Multimatcher Model to Enhance Ontology Matching Using Background Knowledge

Ontology matching is a rapidly emerging topic crucial for semantic web effort, data integration, and interoperability. Semantic heterogeneity is one of the most challenging aspects of ontology matching. Consequently, background knowledge (BK) resources are utilized to bridge the semantic gap between the ontologies. Generic BK approaches use a single matcher to discover correspondences between entities from different ontologies. However, the Ontology Alignment Evaluation Initiative (OAEI) results show that not all matchers identify the same correct mappings. Moreover, none of the matchers can obtain good results across all matching tasks. This study proposes a novel BK multimatcher approach for improving ontology matching by effectively generating and combining mappings from biomedical ontologies. Aggregation strategies to create more effective mappings are discussed. Then, a matcher path confidence measure that helps select the most promising paths using the final mapping selection algorithm is proposed. The proposed model performance is tested using the Anatomy and Large Biomed tracks offered by the OAEI 2020. Results show that higher recall levels have been obtained. Moreover, the F-measure values achieved with our model are comparable with those obtained by the state of the art matchers.

Multi-Source Knowledge Reasoning for Data-Driven IoT Security

Nowadays, there are different kinds of public knowledge bases for cyber security vulnerability and threat intelligence which can be used for IoT security threat analysis. However, the heterogeneity of these knowledge bases and the complexity of the IoT environments make network security situation awareness and threat assessment difficult. In this paper, we integrate vulnerabilities, weaknesses, affected platforms, tactics, attack techniques, and attack patterns into a coherent set of links. In addition, we propose an IoT security ontology model, namely, the IoT Security Threat Ontology (IoTSTO), to describe the elements of IoT security threats and design inference rules for threat analysis. This IoTSTO expands the current knowledge domain of cyber security ontology modeling. In the IoTSTO model, the proposed multi-source knowledge reasoning method can perform the following tasks: assess the threats of the IoT environment, automatically infer mitigations, and separate IoT nodes that are subject to specific threats. The method above provides support to security managers in their deployment of security solutions. This paper completes the association of current public knowledge bases for IoT security and solves the semantic heterogeneity of multi-source knowledge. In this paper, we reveal the scope of public knowledge bases and their interrelationships through the multi-source knowledge reasoning method for IoT security. In conclusion, the paper provides a unified, extensible, and reusable method for IoT security analysis and decision making.

Biomedical Ontology Matching Through Attention-Based Bidirectional Long Short-Term Memory Network

Biomedical ontology formally defines the biomedical entities and their relationships. However, the same biomedical entity in different biomedical ontologies might be defined in diverse contexts, resulting in the problem of biomedicine semantic heterogeneity. It is necessary to determine the mappings between heterogeneous biomedical entities to bridge the semantic gap, which is the so-called biomedical ontology matching. Due to the plentiful semantic meaning and flexible representation of biomedical entities, the biomedical ontology matching problem is still an open challenge in terms of the alignment's quality. To face this challenge, in this work, the biomedical ontology matching problem is deemed as a binary classification problem, and an attention-based bidirectional long short-term memory network (At-BLSTM)-based ontology matching technique is presented to address it, which is able to capture the semantic and contextual feature of biomedical entities. In the experiment, the comparisons with state-of-the-art approaches show the effectiveness of the proposal.

Ontology-Based Probabilistic Estimation for Assessing Semantic Similarity of Land Use/Land Cover Classification Systems

To accurately and formally represent the historical trajectory and present the current situation of land use/land cover (LULC), numerous types of classification standards for LULC have been developed by different nations, institutes, organizations, etc.; however, these land cover classification systems and legends generate polysemy and ambiguity in integration and sharing. The approaches for dealing with semantic heterogeneity have been developed in terms of semantic similarity. Generally speaking, these approaches lack domain ontologies, which might be a significant barrier to implementing these approaches in terms of semantic similarity assessment. In this paper, we propose an ontological approach to assess the similarity of the domain of LULC classification systems and standards. We develop domain ontologies to explicitly define the descriptions and codes of different LULC classification systems and standards as semantic information, and formally organize this semantic information as rules for logical reasoning. Then, we utilize a Bayes algorithm to create a conditional probabilistic model for computing the semantic similarity of terms in two separate LULC land cover classification systems. The experiment shows that semantic similarity can be effectively measured by integrating a probabilistic model based on the content of ontology.

An empirical meta-analysis of the life sciences linked open data on the web

While the biomedical community has published several “open data” sources in the last decade, most researchers still endure severe logistical and technical challenges to discover, query, and integrate heterogeneous data and knowledge from multiple sources. To tackle these challenges, the community has experimented with Semantic Web and linked data technologies to create the Life Sciences Linked Open Data (LSLOD) cloud. In this paper, we extract schemas from more than 80 biomedical linked open data sources into an LSLOD schema graph and conduct an empirical meta-analysis to evaluate the extent of semantic heterogeneity across the LSLOD cloud. We observe that several LSLOD sources exist as stand-alone data sources that are not inter-linked with other sources, use unpublished schemas with minimal reuse or mappings, and have elements that are not useful for data integration from a biomedical perspective. We envision that the LSLOD schema graph and the findings from this research will aid researchers who wish to query and integrate data and knowledge from multiple biomedical sources simultaneously on the Web.

Applications of the Metadata Standards

This chapter addresses issues related to the practical use of the metadata standards, including syntactic interoperability and concrete scenarios from molecular modelling and simulation. It discusses challenges that arise from semantic heterogeneity, wherever multiple interoperability standards are concurrently employed for identical or overlapping domains of knowledge, or where domain ontologies need to be matched to top-level ontologies such as the European Materials and Modelling Ontology (EMMO).

Ways of adapting Greekisms in the Slavic-Russian version of the Euchologion of the Great Church

The Slavic-Russian translation of the Euchologion of the Great Church, made at the end of the 14th century by scribes from the circle of Metropolitan Cyprian, contains a large layer of exotic vocabulary. The purpose of this article is to describe the specifics of the adaptation of Greek vocabulary, borrowings, in this translation within the framework of Greek influence, which are known from the South Slavic translations of the 14th century. The article describes the differences concerning the degree of their morphological development, the relationship with their Slavic equivalent and with each other. Different ways of adapting the exoticisms are associated with their semantic heterogeneity in translation. The least ordered is the use of common noun vocabulary, denoting mainly objects of church use: each word that occurs repeatedly has its own set of declination variants. Proper names (or common nouns in the function of proper ones), as well as the names of heretical movements, were more consistently adapted. The frequency of such vocabulary in the Euchologion contributed to the development of typified means of its transmission. Along with techniques traditional for the 14th century for the Slavic tradition (glossing, deliberate use of unadapted foreign words), the translator also uses some new ways of adaptation, which can be considered as signs of the new wave of Greek influence. The new methods include cases of semantization of a variant of Greekism that differs from the traditional one, as well as methods of morphological and morphophonological adaptation of borrowings not known in the previous tradition.

The Narrative as a Mnemonic Text (Based on “Children of War” Narratives)

The article is focuses on the consideration of "children of war" narratives as mnemonic texts united by a common theme and containing memories of the Great Patriotic War in Russia (1941- 1945). The interdisciplinary approach to the analysis of such texts makes it possible to describe the nature of representation of the war in the minds of its eyewitnesses, to trace its rethinking and changing nature of memories. The research material illustrates the distinctive features of the narrative as a mnemonic text, namely the retrospective nature of the narrative, structural and semantic heterogeneity of the texts, linguistic expression of the authenticity of the event series, the interaction of the narrator with the interviewer in the narration, temporal postponement of memories expressed in evaluative judgments, self-examination of the events, reflexion, as well as cognitive "symbiosis" of the past and present, expressed in the using of past and present tenses of verbs in a sentence. The article introduces the notion of mnemonic situation and describes its structure and types: situations of information presence, situations of information loss, situations of information absence and situations of information recovery. In the course of the research, we found examples of interaction of several types of mnemical situation in a sentence or a thematic fragment.

An Ontology Approach to Data Integration using Mapping Method

Text mining is a technique to discover meaningful patterns from the available text documents. The pattern sighting from the text and document association of document is a well-known problem in data mining. Analysis of text content and categorization of the documents is a composite task of data mining. Some of them are supervised and some of them unsupervised manner of document compilation. The term “Federated Databases” refers to the in sequence integration of distributed, autonomous and heterogeneous databases. Nevertheless, a federation can also include information systems, not only databases. At integrating data, more than a few issues must be addressed. Here, we focus on the trouble of heterogeneity, more specifically on semantic heterogeneity – that is, problems correlated to semantically equivalent concepts or semantically related/unrelated concepts. In categorize to address this problem; we apply the idea of ontologies as a tool for data integration. In this paper, we make clear this concept and we briefly explain a technique for constructing ontology by using a hybrid ontology approach.

WebAssembly potentials: A performance analysis on desktop environment and opportunities for discussions to its application on CPS environment

The Web started as a simple document-sharing network and today has evolved to become a consolidated and ubiquitous platform for creation and application distribution. To explore its demands, web browser vendors have been working on new technologies like WebAssembly, a new type of machine language for a conceptual machine instead of a real physical machine, supported by the modern web browsers, providing new features and greater performance for web applications. At the other end, embedded devices have also evolved along with applications. However, there are still semantic heterogeneity, maintainability, and development issues inherent to the vast number of devices and services that operates in the numerous domains of Cyber-Physical Systems (CPS). The overall objective of this work is to study the WebAssembly technology through a performance analysis in a desktop environment, presenting empirical comparisons between the execution of a program compiled in native machine code and the same program compiled in WebAssembly, to verify its flexibility to compile code written in different languages for web applications and maintain similar performance to their native applications counterpart. We also point out the opportunities and challenges to potentially apply WebAssembly as a semantic abstraction layer for embedded devices in CPS development.