality lexical resources are crucially required for a wide range of HLT-NLP applications, among which word sense disambiguation. Vast and consistent electronic lexical resources do exist which can be further enhanced and enriched through their linking and integration. An ILC project dealing with the link of two large lexical semantic resources for the Italian language, namely ItalWordNet and PAROLE-SIMPLE-CLIPS, fits this trend. Concrete entities were already linked and this paper addresses the semi-automatic mapping of events and abstract entities. The lexical models of the two resources, the mapping strategy and the tool that was implemented to this aim are briefly outlined. Special focus is put on the results of the linking process: figures are reported and examples are given which illustrate both the linking and harmonization of the resources but also cases of discrepancies, mainly due to the different underlying semantic models

Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-Ranking and Adaptive Learning and Extension

The goal of this research project is the realization of an artificial intelligence-driven lightweight domain knowledge search framework that returns a domain knowledge structure upon request with highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology learning models. The re-ranking algorithm, a necessary mechanism to counter-play the heterogeneity and unstructured nature of web data, uses augmented queries and a hierarchical taxonomic structure to get further insight into the initial search results obtained from credited generic search engines. A semantic weight scale is applied to each node in the ontology graph and in turn generates a matrix of aggregated link relation scores that is used to compute the likely semantic correspondence between nodes and documents. Bootstrapped with a light-weight seed domain ontology, the theoretical platform focuses on the core back-end building blocks, employing two supervised automated learning models as well as semi-automated verification processes to progressively enhance, prune, and inspect the domain ontology to formulate a growing, up-to-date, and veritable system.\\ The framework provides an in-depth knowledge search platform and enhances user knowledge acquisition experience. With minimum footprint, the system stores only necessary metadata of possible domain knowledge searches, in order to provide fast fetching and caching. In addition, the re-ranking and ontology learning processes can be operated offline or in a preprocessing stage, the system therefore carries no significant overhead at runtime