Data & Knowledge Engineering

The Digital Bibliography & Library Project (DBLP) dataset is related to the computer science bibliography. DBLP dataset can be modeled as a heterogeneous information network. In the dataset, DBLP contains “four-area” dataset, which was a subset of the DBLP dataset and included conferences in four research area: DataBase, Data Mining, Information Retrieval and Artificial Intelligence. The dataset contains 14 conferences, 5134 authors from 2012 to 2016.

This package contains the data sets used in the experiments of the following paper: Jinli Zhang，Zongli Jiang, Yongping Du, Tong Li, Yida Wang, Jingxuan Liu “Hierarchy Construction and Classification of Heterogeneous Information Networks Based on RSDAEf" USAGE LICENSE: Please note that the data are released as the way they are. The authors assume no responsibility for any potential problems. Permission to use the data is granted for academic and education uses only. Please acknowledge the use of this package by citing the above paper and the following paper if you publish materials based on the data obtained from this package: Jinli Zhang，Zongli Jiang, Yongping Du, Tong Li, Yida Wang, Jingxuan Liu Hierarchy Construction and Classification of Heterogeneous Information Networks Based on RSDAEf. CONTENT Files of four types of objects: author.txt, conference.txt, content.txt,year.txt; file format: "id \t name label" in each line. Files of two types of link relationships: paper_author.txt, title_content.txt; file format: "id_of_object_type_i \t id_of_object_type_j" in each line denoting two linked objects in type i and type j. Files of labels: author.txt, conference_label.txt, paper_author.txt; file format: "id \t name \t label" in each line;

The dataset contains the code used for performing all described the experiments. We also shared the ids of all retrieved tweets (and the code for retrieving the metadata using Twitter's API), the annotated corpus and the list of the edges extracted from the analysed networks.

The preCQE algorithm, in its parameterized form, as described in the article “Publishing Inference-Proof Relational Data: An Implementation and Experiments”, has been implemented as part of the larger Controlled Interaction Execution (CIE) framework prototype. In this data set, in the directory preCQE-source, we provide all the preCQE specific files, which are in the Java package edu.udo.cs.ls6.cie.server.censor.precqe. Furthermore we provide those files from package edu.udo.cs.ls6.cie.server.examples that contain the examples used in the article.

This HowTo wants to assist you in reading the raw data output of the experiments with the preCQE-prototype. It also gives a translation of the parameter and figure names used in the article „Publishing Inference-Proof Relational Data: An Implementation and Experiments“ into the corresponding names used in the experiments with the preCQEprototype.

The ontology consists of a knowledge model about the design of lead-lag controllers by the root locus method. The ontology was built grounded on the underlying mathematical concepts (numbers, quotients, polynomials, roots, etc). On top of these concepts, the ones pertaining to the language of control engineering were defined. Then, by combining or applying restrictions to these basic concepts, more complex ones were represented. Finally, with all this network of interrelated concepts, the ones guiding the design process could also be built. There are two ontologies: 1. Cekb is the basic ontology. It has 258 clases and 810 instances. 2. filled_cekb is the ontology after executing an example of root locus design. It has nearly 40.000 instances.

This file contains the software behind the paper "Aligning Textual and Model-Based Process Description" and the data sets used

This dataset is the news used for predicting Chinese Stock Index from 1 Jan 2015 to 14 Feb 2017. The dataset is used in paper: Chen, Weiling, Chai Kiat Yeo, Chiew Tong Lau, and Bu Sung Lee. “Leveraging social media news to predict stock index movement using RNN-Boost.” submitted to Data & Knowledge Engineering. training.csv includes all the news we have collected from the official accounts of Sina Weibo for prediction of CSI. mid indicates the unique id of the Weibo and uid indicates the user id of the author. It is very easy to get the full content of the Weibo using the api provided by Sina: http://open.weibo.com/wiki/2/statuses/show