Welcome to the Graph-Oriented AppLications Research Lab in CS@RIT. Our research focuses on graphs and their applications: from integrating knowledge graphs and graph databases to program comprehension or from finding subgraphs efficiently to predicting new edges.

GOAL guys

01/2021 New paper in TheWebConf
A new paper was accepted in the The Web Conference: Revisiting the Evaluation Protocol of Knowledge Graph Completion Methods for Link Prediction. We revisit the evaluation protocol of knowledge graph completion algorithms, including splits, accuracy metrics and model selection. Check out our publicly-available implementation.
12/2020 New paper in SIGCSE
A new poster was accepted in the ACM Technical Symposium on Computer Science Education: Learning to Recognize Semantically Similar Program Statements in Introductory Programming Assignments.
07/2020 New paper in CIKM
A new paper was accepted in the ACM International Conference on Information and Knowledge Management: The Impact of Negative Triple Generation Strategies and Anomalies on Knowledge Graph Completion. We analyze how different strategies to generate negative triples impact the accuracy of knowledge graph completion algorithms. Check out our publicly-available implementation.
11/2019 New papers in SAC
Two new papers were accepted in the ACM/SIGAPP Symposium on Applied Computing: Towards summarizing program statements in source code search. We detect program statements that are relevant to search for source code. Selecting suitable configurations for automated link discovery. We help link discovery techniques reduce the search space of possible configurations. Check out our publicly-available implementation.
08/2019 New paper in KCAP
A new paper was accepted in the ACM International Conference on Knowledge Capture: Generating Rules to Filter Candidate Triples for their Correctness Checking by Knowledge Graph Completion Techniques. We filter triples that are not likely to be added as new knowledge in knowledge graphs. Check out our publicly-available implementation.
08/2019 New paper in CIKM
A new paper was accepted in the ACM International Conference on Information and Knowledge Management: Clustering Recurrent and Semantically Cohesive Program Statements in Introductory Programming Assignments. We cluster program statements of interest using structural graph clustering for programming assignments. Check out our publicly-available implementation.
06/2019 New award
We got the best resource paper award at ESWC'19! Check out the announcement.
05/2019 New grant
Thanks to the NSF for supporting our research! Check out our project.
03/2019 New paper in ESWC
A new paper was accepted in the Extended Semantic Web Conference: AYNEC: All You Need for Evaluating Completion Techniques in Knowledge Graphs. A Python framework to deal with knowledge graph completion. Check out our publicly-available implementation.
11/2018 Atendding FSE'18
We attended FSE'18 in Orlando, FL to present our paper: Towards a framework for generating program dependence graphs from source code.
08/2018 New paper in SWAN@FSE
A new paper was accepted in the ACM SIGSOFT International Workshop on Software Analytics: Towards a framework for generating program dependence graphs from source code. We devised a framework to build program dependence graphs directly from Java source code. Check out our publicly-available implementation.
07/2018 Thesis defense
Wilberto Z. Nunez successfully defended his MS thesis entitled Improvements on ORCA for Fast Computation of Graphlet Degree Vectors in any Graphlet Order. Big congrats!
07/2018 ICFP Programming Contest
We participated in the organization of the ICFP Programming Constest. Matthew Fluet, congrats! Check some cool videos from participants: Nanovisu, Skobochka, Bot the Nanobot in the Race to the Bottom.
07/2018 New paper in WWWJ
A new paper was accepted in the World Wide Web journal: Deep Web crawling: a survey. It presents a survey of techniques for perform crawling to extract data from the Deep Web.
09/2017 Atendding FSE'17
We attended FSE'17 in Paderborn, Germany to present our paper: ARCC: Assistant for Repetitive Code Comprehension.
07/2017 New paper in FSE
A new paper was accepted in the ACM SIGSOFT Symposium on the Foundations of Software Engineering: ARCC: Assistant for Repetitive Code Comprehension. We used subgraph pattern matching against program dependence graphs to assist in program comprehension. Watch our screencast.
05/2017 New prototype
We have created a new prototype to deliver personalized feedback.
04/2017 Atendding ICDE'17
We attended ICDE'17 in San Diego, USA to present our paper: Automated Personalized Feedback in Introductory Java Programming MOOCs. Carlos R. Rivero was the chair of the Social networks session.
03/2017 Seminar by Victor J. Marin
Victor J. Marin delivered a seminar at RIT on providing personalized feedback using subgraph pattern matching in programming MOOCs.
01/2017 New paper in ICDE
A new paper was accepted in the IEEE International Conference on Data Engineering: Automated Personalized Feedback in Introductory Java Programming MOOCs. We developed a new framework to provide personalized feedback in introductory programming courses.
09/2016 Seminars by Carlos R. Rivero
Carlos R. Rivero delivered two seminars at RIT. He presented his work on providing personalized feedback using subgraph pattern matching in introductory programming courses. These talks were held in the context of the GCCIS PhD Colloquium Series and the Theory Canal: The Rochester Theory Seminar.
07/2016 New paper in KAIS
A new paper was accepted in the Knowledge and Information Systems journal: Efficient and scalable labeled subgraph matching using SGMatch. It presents a new exact subgraph matching technique based on graphlets and minimum hub covers.
07/2016 Atendding Summer School
Andrea Cimmino attended the Second ScaDS Summer School on Big Data in Leipzig (Picture). He learned about Big Data Storage/NoSQL, Distributed Data Processing (HPC/MapReduce/Streaming/Spark/Flink), Graph Analytics/Management and Big Data Integration.
05/2016 Seminar by Andrea Cimmino
Andrea Cimmino delivered a seminar at RIT entitled: Using context to improve integration in the Web of Data. He presented an overview of his PhD dissertation on improving link discovery using context information in the Web of Data.
04/2016 Atendding WWW'16
We attended WWW'16 in Montreal, Canada to present our paper: Improving Link Specifications using Context-Aware Information.
02/2016 New paper in LDOW (WWW workshops)
A new paper was accepted in the Workshop on Linked Data on the Web (LDOW) co-located with the International World Wide Web Conference (WWW): Improving Link Specifications using Context-Aware Information. We developed a new framework to model data collected from a crowd-sensing platform using semantic-web technologies.
01/2016 New paper in CoMoRea (PerCom workshops)
A new paper was accepted in the Workshop on Context and Activity Modeling and Recognition (CoMoRea)co-located with the IEEE International Conference on Pervasive Computing and Communication (PerCom): PLOMaR: An ontology framework for context modeling and reasoning on crowd-sensing platform. It presents a new approach to improve link discovery in the Web of Data by exploiting context information.

Current members

Carlos R. Rivero
Carlos R. Rivero
Assistant Professor in CS@RIT
Email address of Carlos R. Rivero  Homepage  LinkedIn Profile
Narayanan Asuri Krishnan
Narayanan Asuri Krishnan
MS student at RIT
Starting PhD in Fall'21
Email: nk1581
Michael Peechatt
Michael Peechatt
MS student at RIT
Starting PhD in Fall'21
Email: mp6510
Maheen Riaz Contractor
Maheen Riaz Contractor
MS student at RIT
Email: mc1927
Aishwarya Rao
Aishwarya Rao
MS student at RIT
Email: ar2711

Former members

Iti Bansal
Iti Bansal
Angel Cambero
Angel Cambero
Andrea Cimmino
Andrea Cimmino
Mayur Jawalkar
Mayur Jawalkar
Victor J. Marin
Victor J. Marin
Wilberto Z. Nunez
Wilberto Z. Nunez
Tobin Pereira
Tobin Pereira
Srinivas Sridharan
Srinivas Sridharan
Sudhanshu Tiwari
Sudhanshu Tiwari
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Mainstream topics

Knowledge graphs

Knowledge graphs

Even though knowledge graphs have proven very useful for several tasks, they are marked by incompleteness. Completion algorithms aim to extend knowledge graphs by predicting missing (subject, predicate, object) triples, usually by training a model to discern between correct (positive) and incorrect (negative) triples. However, under the open-world assumption in which a missing triple is not negative but unknown, negative triple generation is challenging. We aim to thoroughly evaluate knowledge graph completion.

Program comprehension

Program comprehension

Many software-related activities require comprehending programs that other programmers wrote. In the majority of such scenarios, program comprehension is a manual process since current approaches cannot adequately handle program variability and, in particular, interleaved tasks, i.e., sets of non-contiguous program statements with specific semantic purposes. We apply recent advances in graph databases to programs modeled as system dependence graphs. We rely on subgraph patterns to model tasks and subgraph matching to compute pattern occurrences.
Additional info: System Dependence Graph builder, ARCC.

Efficient subgraph matching

Efficient subgraph matching

Subgraph matching is a very hard problem and it has many important applications in graph databases to retrieve data represented as graphs, such as biological networks, chemical compounds, geographic maps, social networks and more. There exist several algorithms that exploit different heuristics and indexing structures to perform subgraph matching efficiently in practice. In general, it is not clear how these algorithms compare in terms of performance and/or accuracy. We aim to build a framework on top of Neo4j to fairly compare side by side current and future subgraph matching algorithms. This framework will allow to build intelligent query engines able to estimate, given a subgraph query and a data graphs, which algorithm will have the best performance.

Other topics

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Publications

2021

Contact

We are in the third floor of the Golisano College of Computing and Information Sciences. If you visit us, you need to get a temporary parking permit at the Welcome Center. The closest lots are J and S.

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Graph-Oriented AppLications Research Lab
Computer Science Department
Rochester Institute of Technology
Rochester, NY 14623

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