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2020 Papers

Data-driven construction of SPARQL queries by approximate question graph alignment in question answering over knowledge graphs

Mahdi Bakhshi, Mohammad Ali Nematbakhsh, Mehran Mohsenzadeh, and Amir Masoud Rahmania

As increasingly more semantic real-world data is stored in knowledge graphs, providing intuitive and effective query methods for end-users is a fundamental and challenging task. Since there is a gap between the plain natural language question (NLQ) and structured data, most RDF question/answering (Q/A) systems construct SPARQL queries from NLQs and obtain precise answers from knowledge graphs. A major challenge is how to disambiguate the mapping of phrases and relations in a question to the dataset items, especially in complex questions. read more

Ontology-lexicon–based question answering over linked data

Mahdi Jabalameli, Mohammad Ali Nematbakhsh, and Ahmad Zaeri

Recently, Linked Open Data has become a large set of knowledge bases. Therefore, the need to query Linked Data using question answering (QA) techniques has attracted the attention of many researchers. A QA system translates natural language questions into structured queries, such as SPARQL queries, to be executed over Linked Data. The two main challenges in such systems are lexical and semantic gaps. read more

An investigation of big graph partitioning methods for distribution of graphs in vertex-centric systems

Nasrin Mazaheri Soudani, Afsaneh Fatemi, and Mohammad Ali Nematbakhsh

Relations among data entities in most big data sets can be modeled by a big graph. Implementation and execution of algorithms related to the structure of big graphs is very important in different fields. Because of the inherently high volume of big graphs, their calculations should be performed in a distributed manner. Some distributed systems based on vertex-centric model have been introduced for big graph calculations in recent years.  read more

Mining Association Rules from Semantic Web Data without User Intervention

Reza RamezaniMohammad Ali Nematbakhsh, and Mohamad Saraee

With the introduction and standardization of the semantic web as the third generation of the web, this technology has attracted and received more human attention than ever. Thus, the amount of semantic web data is continuously growing, which makes them a rich source of useful data for data mining techniques. Semantic web data have some complexities, such as the heterogeneous structure of data, the lack of well-defined transactions, and the existence of typed relations between items. read more


Implicit relation-based question answering to answer simple questions overDBpedia

Maryam Jameshourani, Afsaneh Fatemi, and Mohammad Ali Nematbakhsh

RDF-based question answering systems give users the capability of natural language querying over RDF data. In order to respond to natural language questions, it is necessary that the main concept of the question be interpreted correctly, and then it is mapped to RDF data. A natural language question includes entities, classes, and implicit and explicit relationships.  read more

Improving Linked Data Quality Assessment and Fusion by a Conflict Resolution Approach

M. Khodizadeh Nahari, N. Nasser Ghadiri, Ahmad Baraani , and Jörg-R. Sack

The semantic web technology and decision making based on the linked data is progressing every day. The linked data are managed as decentralized sources, and their quality is a serious concern. The assessment of the quality of linked data is a key to adopting them in different fields because each data set has been developed by a different group, using various methods and tools. The qualitative and quantitative diversity of such data is higher than those generated by official organizations and firms.  read more


LISA: Language-Independent Method for Aspect-Based Sentiment Analysis

Mohammadreza Shams, Navid Khoshavi, and Ahmad Baraani

Understanding “what others think” is one of the most eminent pieces of knowledge in the decision-making process required in a wide spectrum of applications. The procedure of obtaining knowledge from each aspect (property) of users’ opinions is called aspect-based sentiment analysis which consists of three core sub-tasks: aspect extraction, aspect and opinion-words separation, and aspect-level polarity classification. Most successful approaches proposed in this area require a set of primary training or extensive linguistic resources, which makes them relatively costly and time consuming in different languages. read more


Data availability improvement in peer-to-peer online social networks

Fariba Khazaei Koohpar, Afsaneh Fatemi, and Fatemeh Raji

One of the main challenges of centralised social networks is having a central provider that stores the data which imposes some limitations to preserve the privacy of users’ data. However, one of the decentralised architectures is peer-to-peer network that every user takes the responsibility of storing and managing his/her data. Although the privacy of data is increased in these networks, authorised friends must have access to the shared data when the user is not online in the network.  read more


A Method For Answer Selection Using DistilBERT And Important Words

Jamshid Mozafari, Afsaneh Fatemi, and Parham Moradi

Question Answering is a hot topic in artificial intelligence and has many real-world applications. This field aims at generating an answer to the user’s question by analyzing a massive volume of text documents. Answer Selection is a significant part of a question answering system and attempts to extract the most relevant answers to the user’s question from the candidate answers pool. read more


Analytical reliability estimation of SRAM-based FPGA designs against single-bit and multiple-cell upsets

Reza Ramezani, Juan Antonio Clemente, and Francisco J.Franco

This paper addresses the problem of hardware tasks reliability estimation in harsh environments. A novel statistical model is presented to estimate the reliability, the mean time to failure, and the number of errors of hardware tasks running on static random-access memory (SRAM)-based partially run-time reconfigurable field programmable gate arrays (FPGAs) in harsh environments by taking both single-bit upsets and multiple-cell upsets into account.  read more


A prefetch-aware scheduling for FPGA-based multi-task graph systems

Reza Ramezani

In partially run-time reconfigurable (PRR) FPGAs, hardware tasks should be configured before their execution. The configuration delay imposed by the reconfiguration process increases the total execution time of the hardware tasks and task graphs. In this paper, a new technique named forefrontfetch is presented to improve the makespan of hardware task graphs running on PRR FPGAs via alleviating the adverse effects of the configuration delays. read more


Dynamic scheduling of task graphs in multi-FPGA systems using critical path

Reza Ramezani

SRAM-based FPGAs feature high performance and flexibility. Thus, they have found many applications in modern high-performance computing (HPC) systems. These systems suffer from the limitation of the computing resources problem for running HPC applications. Therefore, multi-FPGA systems have been emerged to alleviate such resource limitations. In this regard, efficient scheduling strategies are required to dynamically steer the execution of applications—represented as task graphs—on a set of connected FPGAs. read more


BAS: An Answer Selection Method Using BERT Language Model

Jamshid Mozafari, Afsaneh Fatemi, and Mohammad Ali Nematbakhsh

In recent years, Question Answering systems have become more popular and widely used by users. Despite the increasing popularity of these systems, their performance is not even sufficient for textual data and requires further research. These systems consist of several parts that one of them is the Answer Selection component. This component detects the most relevant answer from a list of candidate answers. read more