An Information Content and Set of Common Superconcepts-Based Algorithm to Estimate Similarity between Concepts of Ontologies

Ontologies have been used for several years in life sciences to formally represent concepts and reason about knowledge bases in domains such as the semantic web, information retrieval and artificial intelligence. The exploration of these domains for the correspondence of semantic content requires calculation of the measure of semantic similarity between concepts. Semantic similarity is a measure on a set of documents, based on the similarity of their meanings, which refers to the similarity between two concepts belonging to one or more ontologies. The similarity between concepts is also a quantitative measure of information, calculated based on the properties of concepts and their relationships. This study proposes a method for finding similarity between concepts in two different ontologies based on feature, information content and structure. More specifically, this means proposing a hybrid method using two existing measures to find the similarity between two concepts from different ontologies based on information content and the set of common superconcepts, which represents the set of common parent concepts. We simulated our method on datasets. The results show that our measure provides similarity values that are better than those reported in the literature.

Bridge Ontology: A Multi-Ontologies-Based Approach for Semantic Annotation

Representing the relationships between ontologies is the key problem of semantic annotations based on multi-ontologies. Traditional approaches only had the ability of denoting the simple concept subsumption relations between ontologies. Through analyzing and classifying the relationships between ontologies, the idea of bridge ontology was proposed, which had the powerful capability of expressing the complex relationships between concepts and relationships between relations in multi-ontologies. Meanwhile, a new approach employing bridge ontology was proposed to deal with the multi-ontologies-based semantic annotation problem. The bridge ontology is a peculiar ontology, which can be created and maintained conveniently, and is effective in the multi-ontologies-based semantic annotation. The approach using bridge ontology has the advantages of low-cost, scalable, robust in the web circumstance, and avoiding the unnecessary ontology extending and integration. Key words semantic web - bridge ontology - multi-ontologies - semantic annotation CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024). National Grand Fundamental Research 973 Program of China (2002CB312000), National Re-search Foundation for the Doctoral Program of Higher Education of China (20020286004)Biography: WANG Peng (1977-), male, Ph.D candidate, research direction: semantic web, ontology, and knowledge representation on the Web.

Rac GTPases:domain-specific functions in neuronal development

Understanding fundamental mechanisms governing axon outgrowth and guidance can inform the development of therapeutic strategies to restore neuronal function damaged though injury or disease. Axons navigate the extracellular environment by responding to guidance cues that bind to cell surface receptors to relay information intracellularly via Rho GTPase family members, including the Rac GTPases.

Facilitating transparency in spinal cord injury studies using data standards and ontologies

Progress in developing robust therapies for spinal cord injury （SCI）, trau- matic brain injury （TBI） and peripheral nerve injury has been slow. A great deal has been learned over the past 30 years regarding both the intrinsic factors and the environmental factors that regulate axon growth, but this large body of information has not yet resulted in clinically available thera- peutics. This therapeutic bottleneck has many root causes, but a consensus is emerging that one contributing factor is a lack of standards for experi- mental design and reporting. The absence of reporting standards, and even of commonly accepted definitions of key words, also make data mining and bioinformatics analysis of neural plasticity and regeneration difficult, if not impossible. This short review will consider relevant background and poten- tial solutions to this problem in the axon regeneration domain.

Commentary on:“Facilitating transparency in spinal cord injury studies using data standards and ontologies”

Commentary Most would agree that providing comprehensive detail in scientific reporting is critical for the development of mean- ingful therapies and treatments for diseases. Such stellar practices 1） allow for reproduction of experiments to con- firm results, 2） promote thorough analyses of data, and 3） foster the incremental advancement of valid approaches. Unfortunately, most would also agree we have far to go to reach this vital goal （Hackam and Redelmeier, 2006; Prinz et al., 2011; Baker et al., 2014）.

NNL:a domain-specific language for neural networks

Recent years,neural networks(NNs)have received increasing attention from both academia and industry.So far significant diversity among existing NNs as well as their hardware platforms makes NN programming a daunting task.In this paper,a domain-specific language(DSL)for NNs,neural network language(NNL)is proposed to deliver productivity of NN programming and portable performance of NN execution on different hardware platforms.The productivity and flexibility of NN programming are enabled by abstracting NNs as a directed graph of blocks.The language describes 4 representative and widely used NNs and runs them on 3 different hardware platforms(CPU,GPU and NN accelerator).Experimental results show that NNs written with the proposed language are,on average,14.5%better than the baseline implementations across these 3 platforms.Moreover,compared with the Caffe framework that specifically targets the GPU platform,the code can achieve similar performance.

Domain-specific modeling and verification for C4ISR capability requirements

An approach was proposed to specify the C4ISR capability of domain-specific modeling language.To confine the domain modeling within a standard architecture framework,formally a C4ISR capability meta-ontology was defined according to the meta-model of DoD Architecture Framework.The meta-ontology is used for extending UML Profile so that the domain experts can model the C4ISR domains using the C4ISR capability meta-concepts to define a domain-specific modeling language.The domain models can be then checked to guarantee the consistency and completeness through converting the UML models into the Description Logic ontology and making use of inference engine Pellet to verify the ontology.

Query Through Heterogeneous Ontologies Using Association Matrix

This paper introduces the definition and calculation of the association matrix between ontologies. It uses the association matrix to describe the relations between concepts in different ontologies and uses concept vectors to represent queries; then computes the vectors with the association matrix in order to rewrite queries. This paper proposes a simple method of querying through heterogeneous Ontology using association matrix. This method is based on the correctness of approximate information filtering theory; and it is simple to be implemented and expected to run quite fast. Key words semantic Web - information retrieval - ontology - query - association matrix CLC number TP 391 Foundation item: Supported by the National Natural Science Foundation of China (60373066, 60303024), National Grand Fundamental Research 973 Program of China (2002CB312000) and National Research Foundation for the Doctoral Program of Higher Education of China (20020286004)Biography: KANG Da-zhou (1980-), male, Master candidate, research direction: Semantic Web, knowledge representation on the Web.

Subsumption Checking between Concept Queries in Different Ontologies Based on Mutual Instances

This paper proposes a checking method based on mutual instances and discusses three key problems in the method： how to deal with mistakes in the mutual instances and how to deal with too many or too few mutual instances. It provides the checking based on the weighted mutual instances considering fault tolerance, gives a way to partition the large-scale mutual instances, and proposes a process greatly reducing the manual annotation work to get more mutual instances. Intension annotation that improves the checking method is also discussed. The method is practical and effective to check subsumption relations between concept queries in different ontologies based on mutual instances.

Constructing Product Ontologies with an Improved Conceptual Clustering Algorithm

In a distributed eMarketplace, recommended product ontologies are required for trading between buyers and sellers. Conceptual clustering can be employed to build dynamic recommended product ontologies. Traditional methods of conceptual clustering (e.g. COBWEB or Cluster/2) do not take heterogeneous attributes of a concept into account. Moreover, the result of these methods is clusters other than recommended concepts. A center recommendation clustering algorithm is provided. According to the values of heterogeneous attributes, recommended product names can be selected at the clusters, which are produced by this algorithm. This algorithm can also create the hierarchical relations between product names. The definitions of product names given by all participants are collected in a distributed eMarketplace. Recommended product ontologies are built. These ontologies include relations and definitions of product names, which come from different participants in the distributed eMarketplace. Finally a case is given to illustrate this method. The result shows that this method is feasible.

A Process for Extracting Non-Taxonomic Relationships of Ontologies from Text

Manual construction of ontologies by domain experts and knowledge engineers is an expensive and time consuming task so, automatic and/or semiautomatic approaches are needed. Ontology learning looks for identifying ontology elements like non-taxonomic relationships from information sources. These relationships correspond to slots in a frame-based ontology. This article proposes an initial process for semiautomatic extraction of non-taxonomic relationships of ontologies from textual sources. It uses Natural Language Processing (NLP) techniques to identify good candidates of non-taxonomic relationships and a data mining technique to suggest their possible best level in the ontology hierarchy. Once the extraction of these relationships is essentially a retrieval task, the metrics of this field like recall, precision and f-measure are used to perform evaluation.

Concept Approximation between Fuzzy Ontologies

Fuzzy ontologics are efficient tools to handle fuzzy and uncertain knowledge on the semantic web; but there are heterogeneity problems when gaining interoperability among different fuzzy ontologies. This paper uses concept approximation between fuzzy ontologies based on instances to solve the heterogeneity problems. It firstly proposes an instance selection technology based on instance clustering and weighting to unify the fuzzy interpretation of different ontologies and reduce the number of instances to increase the efficiency. Then the paper resolves the problem of computing the approximations of concepts into the problem of computing the least upper approximations of atom concepts. It optimizes the search strategies by extending atom concept sets and defining the least upper bounds of concepts to reduce the searching space of the problem. An efficient algorithm for searching the least upper bounds of concept is given.

An Algebra of Ontologies Approximation under Uncertainty

Ontologies are widely used in modeling the real world for the purpose of information sharing and reasoning. Traditional ontologies contain only concepts and relations that describe asserted facts about the world. Modeling in a dynamic world requires taking into consideration the uncertainty that may arise in the domain. In this paper, the concept of soft sets initiated by Molodtsov and the concept of rough sets introduced by Pawlack are used to define a way of instantiating ontologies of vague domains. We define ontological algebraic operations and their properties while taking into consideration the uncertain nature of domains. We show that, by doing so, intra ontological operations and their properties are preserved and formalized as operations in a vague set of objects and can be proved algebraically.

Converting Instance Checking to Subsumption: A Rethink for Object Queries over Practical Ontologies

Efficiently querying Description Logic (DL) ontologies is becoming a vital task in various data-intensive DL applications. Considered as a basic service for answering object queries over DL ontologies, instance checking can be realized by using the most specific concept (MSC) method, which converts instance checking into subsumption problems. This method, however, loses its simplicity and efficiency when applied to large and complex ontologies, as it tends to generate very large MSCs that could lead to intractable reasoning. In this paper, we propose a revision to this MSC method for DL SHI , allowing it to generate much simpler and smaller concepts that are specific enough to answer a given query. With independence between computed MSCs, scalability for query answering can also be achieved by distributing and parallelizing the computations. An empirical evaluation shows the efficacy of our revised MSC method and the significant efficiency achieved when using it for answering object queries.

Application of ontologies to traceability in the dairy supply chain

Systems for tracking products through supply chains range from paper-based records maintained by producers, processors, and suppliers to sophisticated ICT-based solutions. In addition to supporting product traceability, ICTs may also support data capture, recording, storage, and sharing of traceability attributes on processing, genetics, inputs, disease/pest tracking and measurement of environmental variables. A key success factor for a traceability system is the capability to integrate and share information along the supply chain. ICT represents a tool to overcome integration problems, data fusion and information dissemination. In this paper we illustrate the application of ontology as a tool to model business processes and rules within an agri- food chain. The business case is represented by the Bovlac project: a scientific and technologic platform to trace fresh cheese production.

BioBroker: Knowledge Discovery Framework for Heterogeneous Biomedical Ontologies and Data

A large number of ontologies have been introduced by the biomedical community in recent years. Knowledge discovery for entity identification from ontology has become an important research area, and it is always interesting to discovery how associations are established to connect concepts in a single ontology or across multiple ontologies. However, due to the exponential growth of biomedical big data and their complicated associations, it becomes very challenging to detect key associations among entities in an inefficient dynamic manner. Therefore, there exists a gap between the increasing needs for association detection and large volume of biomedical ontologies. In this paper, to bridge this gap, we presented a knowledge discovery framework, the BioBroker, for grouping entities to facilitate the process of biomedical knowledge discovery in an intelligent way. Specifically, we developed an innovative knowledge discovery algorithm that combines a graph clustering method and an indexing technique to discovery knowledge patterns over a set of interlinked data sources in an efficient way. We have demonstrated capabilities of the BioBroker for query execution with a use case study on a subset of the Bio2RDF life science linked data.

Ontologies for Software Project Management: A Review

Software Project Management is a knowledge intensive process that can benefit substantially from ontology development and ontology engineering. Ontology development could facilitate or improve substantially the software development process through the improvement of knowledge management, the increase of software and artefacts reusability, and the establishment of internal consistency within project management processes of various phases of software life cycle. A large number of ontologies have been developed attempting to address various software engineering aspects, such as requirements engineering, components reuse, domain modelling, etc. In this paper, we present a systematic literature review focusing on software project management ontologies. The literature review, among other, has identified lack of standardization in terminology and concepts, lack of systematic domain modelling and use of ontologies mainly in prototype ontology systems that address rather limited aspects of software project management processes.

Building a Productive Domain-Specific Cloud for Big Data Processing and Analytics Service

Cloud Computing as a disruptive technology, provides a dynamic, elastic and promising computing climate to tackle the challenges of big data processing and analytics. Hadoop and MapReduce are the widely used open source frameworks in Cloud Computing for storing and processing big data in the scalable fashion. Spark is the latest parallel computing engine working together with Hadoop that exceeds MapReduce performance via its in-memory computing and high level programming features. In this paper, we present our design and implementation of a productive, domain-specific big data analytics cloud platform on top of Hadoop and Spark. To increase user’s productivity, we created a variety of data processing templates to simplify the programming efforts. We have conducted experiments for its productivity and performance with a few basic but representative data processing algorithms in the petroleum industry. Geophysicists can use the platform to productively design and implement scalable seismic data processing algorithms without handling the details of data management and the complexity of parallelism. The Cloud platform generates a complete data processing application based on user’s kernel program and simple configurations, allocates resources and executes it in parallel on top of Spark and Hadoop.

Graphical Transformation of OWL Ontologies to Event-B Formal Models

Formal methods use mathematical models to develop systems.Ontologies are formal specifications that provide reusable domain knowledge representations.Ontologies have been successfully used in several data-driven applications,including data analysis.However,the creation of formal models from informal requirements demands skill and effort.Ambiguity,inconsistency,imprecision,and incompleteness are major problems in informal requirements.To solve these problems,it is necessary to have methods and approaches for supporting the mapping of requirements to formal specifications.The purpose of this paper is to present an approach that addresses this challenge by using theWeb Ontology Language(OWL)to construct Event-B formal models and support data analysis.Our approach reduces the burden of working with the formal notations of OWL ontologies and Event-B models and aims to analyze domain knowledge and construct Event-B models from OWL ontologies using visual diagrams.The idea is based on the transformation of OntoGraf diagrams of OWL ontologies to UML-B diagrams for the purpose of bridging the gap between OWL ontologies and Event-B models.Visual data exploration assists with both data analysis and the development of Event-B formal models.To manage complexity,Event-B supports stepwise refinement to allow each requirement to be introduced at themost appropriate stage in the development process.UML-B supports refinement,so we also introduce an approach that allows us to divide and layer OntoGraf diagrams.

Contextual Text Mining Framework for Unstructured Textual Judicial Corpora through Ontologies

Digitalization has changed the way of information processing, and newtechniques of legal data processing are evolving. Text mining helps to analyze andsearch different court cases available in the form of digital text documents toextract case reasoning and related data. This sort of case processing helps professionals and researchers to refer the previous case with more accuracy in reducedtime. The rapid development of judicial ontologies seems to deliver interestingproblem solving to legal knowledge formalization. Mining context informationthrough ontologies from corpora is a challenging and interesting field. Thisresearch paper presents a three tier contextual text mining framework throughontologies for judicial corpora. This framework comprises on the judicial corpus,text mining processing resources and ontologies for mining contextual text fromcorpora to make text and data mining more reliable and fast. A top-down ontologyconstruction approach has been adopted in this paper. The judicial corpus hasbeen selected with a sufficient dataset to process and evaluate the results.The experimental results and evaluations show significant improvements incomparison with the available techniques.