Understanding Large-Scale Social Relationship Data by Combining Conceptual Graphs and Domain Ontologies

People worldwide communicate online and create a great amount of data on social media. The understanding of such large-scale data generated on social media and uncovering patterns from social relationship has received much attention from academics and practitioners. However, it still faces challenges to represent and manage the large-scale social relationship data in a formal manner. Therefore, this study proposes a social relationship representation model, which addresses both conceptual graph and domain ontology. Such a formal representation of a social relationship graph can provide a flexible and adaptive way to complete social relationship discovery. Using the term-define capability of ontologies and the graphical structure of the conceptual graph, this paper presents a social relationship description with formal syntax and semantics. The reasoning procedure working on this formal representation can exploit the capability of ontology reasoning and graph homomorphism-based reasoning. A social relationship graph constructed from the Lehigh University Benchmark (LUBM) is used to test the efficiency of the relationship discovery method.

Conceptual Graphs and Terminological Idiosyncrasy in UNCLOS and CBD

Do two conventions of international environmental law necessarily endow the same word with the same meaning? A single counterexample is enough to answer in the negative: this is the case of the term “resource” in the United Nations Convention on the Law of the Sea (UNCLOS) and the Convention on Biological Diversity (CBD). Beyond this result, we tackle the questions, raised by the method of analysis implemented, about the semantics of legal texts, a source of interpretative flexibility but also of cognitive amalgamations and confusions of various types. A conceptual graph is associated with each proposition or sentence comprising the term “resource.” Some expressions, especially those of a deontic nature and noun phrases naming a group of interrelated entities or a fact, are encoded in nested graphs. The scope of a term is revealed by the neighbourhood of its uses. Neighbouring expressions, positioned along the paths of conceptual graphs, are ranked owing to their distance from the target expression. Then the neighbours the most contributing to the distributional meaning of the targets are classified in a coarse taxonomy, providing basic ontological traits to “resource” and related expressions in each convention. Although the two conventions rely on the same language, the weak overlap of their respective neighbourhoods of the term “resource” and associated expressions and their contrasted ontological anchorages highlight idiosyncratic meanings and, consequently, divergent orientations and understandings regarding the protection and conservation of resources, especially of living resources. Thus, the complexity of legal texts operates both in the gap between language semantics and cognitive understanding of the concepts used, and in the interpretative flexibility and opportunities for confusion that the texts offer but that the elementary operations of formalisation allow to deconstruct and clarify.

Active Semantic Relations in Layered Enterprise Architecture Development

Enterprise Architecture (EA) metamodels align an organisation’s business, information and technology resources so that these assets best meet the organisation’s purpose. The Layered EA Development (LEAD) Ontology enhances EA practices by a metamodel with layered metaobjects as its building blocks interconnected by semantic relations. Each metaobject connects to another metaobject by two semantic relations in opposing directions, thus highlighting how each metaobject views other metaobjects from its perspective. While the resulting two directed graphs reveal all the multiple pathways in the metamodel, more desirable would be to have one directed graph that focusses on the dependencies in the pathways. Towards this aim, using CG-FCA (where CG refers to Conceptual Graph and FCA to Formal Concept Analysis) and a LEAD case study, we determine an algorithm that elicits the active as opposed to the passive semantic relations between the metaobjects resulting in one directed graph metamodel. We also identified the general applicability of our algorithm to any metamodel that consists of triples of objects with active and passive relations.

An Integrated Approach to Biomedical Term Identification Systems

In this paper a novel architecture to build biomedical term identification systems is presented. The architecture combines several sources of information and knowledge bases to provide practical and exploration-enabled biomedical term identification systems. We have implemented a system to evidence the convenience of the different modules considered in the architecture. Our system includes medical term identification, retrieval of specialized literature and semantic concept browsing from medical ontologies. By applying several Natural Language Processing (NLP) technologies, we have developed a prototype that offers an easy interface for helping to understand biomedical specialized terminology present in Spanish medical texts. The result is a system that performs term identification of medical concepts over any textual document written in Spanish. It is possible to perform a sub-concept selection using the previously identified terms to accomplish a fine-tune retrieval process over resources like SciELO, Google Scholar and MedLine. Moreover, the system generates a conceptual graph which semantically relates all the terms found in the text. In order to evaluate our proposal on medical term identification, we present the results obtained by our system using the MANTRA corpus and compare its performance with the Freeling-Med tool.

Quality Evaluation for Evolving Conceptual Database Design

The chapter is concerned with the proposal of a new approach of conceptual database design, called evolving conceptual database design, which exploits the structure for the preservation of database classes/concepts within the design. In order to discuss the opportunity to take into consideration this approach, the structure is constructed starting from a database conceptual graph. The leaves of the structure are mapped to a logical/object database graph. Horizontal steps of constructive logical database design extend the model. The computational costs required to design the structure for the preservation of database classes/concepts, as well as the qualitative/conceptual costs of the logical models resulting from the constructive design, are discussed.

Semantic Contextual Search Based on Conceptual Graphs over Encrypted Cloud

Currently, searchable encryption becomes the focus topic with the emerging cloud computing paradigm. The existing research schemes are mainly semantic extensions of multiple keywords. However, the semantic information carried by the keywords is limited and does not respond well to the content of the document. And when the original scheme constructs the conceptual graph, it ignores the context information of the topic sentence, which leads to errors in the semantic extension. In this paper, we define and construct semantic search encryption scheme for context-based conceptual graph (ESSEC). We make contextual contact with the central key attributes in the topic sentence and extend its semantic information, so as to improve the accuracy of the retrieval and semantic relevance. Finally, experiments based on real data show that the scheme is effective and feasible.