Large software projects are among most sophisticated human-made systems consisting of a network of interdependent parts. Past studies of software systems from the perspective of complex networks have ...already led to notable discoveries with different applications. Nevertheless, our comprehension of the structure of software networks remains to be only partial. We here investigate correlations or mixing between linked nodes and show that software networks reveal dichotomous node degree mixing similar to that recently observed in biological networks. We further show that software networks also reveal characteristic clustering profiles and mixing. Hence, node mixing in software networks significantly differs from that in, e.g., the Internet or social networks. We explain the observed mixing through the presence of groups of nodes with common linking pattern. More precisely, besides densely linked groups known as communities, software networks also consist of disconnected groups denoted modules, core/periphery structures and other. Moreover, groups coincide with the intrinsic properties of the underlying software projects, which promotes practical applications in software engineering.
Traditional information extraction (IE) tasks roughly consist of named-entity recognition, relation extraction and coreference resolution. Much work in this area focuses primarily on separate ...subtasks where best performance can be achieved only on specialized domains.
In this paper we present a collective IE approach combining all three tasks by employing linear-chain conditional random fields. The usage of probabilistic models enables us to easily communicate between tasks on the fly and error correction during the iterative process execution. We introduce a novel iterative-based IE system architecture with additional semantic and collective feature functions.
Proposed system is evaluated against real-world data set, introduced in the paper, and results are better over traditional approaches on two tested tasks by error reduction and performance improvements.
Machine understanding of textual documents has been challenging since the early computer era. Since the information extraction research field emerged it has inferred multiple natural language ...processing tasks, such as named entities recognition, relationships extraction and coreference resolution. Even though for the purpose of the end-to-end information extraction all of the three tasks are crucial, existing work has been focusing merely on one specific task at the time or at best on their connection in a pipeline. In this paper we introduce a novel iterative and joint information extraction system that interconnects all the three tasks together using iterative feature functions which use the advantage of the intermediate extractions. Furthermore, we introduce a special transformation of data into skip-mention sequences to enable the extraction of relations and coreferences using fast first-order graphical models. Additionally, the system uses an ontology as its knowledge source, as a list of inferred extraction rules, and as a data schema of extracted results. Experimental results show that the accuracy of extractions improves after each iteration. In particular, our model obtained a 15% error reduction on named entity recognition over individual models.
Relational database to ontology mapping and ontology matching techniques are mostly addressed separately, even though it is known that the real power of semantic data lies in data interconnection. ...The latter is especially important when designing a new ontology, which often includes at least some of the concepts that already exist in the linked open data cloud. Thus, in this paper we describe a new end-to-end tool LogMap+ for transformation of relational data into an ontology and matching it against a pre-existent semantic source. Apart from offering the efficient web-based application, the main contributions are the improvements of the domain specific LogMap system. We evaluate our general tool against OAEI 2014 challenge datasets and achieve comparable results to the top performing algorithms and also outperform the domain specific LogMap tool.
IoT Platform marketplace is gaining a lot of attention in many areas. The paper presents an interoperable and extensible IoT framework that follows oneM2M standard. The framework is validated by a ...reference implementation that includes automatic sensor recognition and pairing, NoSQL support, complex event processing and alarming and extensions for IoT devices communication over HTTP, WebSocket, CoAP, MQTT, Z-Wave, ZigBee and Bluetooth. The novelty includes the theoretical and practical solution to the proposed platform. Furthermore, we show the feasibility to build generally useful IoT platforms that are based on standards.
Machine-learning techniques are widely used in the computer processing of natural language. Software agents are programs that use machine learning and natural language processing to communicate with ...users and to perform certain tasks or provide specific information. This paper provides an overview of basic software agents and describes the implementation of an intelligent software agent for social network Facebook Finally, the results of the research propose potential improvements of implemented system.
