Language classification from bilingual word embedding graphs

We study the role of the second language in bilingual word embeddings in monolingual semantic evaluation tasks. We find strongly and weakly positive correlations between down-stream task performance and second language similarity to the target language. Additionally, we show how bilingual word embeddings can be employed for the task of semantic language classification and that joint semantic spaces vary in meaningful ways across second languages. Our results support the hypothesis that semantic language similarity is influenced by both structural similarity as well as geography/contact.Comment: To be published at Coling 201

empathi: An ontology for Emergency Managing and Planning about Hazard Crisis

In the domain of emergency management during hazard crises, having sufficient situational awareness information is critical. It requires capturing and integrating information from sources such as satellite images, local sensors and social media content generated by local people. A bold obstacle to capturing, representing and integrating such heterogeneous and diverse information is lack of a proper ontology which properly conceptualizes this domain, aggregates and unifies datasets. Thus, in this paper, we introduce empathi ontology which conceptualizes the core concepts concerning with the domain of emergency managing and planning of hazard crises. Although empathi has a coarse-grained view, it considers the necessary concepts and relations being essential in this domain. This ontology is available at https://w3id.org/empathi/

PowerAqua: fishing the semantic web

The Semantic Web (SW) offers an opportunity to develop novel, sophisticated forms of question answering (QA). Specifically, the availability of distributed semantic markup on a large scale opens the way to QA systems which can make use of such semantic information to provide precise, formally derived answers to questions. At the same time the distributed, heterogeneous, large-scale nature of the semantic information introduces significant challenges. In this paper we describe the design of a QA system, PowerAqua, designed to exploit semantic markup on the web to provide answers to questions posed in natural language. PowerAqua does not assume that the user has any prior information about the semantic resources. The system takes as input a natural language query, translates it into a set of logical queries, which are then answered by consulting and aggregating information derived from multiple heterogeneous semantic sources

A multi level approach for business process retrieval

Nowadays business process reuse is critical in companies that need to build flexible and service-based business solutions in order to react quic­kly and cost-effective to dynamic market-conditions. For this reason, many companies have implemented approaches to find relevant business processes to be reused to create new software solutions performing re­quired business functionalities. This paper presents a multilevel retrieval approach that detects linguistic, structural, and behavioral properties to increase the precision level in recovering those business processes stored in a repository.Actualmente reutilizar procesos de negocio es un procedimiento crítico especialmente para compañías que requieren construir soluciones flexibles y soportadas por servicios con el fin de afrontar de manera efectiva y a bajo costo las condiciones cambiantes del mercado. Por esta razón, muchas de ellas han implementado métodos para encontrar procesos de negocio relevantes que puedan ser reutilizados en la creación de nuevas soluciones software que cumplan con una determinada función de negocio. Este artí­culo presenta un método multinivel que detecta similitudes entre procesos de negocio, teniendo en cuenta propiedades lingüísticas, estructurales y de comportamiento, con el fin de incrementar el nivel de precisión en la recuperación de aquellos procesos existentes en un repositorio

Un enfoque multinivel para la recuperación de procesos de negocio

Nowadays business process reuse is critical in companies that need to build flexible and service-based business solutions in order to react quic­kly and cost-effective to dynamic market-conditions. For this reason, many companies have implemented approaches to find relevant business processes to be reused to create new software solutions performing re­quired business functionalities. This paper presents a multilevel retrieval approach that detects linguistic, structural, and behavioral properties to increase the precision level in recovering those business processes stored in a repository.Actualmente reutilizar procesos de negocio es un procedimiento crítico especialmente para compañías que requieren construir soluciones flexibles y soportadas por servicios con el fin de afrontar de manera efectiva y a bajo costo las condiciones cambiantes del mercado. Por esta razón, muchas de ellas han implementado métodos para encontrar procesos de negocio relevantes que puedan ser reutilizados en la creación de nuevas soluciones software que cumplan con una determinada función de negocio. Este artí­culo presenta un método multinivel que detecta similitudes entre procesos de negocio, teniendo en cuenta propiedades lingüísticas, estructurales y de comportamiento, con el fin de incrementar el nivel de precisión en la recuperación de aquellos procesos existentes en un repositorio

Onto Collab: Strategic review oriented collaborative knowledge modeling using ontologies

Modeling efficient knowledge bases for improving the semantic property of the World Wide Web is mandatory for promoting innovations and developments in World Wide Web. There is a need for efficient and organized modeling of the knowledge bases. In this paper, a strategy Onto Collab is proposed for construction of knowledge bases using ontology modeling. Ontologies are visualized as the basic building blocks of the knowledge in the web. The cognitive bridge between the human conceptual understanding of real world data and the processable data by computing systems is represented by Ontologies. A domain is visualized as a collection of similar ontologies. A review based strategy is proposed over a secure messaging system to author ontologies and a platform for retracing the domain ontologies as individuals and as a team is proposed. Evaluations for ontologies constructed pertaining to a domain for non-wiki knowledge bases is carried out

Cognitive satellite communications and representation learning for streaming and complex graphs.

This dissertation includes two topics. The first topic studies a promising dynamic spectrum access algorithm (DSA) that improves the throughput of satellite communication (SATCOM) under the uncertainty. The other topic investigates distributed representation learning for streaming and complex networks. DSA allows a secondary user to access the spectrum that are not occupied by primary users. However, uncertainty in SATCOM causes more spectrum sensing errors. In this dissertation, the uncertainty has been addressed by formulating a DSA decision-making process as a Partially Observable Markov Decision Process (POMDP) model to optimally determine which channels to sense and access. Large-scale networks have attracted many attentions to discover the hidden information from big data. Particularly, representation learning embeds the network into a lower vector space while maximally preserving the similarity among nodes. I propose a real-time distributed graph embedding algorithm (RTDGE) which is capable of distributively embedding the streaming graph by combining a novel edge partition approach and an incremental negative sample approach. Furthermore, a platform is prototyped based on Kafka and Storm. Real-time Twitter network data can be retrieved, partitioned and processed for state-of-art tasks. For knowledge graphs, existing works cannot capture the complex connection patterns and never consider the impacts from complicated relations, due to the unquantifiable relationships. A novel embedding algorithm is proposed to hierarchically measure the structural similarity and the impacts from relations by constructing a multi-layer graph. Then, an advanced representation learning model is designed based on an entity\u27s context generated by random walks on the multi-layer content graph