Socialising around media. Improving the second screen experience through semantic analysis, context awareness and dynamic communities

SAM is a social media platform that enhances the experience of watching video content in a conventional living room setting, with a service that lets the viewer use a second screen (such as a smart phone) to interact with content, context and communities related to the main video content. This article describes three key functionalities used in the SAM platform in order to create an advanced interactive and social second screen experience for users: semantic analysis, context awareness and dynamic communities. Both dataset-based and end user evaluations of system functionalities are reported in order to determine the effectiveness and efficiency of the components directly involved and the platform as a whole

A Knowledge Graph Construction Approach for Legal Domain

Considering that the existing domain knowledge graphs have difficulty in updating data in a timely manner and cannot make use of knowledge sufficiently in the construction process, this paper proposes a legal domain knowledge graph construction approach based on \u27China Judgments Online\u27 in order to manage the cases\u27 knowledge contained in it. The construction process is divided into two steps. First, we extract the classification relationships of the cases from structured data. Then, we obtain attribute knowledge of cases from semi-structured data and unstructured data through a relationship extraction model based on an improved cross-entropy loss function. The triples describing knowledge of cases are stored through Neo4j. The accuracy of the proposed approach is verified through experiments and we construct a legal domain knowledge graph which contains more than 4K classification relationships and 12K attribute knowledge to prove its validity

Multi-threaded execution of Cypher queries

In this report we investigate parallel execution of queries in graph databases. We analyse different methods of parallelization, how to introduce query parallelization to a graph database, which query operations that are suitable for parallelization and if we can improve the execution time of a single query. We do this by designing and implementing a parallel runtime for the Cypher query language in the graph database Neo4j, but many of the design ideas and operators investigated are applicable to any graph database. We focus on increasing performance for a select few operators, while still being fully integrated with Neo4j. We take much inspiration from a design called morsel-driven parallelism. This means that we strive to split the workload into many small pieces, “morsels”, and then hand these morsels to the threads executing the query. This is in contrast to a more classical parallelization approach, where you split the workload into a few big parts of equal size. We conclude that the operators best suited for parallelization are the operators that can be split into several smaller parts, where each part can be computed independently. We successfully introduce parallel execution of Cypher queries to Neo4j and by doing so we increase the performance of a single query by up to 15 times under certain conditionsGrafdatabaser blir allt vanligare, samtidigt som antalet processorer i moderna datorer ökar mer och mer. Vi tittar i detta arbete på hur parallelliserad sökning kan leda till prestandavinster i den populära grafdatabashanteraren Neo4j. För att ta reda på om det går att parallellisera en enskild sökning i en grafdatabas och hur stor påverkan detta då har på svarstider, skapade vi vår egen modifierade version av Neo4j. Vi började med att ta reda på vilka delar av mjukvaran som bäst lämpade sig för parallellisering, med hänsyn till hur ofta de förekom i sökningar samt hur pass stora krav de ställde på processorn. Efter att ha valt ut ett antal av dessa så gick vi vidare med att ta fram metoder för att dela upp dem i mindre uppgifter som kunde köras i olika delar av processorn samtidigt, för att slutligen införa dessa ändringar i Neo4j. Resultatet är en version av Neo4j som under rätt förhållanden ger upp till 15 gånger snabbare svar på enskilda sökningar

AN EMPIRICAL COMPARISON OF NEO4J AND TIGERGRAPH DATABASES FOR NETWORK CENTRALITY

Graph databases have recently gained a lot of attention in areas where the relationships between data and the data itself are equally important, like the semantic web, social networks, and biological networks. A graph database is simply a database designed to store, query, and modify graphs. Recently, several graph database models have been developed. The goal of this research is to evaluate the performance of the two most popular graph databases, Neo4j and TigerGraph, for network centrality metrics including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and PageRank. We applied those metrics to a set of real-world networks in both graph databases to see their performance. Experimental results show Neo4j outperforms TigerGraph for computing the centrality metrics used in this study, but TigerGraph performs better during the data loading phase

ANALISIS AKTOR POPULAR DAN SUTRADARA BERPENGARUH BERDASARKAN DATA DBPEDIA MENGGUNAKAN ALGORITMA CLOSENESS CENTRALITY DAN NODE2VEC

The rapid development of web technology makes it easier for humans to access various information. The third generation of web-based internet services (Web 3.0) has introduced the Semantic Web which aims to enable content on the web to be understood by computers. The application of semantic web can be done to retrieve a dataset from Dbpedia Indonesia, which is a list of Indonesian films, for further data analysis. The purpose of data analysis is to find out the most popular actors and directors in the Indonesian film industry. This study uses closeness centrality and Node2vec algorithms to determine the level of popularity of actors. In addition, this study also uses density graph to determine the influential directors in the Indonesian film industry. The results of the algorithm calculations are visualized using Neo4j, Networkx, and tSNE which are graphs. In this study it was found that Rima Melati was the most popular actor because of the highest closeness centrality value. This can also be interpreted that Rima Melati is an actor who starred in the most films. While in density graph calculation, Sophan Sophiaan is the most influential director because he directs the most movie titles. Key Words : closeness centrality, dbpedia, density graph, networkx, node2vec  Perkembangan teknologi web yang semakin pesat membuat manusia semakin mudah dalam mengakses berbagai informasi. Generasi ketiga dari layanan internet berbasis web (Web 3.0) telah memperkenalkan Semantic Web yang bertujuan untuk memungkinakan konten pada web agar dapat dipahami oleh komputer. Penerapan Semantic Web dapat dilakukan untuk mengambil dataset dari DBpedia Indonesia, yaitu daftar film Indonesia, untuk selanjutnya dilakukan analisis data. Tujuan analisis data adalah untuk mengetahui aktor dan sutradara terpopuler dalam industri perfilman Indonesia. Penelitian ini menggunakan algoritma closeness centrality dan Node2vec untuk menentukan tingkat popularitas aktor. Selain itu, penelitian ini juga menggunakan density graph untuk mengetahui sutradara yang berpengaruh di industri perfilman Indonesia. Hasil dari perhitungan algoritma tersebut divisualisasikan menggunakan Neo4j, Networkx dan tSNE yang mana berupa graf. Pada penelitian ini ditemukan bahwa Rima Melati merupakan aktor terpopuler karena nilai closeness centralitynya tertinggi. Hal ini juga dapat diartikan bahwa Rima Melati merupakan aktor yang membintangi judul film terbanyak. Sedangkan pada perhitungan density graph, Sophan Sophiaan merupakan sutradara yang paling berpengaruh karena menyutradarai paling banyak judul film. Kata Kunci: closeness centrality, dbpedia, density graph, networkx, node2vec &nbsp

Recommender system in a graph database

The thesis presents a recommender system, which is implemented using graph databases. The recommender system aims to predict the "rating" which the user would give to the element or predict which elements would be of interest to the user. There are several algorithms for recommendations. The more important approaches are: collaborative filtering, content-based filtering, and hybrid recommender systems. Graph databases are particularly suitable for such systems due to their data model. The most prominent representative is Neo4j. Based on the Neo4j system, we developed a recommender system to recommend movies (based on GroupLens data) and supported it with a web application. We used collaborative and content-based filtering. The results of the application were compared with the results of the Surprise tool. We found out that the values of MAE and RMSE are similar if we use the same algorithm