Our latest paper for the AAAI Fall Symposium.
Abstract: Wild Big Data is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing Wild Big Data that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph’s nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.
Showing posts with label Semantic Web. Show all posts
Showing posts with label Semantic Web. Show all posts
Friday, October 3, 2014
Sunday, September 15, 2013
UMBC ebiquity group
Our group is performing interesting research by blending semantic computing with security, mobile devices, medical informatics and big data problems. Some of this research overlaps quite a bit with natural language processing.
Read our publications to see the latest research coming from our lab.
Tuesday, April 9, 2013
Saturday, February 25, 2012
Paper Summary - Linking Social Networks on the Web with FOAF: A Semantic Web Case Study
Linking Social Networks on the Web with FOAF: A Semantic Web Case Study,J. Golbeck and M. Rothstein, 2008
Another paper related to FOAF and the Semantic Web. This paper examines linking FOAF identities across social networks. They propose that it would be better to merge these accounts across sites so a user has one social network. This may have been an interesting idea in 2008 but I'm not sure if this is relevant now seeing as some social networks let you bring in contacts from other sites. In general, this paper can be excluded from further analysis.
Another paper related to FOAF and the Semantic Web. This paper examines linking FOAF identities across social networks. They propose that it would be better to merge these accounts across sites so a user has one social network. This may have been an interesting idea in 2008 but I'm not sure if this is relevant now seeing as some social networks let you bring in contacts from other sites. In general, this paper can be excluded from further analysis.
Friday, July 15, 2011
Linked Data Paper Summary
"Linked Data - The Story So Far", C. Bizer, T. Heath, T. Berners-Lee, 2009, http://eprints.ecs.soton.ac.uk/21285/1/bizer-heath-berners-lee-ijswis-linked-data.pdf
My work will support working with linked data so I am attempting to build a better understanding of this topic.
This paper is good for providing a very basic understanding of linked data. If you are new to the concept of linked data, this is a good starting paper to read. It provides basic principles, examples, and isn't too technical.
If you already have a good understanding of the basics, skip this paper. I read it in about 10 minutes and it was pretty much just a review of what I already knew.
My work will support working with linked data so I am attempting to build a better understanding of this topic.
This paper is good for providing a very basic understanding of linked data. If you are new to the concept of linked data, this is a good starting paper to read. It provides basic principles, examples, and isn't too technical.
If you already have a good understanding of the basics, skip this paper. I read it in about 10 minutes and it was pretty much just a review of what I already knew.
Saturday, May 29, 2010
Hadoop + SPARQL
I began working with cloud computing about 2 years ago because of my interest in using this environment for semantic web applications. It seems to be of interest to others too.
https://opencirrus.org/content/sparql-query-over-hadoop-very-large-rdf-dataset
http://portal.acm.org/citation.cfm?id=1779599.1779605
http://cs264.org/projects/web/Rohloff_Kurt/rohloff/index.html
https://opencirrus.org/content/sparql-query-over-hadoop-very-large-rdf-dataset
http://portal.acm.org/citation.cfm?id=1779599.1779605
http://cs264.org/projects/web/Rohloff_Kurt/rohloff/index.html
Sunday, April 25, 2010
Paper Summary - A Framework for Combining Ontology and Schema Matchers with Dempster-Shafer
"Paper Summary - A Framework for Combining Ontology and Schema Matchers with Dempster-Shafer", P. Besana
This is a short paper about using Dempster-Shafer for ontology mapping. They have a tool based on their work: PyontoMap. This paper is really relevant so the summary will be deferred.
This is a short paper about using Dempster-Shafer for ontology mapping. They have a tool based on their work: PyontoMap. This paper is really relevant so the summary will be deferred.
Paper Summary - BeliefOWL: An Evidential Representation in OWL Ontology
Amira Essaid and Boutheina Ben Yaghlane, BeliefOWL: An Evidential Representation in OWL Ontology, pages 77-80, International Semantic Web Conference, International Workshop on Uncertainty Reasoning for the Semantic Web, Washington DC, USA, 2009.
This paper is very short, only 4 pages. It starts with a discussion of how uncertainty is represented currently in ontologies using either probabilistic or fuzzy approaches. It then proposed the Dempster-Shafer approach as another option. They discuss the work by :
Ben Yaghlane,B.: Uncertainty representation and reasoning in directed evidential
networks, PhD thesis, Institut Sup´erieur de Gestion de Tunis Tunisia, 2002.
which is work related to representing uncertainty using a DAG. I haven't read this paper yet but it certainly seems like a good read.
This paper then goes on with a presentation of BeliefOWL, their uncertainty extension to OWL.
They define two classes to represent prior evidence:
- enumerates different masses and has object property which specifies the relation between itself and .
- expreses prior evidence and has property
They define two classes which represent conditional evidence:
- has an object property of
- conditional evidence with property
They construct an evidential network by translating the OWL ontology into a DAG. They then assign masses to nodes in the DAG. Details of this work are mentioned but briefly.
Overall I don't know if this helps my work in anyway except to see some uses of DS with ontologies.
This paper is very short, only 4 pages. It starts with a discussion of how uncertainty is represented currently in ontologies using either probabilistic or fuzzy approaches. It then proposed the Dempster-Shafer approach as another option. They discuss the work by :
Ben Yaghlane,B.: Uncertainty representation and reasoning in directed evidential
networks, PhD thesis, Institut Sup´erieur de Gestion de Tunis Tunisia, 2002.
which is work related to representing uncertainty using a DAG. I haven't read this paper yet but it certainly seems like a good read.
This paper then goes on with a presentation of BeliefOWL, their uncertainty extension to OWL.
They define two classes to represent prior evidence:
They define two classes which represent conditional evidence:
They construct an evidential network by translating the OWL ontology into a DAG. They then assign masses to nodes in the DAG. Details of this work are mentioned but briefly.
Overall I don't know if this helps my work in anyway except to see some uses of DS with ontologies.
Thursday, March 18, 2010
Paper Summary - Performing Object Consolidation on the Semantic Web Data Graph
Hogan, A.; Harth, A.; and Decker, S. 2007. Performing object
consolidation on the semantic web data graph. In In Proceedings
of I3: Identity, Identifiers, Identification. Workshop at 16th
International World Wide Web Conference (WWW2007).
This paper describes identities and the integration of data. They present a method for merging instances across multiple data sources (**large scale**). They describe how they determine two instances represent the same entity using inverse functional properties. Their dataset includes over 72 million instances (wow).
Key points:
consolidation on the semantic web data graph. In In Proceedings
of I3: Identity, Identifiers, Identification. Workshop at 16th
International World Wide Web Conference (WWW2007).
This paper describes identities and the integration of data. They present a method for merging instances across multiple data sources (**large scale**). They describe how they determine two instances represent the same entity using inverse functional properties. Their dataset includes over 72 million instances (wow).
Key points:
- There isn't much agreement on use of common URIs to identify entities (optional in RDF) so URI represents multiple instances at times
- There is a lack of formal specification for determining equivalences among entities
- Existing methods that perform object consolidation rely upon probabilistic methods
- The use of inverse functional properties in the Semantic Web world is widely used
Paper Summary - On Searching and Displaying RDF Data from the Web
A., H., and H., G. 2005. On searching and displaying RDF data
from the web. In Proceedings of Demos and Posters of the 2nd
European Semantic Web Conference.
This document describes an application built to show RDF in a user interface. It describes how it gathered the data and also a smushing technique it used. The goal was to determine if it was feasible to integrate data without an underlying ontology. They describe their challenges with the data in this paper.
The key points of this paper:
The paper is short and missing a lot of details. Though I referenced this in the "A Machine Learning Approach to Linking FOAF Instances" document, it really was just to give an example of a smushing process.
from the web. In Proceedings of Demos and Posters of the 2nd
European Semantic Web Conference.
This document describes an application built to show RDF in a user interface. It describes how it gathered the data and also a smushing technique it used. The goal was to determine if it was feasible to integrate data without an underlying ontology. They describe their challenges with the data in this paper.
The key points of this paper:
- To determine if it is possible to integrate data without using an underlying ontology to do so
- They describe their data retrieval process
- They describe how they smush data
- They describe their application and how they make this data available for a user interface
The paper is short and missing a lot of details. Though I referenced this in the "A Machine Learning Approach to Linking FOAF Instances" document, it really was just to give an example of a smushing process.
Wednesday, March 3, 2010
Paper Summary - Semantics for the Semantic Web: The Implicit, the Formal and the Powerful
A. Sheth, C. Ramakrishnan, and C. Thomas. Semantics for the semantic web: The implicit, the
formal, and the powerful. Semantic Web and Information Systems, 1(1), Idea Group, 2005.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.9929&rep=rep1&type=pdf
This paper discusses both the limitation of relying upon just description logics and the need to utilize different semantics to handle the complexity of exploiting semantic web data. In particular it organizes these semantics into three categories:
"Even though it is desirable to have a consistent knowledge base, it becomes impractical as the size of the knowledge base increases or as knowledge from many sources is added. It is rare that human experts in most scientific domains have a full and complete agreement. In these cases it becomes more desirable that the system can deal with inconsistencies."
"Sometimes it is useful to look at a knowledge base as a map. This map can be partitioned according to different criteria, e.g. the source of the facts or their domain. While on such a map the knowledge is usually locally consistent, it is almost impossible and practically infeasible to maintain a global consistency. Experience in developing the Cyc ontology demonstrated this challenge. Hence, a system must be able to identify sources of inconsistency and deal with contradicting statements in such a way that it can still produce derivations that are reliable."
They then go on to discuss current approaches to deal with this inconsistency.
It highlights drawbacks with these methods and proposes the need for a standardization in this area.
The paper then discusses correlating semantic capabilities with types of semantics in relation to the bootstrapping and utilization phases.
The last part of this paper discusses information integration, information retrieval and extraction, data mining and analytical applications.
Some of the interesting papers it references:
R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993.
D. Barbará, H. Garcia-Molina and D. Porter. The Management of Probabilistic Data. IEEE Transactions on Knowledge and Data Engineering, Volume 4 , Issue 5 (October 1992), Pages: 487 - 502
Jochen Heinsohn: Probabilistic Description Logics. UAI 1994: 311-318.
Int’l Journal on Semantic Web & Information Systems, 1(1), 1-18, Jan-March 2005
Hui Han, C. Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. "Two Supervised Learning Approaches for Name Disambiguation in Author Citations" , in Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL 2004), pages 296-305, 2004.
Birger Hjorland Information retrieval, text composition, and semantics. Knowledge Organization 25(1/2):16-31, 1998
Vipul Kashyap, Amit Sheth: Semantic Heterogeneity in Global Information Systems: The Role of Metadata, Context and Ontologies, Cooperative Information Systems 1996
M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. In SIAM International Conference on Data Mining (SDM-04), 2004.
Alexander Maedche, Steffen Staab: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2): 72-79 (2001)
B. Omelayenko. Learning of Ontologies for the Web: the Analysis of Existent approaches. In Proceedings of the International Workshop on Web Dynamics, 2001.
Erhard Rahm, Philip A. Bernstein. A Survey of Approaches to Automatic Schema Matching. In VLDB Journal 10: 4, 2001
Int’l Journal on Semantic Web & Information Systems, 1(1), 1-18, Jan-March 2005
Amit P. Sheth, Sanjeev Thacker, Shuchi Patel: Complex relationships and knowledge discovery support in the InfoQuilt system. VLDB J. 12(1): 2-27 (2003)
J. Townley, The Streaming Search Engine That Reads Your Mind, August 10, 2000. http://smw.internet.com/gen/reviews/searchassociation/
William A. Woods: “Meaning and Links: A Semantic Odyssey”. Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004), June 2-5, 2004. pp. 740-742
Lotfi A. Zadeh. Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. In Journal of Statistical Planning and Inference 105 (2002) 233-264.
formal, and the powerful. Semantic Web and Information Systems, 1(1), Idea Group, 2005.
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.83.9929&rep=rep1&type=pdf
This paper discusses both the limitation of relying upon just description logics and the need to utilize different semantics to handle the complexity of exploiting semantic web data. In particular it organizes these semantics into three categories:
- Implicit - from Patterns in the data, examples include co-occurrence and links
- Formal - formal language which presents syntactical rules, Description Logic falls under this category
- Powerful - statistical analysis that uses patterns in the data
"Even though it is desirable to have a consistent knowledge base, it becomes impractical as the size of the knowledge base increases or as knowledge from many sources is added. It is rare that human experts in most scientific domains have a full and complete agreement. In these cases it becomes more desirable that the system can deal with inconsistencies."
"Sometimes it is useful to look at a knowledge base as a map. This map can be partitioned according to different criteria, e.g. the source of the facts or their domain. While on such a map the knowledge is usually locally consistent, it is almost impossible and practically infeasible to maintain a global consistency. Experience in developing the Cyc ontology demonstrated this challenge. Hence, a system must be able to identify sources of inconsistency and deal with contradicting statements in such a way that it can still produce derivations that are reliable."
They then go on to discuss current approaches to deal with this inconsistency.
- Probabilistic reasoning
- Possibilistic reasoning
- Fuzzy reasoning
It highlights drawbacks with these methods and proposes the need for a standardization in this area.
The paper then discusses correlating semantic capabilities with types of semantics in relation to the bootstrapping and utilization phases.
The last part of this paper discusses information integration, information retrieval and extraction, data mining and analytical applications.
Some of the interesting papers it references:
R. Agrawal, T. Imielinski, and A. N. Swami. Mining association rules between sets of items in large databases. In P. Buneman and S. Jajodia, editors, Proceedings of the 1993.
D. Barbará, H. Garcia-Molina and D. Porter. The Management of Probabilistic Data. IEEE Transactions on Knowledge and Data Engineering, Volume 4 , Issue 5 (October 1992), Pages: 487 - 502
Jochen Heinsohn: Probabilistic Description Logics. UAI 1994: 311-318.
Int’l Journal on Semantic Web & Information Systems, 1(1), 1-18, Jan-March 2005
Hui Han, C. Lee Giles, Hongyuan Zha, Cheng Li, and Kostas Tsioutsiouliklis. "Two Supervised Learning Approaches for Name Disambiguation in Author Citations" , in Proceedings of ACM/IEEE Joint Conference on Digital Libraries (JCDL 2004), pages 296-305, 2004.
Birger Hjorland Information retrieval, text composition, and semantics. Knowledge Organization 25(1/2):16-31, 1998
Vipul Kashyap, Amit Sheth: Semantic Heterogeneity in Global Information Systems: The Role of Metadata, Context and Ontologies, Cooperative Information Systems 1996
M. Kuramochi and G. Karypis. Finding frequent patterns in a large sparse graph. In SIAM International Conference on Data Mining (SDM-04), 2004.
Alexander Maedche, Steffen Staab: Ontology Learning for the Semantic Web. IEEE Intelligent Systems 16(2): 72-79 (2001)
B. Omelayenko. Learning of Ontologies for the Web: the Analysis of Existent approaches. In Proceedings of the International Workshop on Web Dynamics, 2001.
Erhard Rahm, Philip A. Bernstein. A Survey of Approaches to Automatic Schema Matching. In VLDB Journal 10: 4, 2001
Int’l Journal on Semantic Web & Information Systems, 1(1), 1-18, Jan-March 2005
Amit P. Sheth, Sanjeev Thacker, Shuchi Patel: Complex relationships and knowledge discovery support in the InfoQuilt system. VLDB J. 12(1): 2-27 (2003)
J. Townley, The Streaming Search Engine That Reads Your Mind, August 10, 2000. http://smw.internet.com/gen/reviews/searchassociation/
William A. Woods: “Meaning and Links: A Semantic Odyssey”. Principles of Knowledge Representation and Reasoning: Proceedings of the Ninth International Conference (KR2004), June 2-5, 2004. pp. 740-742
Lotfi A. Zadeh. Toward a perception-based theory of probabilistic reasoning with imprecise probabilities. In Journal of Statistical Planning and Inference 105 (2002) 233-264.
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