Disambiguating entity references within an ontological model
Article No.: 22, Pages 1 - 11
Abstract
In our everyday conversations, entities (persons, companies, etc.) are referred to by natural language identifiers (NLIs). Humans employ personal experience and situational context to interpret such identifiers. However, due to ambiguity, even humans run the risk of misinterpretations. In our prior work, we presented a novel method to resolve entity references in texts under the aspect of ambiguity. We explore ontological background knowledge represented in an RDF(S) graph. The different interpretation possibilities lead to different subgraphs of the underlying ontology, each subgraph describing one consistent, non-ambiguous interpretation of the ambiguous NLIs within the ontological knowledge base. Our domain-independent approach is based on spreading activation and uses a semantic relational ranking. In this paper, we suggest three extensions to our original algorithm. First, we process in a two-step interpretation---instead of the whole original input text---at first hand smaller text windows in order to get more precise reference interpretations through a smaller local text context. Second, we extend the spreading-activation algorithm within the RDF(S) graph towards a bidirectional exploration of edges which shall speed-up the algorithm. Third we use reinforcement learning in order to take advantage of re-occurring information. We present first experimental results with these algorithmic extensions and derive directions for future work.
References
[1]
J. R. Anderson. A Spreading Activation Theory of Memory. Journal of Verbal Learning and Verbal Behavior, 22:261--295, 1983.
[2]
K. Anyanwu, A. Maduko, and A. Sheth. Semrank: ranking complex relationship search results on the semantic web. In WWW '05: Proceedings of the 14th international conference on World Wide Web, pages 117--127, New York, NY, USA, 2005. ACM.
[3]
J. Apresjan. Regular polysemy. Linguistics, 142:5--32, 1973.
[4]
M. Banek, B. Vrdoljak, and A. M. Tjoa. Word Sense Disambiguation as the Primary Step of Ontology Integration. In Proc. DEXA. Springer-Verlag, 2008.
[5]
Z. Bao, T. W. Ling, B. Chen, and J. Lu. Effective xml keyword search with relevance oriented ranking. In ICDE, pages 517--528, 2009.
[6]
G. Bhalotia, A. Hulgeri, C. Nakhe, S. Chakrabarti, and S. Sudarshan. Keyword Searching and Browsing in Databases using BANKS. In Proc. ICDE. IEEE Computer Society, 2002.
[7]
I. Bhattacharya and L. Getoor. Collective Entity Resolution In Relational Data. IEEE Data Eng. Bull., 29(2), 2006.
[8]
A. M. Collins and E. F. Loftus. A Spreading-Activation Theory of Semantic Processing. Psychological Review, 82(6), 1975.
[9]
A. Copestake and T. Briscoe. Semi-productive polysemy and sense extension. Journal of Semantics, 12:15--67, 1995.
[10]
F. De Saussure, C. Bally, A. Sechehaye, R. Harris, and A. Riedlinger. Course in general linguistics. Open Court, 1986.
[11]
N. F. García, J. M. B. del Toro, L. Sánchez, and A. Bernardi. IdentityRank: Named Entity Disambiguation in the Context of the NEWS Project. In Proc. ESWC, 2007.
[12]
N. F. García, J. M. B. del Toro, L. Sánchez, and V. L. Centeno. Semantic Annotation of Web Resources Using IdentityRank and Wikipedia. In Proc. AWIC, 2007.
[13]
R. Grishman and B. Sundheim. Message Understanding Conference - 6: A Brief History. In Proc. Int. Conference on Computational Linguistics, 1996.
[14]
D. Gruhl, M. Nagarajan, J. Pieper, C. Robson, and A. P. Sheth. Context and Domain Knowledge Enhanced Entity Spotting in Informal Text. In Proc ISWC, 2009.
[15]
M. M. Hasan. A Spreading Activation Framework for Ontology-Enhanced Adaptive Information Access within Organisations. In L. van Elst, V. Dignum, and A. Abecker, editors, Proc. AMKM, volume 2926 of LNCS. Springer, 2004.
[16]
J. Hassell, B. Aleman-Meza, and I. B. Arpinar. Ontology-Driven Automatic Entity Disambiguation in Unstructured Text. In Proc. ISWC, 2006.
[17]
J. Hayes and C. Gutiérrez. Bipartite graphs as intermediate model for rdf. In International Semantic Web Conference, pages 47--61, 2004.
[18]
H. He, H. Wang, J. Yang, and P. S. Yu. BLINKS: Ranked Keyword Searches on Graphs. In Proc. SIGMOD, New York, NY, USA, 2007. ACM Press.
[19]
D. Jurafski and J. H. Martin. Speech and Language Processing. Prentice Hall, 2000.
[20]
V. Kacholia, S. Pandit, S. Chakrabarti, S. Sudarshan, R. Desai, and H. Karambelkar. Bidirectional Expansion for Keyword Search on Graph Databases. In Proc. VLDB, 2005.
[21]
A. Kilgarriff. Polysemy. PhD thesis, University of Sussex, UK, 1992.
[22]
J. Klavans. Polysemy, Ambiguity and Generativity. Technical report, AAAI Symposium, 1995.
[23]
J. Kleb and A. Abecker. Entity Reference Resolution via Spreading Activation on RDF-Graphs. In Proc. ESWC 2010, Part I, pages 152--166, 2010.
[24]
J. Kleb and R. Volz. Ontology based Entity Disambiguation with Natural Language Patterns. In Proc. ICDIM, 2009.
[25]
V. Malais, L. Gazendam, and H. Brugman. Disambiguating Automatic Semantic Annotation Based on a Thesaurus Structure. In Proc. TALN, 2007.
[26]
H. T. Nguyen and T. H. Cao. A Knowledge-Based Approach to Named Entity Disambiguation in News Articles. In Proc. AI, 2007.
[27]
H. T. Nguyen and T. H. Cao. Named Entity Disambiguation on an Ontology Enriched by Wikipedia. In Proc RIVF 2008, 2008.
[28]
G. Pethö. What Is Polysemy? A Survey of Current Research and Results. In K. Bibok and E. Németh, editors, Pragmatics and the Flexibility of Word Meaning, pages 175--224, Amsterdam, 2001. Elsevier.
[29]
B. Pouliquen, R. Steinberger, C. Ignat, E. Käsper, and I. Temnikova. Multilingual and Cross-lingual News Topic Tracking. In Proc. COLING. ACL, 2004.
[30]
S. Preece. A Spreading Activation Network Model for Information Retrieval. PhD thesis, University of Illinois, Urbana-Champaign, 1985.
[31]
M. Quillian. A Revised Design for an Understanding Machine. Mechanical Translation, 7(1):17--29, 1962.
[32]
M. Rada. Unsupervised Large-Vocabulary Word Sense Disambiguation with Graph-based Algorithms for Sequence Data Labeling. In Proc. HLT. ACL, 2005.
[33]
S. Sekine and H. Isahara. IREX: IR and IE Evaluation project in Japanese. In Conference on Language Resources and Evaluation, 2000.
[34]
R. Sinha and M. Rada. Unsupervised Graph-based Word Sense Disambiguation Using Measures of Word Semantic Similarity. In Proc. ICSC. IEEE Computer Society, 2007.
[35]
T. Tran, H. Wang, S. Rudolph, and P. Cimiano. Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped RDF Data. In Proc. ICDE. IEEE, 2009.
[36]
G. Tsatsaronis, M. Vazirgiannis, and I. Androutsopoulos. Word Sense Disambiguation with Spreading Activation Networks Generated from Thesauri. In Proc IJCAI, 2007.
[37]
J. Veronis and N. M. Ide. Word Sense Disambiguation With Very Large Neural Networks Extracted From Machine Readable Dictionaries. In Proc. COLING. ACL, 1990.
[38]
R. Volz, J. Kleb, and W. Müller. Towards Ontology-based Disambiguation of Geographical Identifiers. In Proc. WWW Workshop I<sup>3</sup>, 2007.
Index Terms
- Disambiguating entity references within an ontological model
Recommendations
Named entity recognition and disambiguation using linked data and graph-based centrality scoring
SWIM '12: Proceedings of the 4th International Workshop on Semantic Web Information ManagementNamed Entity Recognition (NER) is a subtask of information extraction and aims to identify atomic entities in text that fall into predefined categories such as person, location, organization, etc. Recent efforts in NER try to extract entities and link ...
Ontology-based automatic semantic annotation for named entity disambiguation
ISC '07: Proceedings of the 10th IASTED International Conference on Intelligent Systems and ControlThe vision of Semantic Web can be realized when there are masses of machine-processable semantic metadata. Manual construction of metadata is not feasible, methods for automated semantic annotation have been developed. Semantic annotation is the process ...
First Steps in Czech Entity Linking
TSD 2015: Proceedings of the 18th International Conference on Text, Speech, and Dialogue - Volume 9302In this paper, we present our approach for a simplified Entity Linking task in Czech, where entity mentions found in text are linked to a list of known entities. We evaluate both known and newly proposed methods for entity names similarity on a manually ...
Comments
Information & Contributors
Information
Published In
Copyright © 2011 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 25 May 2011
Check for updates
Author Tags
Qualifiers
- Research-article
Conference
WIMS '11
Acceptance Rates
Overall Acceptance Rate 140 of 278 submissions, 50%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 159Total Downloads
- Downloads (Last 12 months)0
- Downloads (Last 6 weeks)0
Reflects downloads up to 02 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in