An algebra and conceptual model for semantic tagging of collaborative digital libraries

Cost-effective semantic description and annotation of shared knowledge resources has always been of great importance for digital libraries and large scale information systems in general. With the emergence of the Social Web and Web 2.0 technologies, a more effective semantic description and annotation, e.g., folksonomies, of digital library contents is envisioned to take place in collaborative and personalised environments. However, there is a lack of foundation and mathematical rigour for coping with contextualised management and retrieval of semantic annotations throughout their evolution as well as diversity in users and user communities. In this paper, we propose an ontological foundation for semantic annotations of digital libraries in terms of flexonomies. The proposed theoretical model relies on a high dimensional space with algebraic operators for contextualised access of semantic tags and annotations. The set of the proposed algebraic operators, however, is an adaptation of the set theoretic operators selection, projection, difference, intersection, union in database theory. To this extent, the proposed model is meant to lay the ontological foundation for a Digital Library 2.0 project in terms of geometric spaces rather than logic (description) based formalisms as a more efficient and scalable solution to the semantic annotation problem in large scale

Are Deep Learning Approaches Suitable for Natural Language Processing?

In recent years, Deep Learning (DL) techniques have gained much at-tention from Artificial Intelligence (AI) and Natural Language Processing (NLP) research communities because these approaches can often learn features from data without the need for human design or engineering interventions. In addition, DL approaches have achieved some remarkable results. In this paper, we have surveyed major recent contributions that use DL techniques for NLP tasks. All these reviewed topics have been limited to show contributions to text understand-ing, such as sentence modelling, sentiment classification, semantic role labelling, question answering, etc. We provide an overview of deep learning architectures based on Artificial Neural Networks (ANNs), Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM), and Recursive Neural Networks (RNNs)

MDDQL: an ontology driven, multi-lingual query language and system for an integrated view of heterogeneous data sources

Query languages and keywords based search engines are conventionally specified and implemented with the emphasis put on syntactic rules to which query typing and answering must be bound. MDDQL is a query language and system that operates on a semantic model in terms of a graph based ontology. As a software technology, MDDQL allows the meaning of/and associations between information to be known and processed at execution time at following levels: (a) driving the user to the construction of, as meaningful as possible, queries with an advanced concept-based search method, (b) resolving high level queries into various data source specific query statements. In addition, queries can be posed in more than one natural sub-language. The major goal behind this approach has been the simplification and scalability of both tasks: query construction, even within multi-lingual user communities, and addressing of a large number of possibly semantically heterogeneous data sources in a distributed environment

An information states blackboard as an intelligent querying interface for snow and avalanche data

We present the graph-based querying paradigm used in the Regional Avalanche Information and Forecasting System (RAIFoS) for the collection and analysis of snow and weather related physical parameters in the Swiss Alps. The querying paradigm relies upon the issue of interactively constructing a semantically valid query graph on an Information States Blackboard as guided by meta-data elements standing for interpretations of conceptual model, data values and/or operations. The meta-data elements constitute the terms of a meta-data-driven query language (MDDQL) the interpretation of which is done interactively relying on a kind of finite state automaton

Context based querying of scientific data: changing querying paradigms?

We are investigating and applying a semantically enhanced query answering machine for the needs of addressing semantically meaningful data and operations within a scientific information system. We illustrate a context based querying paradigm on the basis of a Regional Avalanche Information and Forecasting System - RAIFoS which is concerned with the collection and analysis of snow and weather related physical parameters in the Swiss Alps. The querying paradigm relies upon the issue of interactively constructing a semantically valid query rather than formulating one in a database specific query language and for a particular implementation model. In order to achieve this goal, the query answering machine has to make inferences concerning the properties and value domains, as well as data analysis operations, which are semantically valid within particular contexts. These inferences take place when the intended query is being constructed interactively on a Web-based blackboard. A graph-based display presentation formalism is used with elements including natural language terms, measurement units, statistical quantifiers and/or specific value domains. A meta-data database is used to organise and provide the elements of the graph each time the graph, and consequently the intended query, is expanded or further refined. Finally, the displayed graph is transformed into elements of the implementation model from which, in turn, SQL statements and/or sequences of statistical operations are created

Exploring the Suitability of Semantic Spaces as Word Association Models for the Extraction of Semantic Relationships

Given the recent advances and progress in Natural Language Processing (NLP), extraction of semantic relationships has been at the top of the research agenda in the last few years. This work has been mainly motivated by the fact that building knowledge graphs (KG) and bases (KB), as a key ingredient of intelligent applications, is a never-ending challenge, since new knowledge needs to be harvested while old knowledge needs to be revised. Currently, approaches towards relation extraction from text are dominated by neural models practicing some sort of distant (weak) supervision in machine learning from large corpora, with or without consulting external knowledge sources. In this paper, we empirically study and explore the potential of a novel idea of using classical semantic spaces and models, e.g., Word Embedding, generated for extracting word association, in conjunction with relation extraction approaches. The goal is to use these word association models to reinforce current relation extraction approaches. We believe that this is a first attempt of this kind and the results of the study should shed some light on the extent to which these word association models can be used as well as the most promising types of relationships to be considered for extraction

Cross-lingual information retrieval and delivery using community mobile networks

Much of the Web content is in English and accessing this content is difficult for non-English speaking users because of the language barrier. Hence, there is a great need for providing applications and interfaces in one's own language to tap into this vast knowledge reserve. In addition, access to the Internet is still a major problem in developing countries because of the "digital divide" and hand held devices such as PDAs and Mobile Phones are seen as enablers in bridging this gap. However, displaying cross-lingual content on these mobile devices is a non trivial task and there is a great need for robust mechanisms and infrastructure for content delivery in different languages on the fly. This paper presents an overall approach for cross-lingual content specification and delivery for computing/mobile devices. It helps mitigate the language barrier by providing cross-lingual search and retrieval capabilities for accessing the Web content