General entity linking systems usually leverage global coherence of all the mapped entities in the same document by using semantic embeddings and graph-based approaches. However, graph-based approaches are computationally expensive for open-domain datasets. In this paper, the authors overcome these problems by presenting an RDF embedding-based seq2seq entity linking method in specific domains. They filter candidate entities of mentions having similar meanings by using the domain information of the annotated pairs. They resolve high ambiguous pairs by using Bi-directional long short-term memory (Bi-LSTM) and attention mechanism for the entity disambiguation. To evaluate the system with baseline methods, they generate a dataset including book, music, and movie categories. They achieved 0.55 (Mi-F1), 0.586 (Ma-F1), 0.846 (Mi-F1), and 0.87 (Ma-F1) scores for high and low ambiguous datasets. They compare the method by using recent (WNED-CWEB) datasets with existing methods. Considering the domain-specificity of the proposed method, it tends to achieve competitive results while using the domain-oriented datasets.

BACKGROUND: Current approaches to identifying drug-drug interactions (DDIs), include safety studies during drug development and post-marketing surveillance after approval, offer important opportunities to identify potential safety issues, but are unable to provide complete set of all possible DDIs. Thus, the drug discovery researchers and healthcare professionals might not be fully aware of potentially dangerous DDIs. Predicting potential drug-drug interaction helps reduce unanticipated drug interactions and drug development costs and optimizes the drug design process. Methods for prediction of DDIs have the tendency to report high accuracy but still have little impact on translational research due to systematic biases induced by networked/paired data. In this work, we aimed to present realistic evaluation settings to predict DDIs using knowledge graph embeddings. We propose a simple disjoint cross-validation scheme to evaluate drug-drug interaction predictions for the scenarios where the drugs have no known DDIs. RESULTS: We designed different evaluation settings to accurately assess the performance for predicting DDIs. The settings for disjoint cross-validation produced lower performance scores, as expected, but still were good at predicting the drug interactions. We have applied Logistic Regression, Naive Bayes and Random Forest on DrugBank knowledge graph with the 10-fold traditional cross validation using RDF2Vec, TransE and TransD. RDF2Vec with Skip-Gram generally surpasses other embedding methods. We also tested RDF2Vec on various drug knowledge graphs such as DrugBank, PharmGKB and KEGG to predict unknown drug-drug interactions. The performance was not enhanced significantly when an integrated knowledge graph including these three datasets was used. CONCLUSION: We showed that the knowledge embeddings are powerful predictors and comparable to current state-of-the-art methods for inferring new DDIs. We addressed the evaluation biases by introducing drug-wise and pairwise disjoint test classes. Although the performance scores for drug-wise and pairwise disjoint seem to be low, the results can be considered to be realistic in predicting the interactions for drugs with limited interaction information.

Multi-agent systems (MAS) which communicate with intra-domain and inter-domain agent platforms have access control requirements. Instead of a central mechanism, a fine-grained access control mechanism could have been applied to MAS platforms. This paper emphasizes MAS-based domain and security ontologies with XACML-based access control approach for MAS platforms. The domain dependent behaviour and access control parameters in agent ontologies could be combined within a common XACML policy document that is used through different MAS applications. Agent-based access control requirements and common XACML policy documents should be consistent to enforce policies for MAS. To obtain this condition, the translation of organizational policies and platform based policies have to be considered in detail and the verified policy features have to be enforced in MAS to provide access for resources.

Semantic Web evolution brought a new vision into agent research. The interpretation of this second generation web will be realized by autonomous computational entities, called agents, to handle the semantic content on behalf of their human users. Surely, Semantic Web environment has specific architectural entities and a different semantic which must be considered to model a Multi-agent System (MAS) within this environment. Hence, in this study, we introduce a MAS development process which supports the Semantic Web environment. Our approach is based on Model Driven Development (MDD) which aims to change the focus of software development from code to models. We first define an architecture for Semantic Web enabled MASs and then provide a MAS metamodel which consists of the first class meta-entities derived from this architecture. We also define a model transformation process for MDD of such MASs. We present a complete transformation process in which the source and the target metamodels, entity mappings between models and the implementation of the transformation for two different real MAS frameworks by using a well-known model transformation language are all included. In addition to the model-to-model transformation, the implementation of the model-to-code transformation is given as the last step of the system development process. The evaluation of the proposed development process by considering its use within the scope of a real commercial software project is also discussed.

Software process improvement in small organizations is a challenging task where the "smallness" brings a number of unique problems. We report the status of our work on creating ISO 9001 compliant quality systems in a small software organization.