Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition
Keyword Searching and Browsing in Databases using BANKS
ICDE '02 Proceedings of the 18th International Conference on Data Engineering
A spreading activation network model for information retrieval
A spreading activation network model for information retrieval
Message Understanding Conference-6: a brief history
COLING '96 Proceedings of the 16th conference on Computational linguistics - Volume 1
COLING '90 Proceedings of the 13th conference on Computational linguistics - Volume 2
SemRank: ranking complex relationship search results on the semantic web
WWW '05 Proceedings of the 14th international conference on World Wide Web
Bidirectional expansion for keyword search on graph databases
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Multilingual and cross-lingual news topic tracking
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
BLINKS: ranked keyword searches on graphs
Proceedings of the 2007 ACM SIGMOD international conference on Management of data
Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity
ICSC '07 Proceedings of the International Conference on Semantic Computing
IdentityRank: Named Entity Disambiguation in the Context of the NEWS Project
ESWC '07 Proceedings of the 4th European conference on The Semantic Web: Research and Applications
Word Sense Disambiguation as the Primary Step of Ontology Integration
DEXA '08 Proceedings of the 19th international conference on Database and Expert Systems Applications
Top-k Exploration of Query Candidates for Efficient Keyword Search on Graph-Shaped (RDF) Data
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Effective XML Keyword Search with Relevance Oriented Ranking
ICDE '09 Proceedings of the 2009 IEEE International Conference on Data Engineering
Word sense disambiguation with spreading activation networks generated from thesauri
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Context and Domain Knowledge Enhanced Entity Spotting in Informal Text
ISWC '09 Proceedings of the 8th International Semantic Web Conference
A knowledge-based approach to named entity disambiguation in news articles
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Ontology-driven automatic entity disambiguation in unstructured text
ISWC'06 Proceedings of the 5th international conference on The Semantic Web
Entity reference resolution via spreading activation on RDF-Graphs
ESWC'10 Proceedings of the 7th international conference on The Semantic Web: research and Applications - Volume Part I
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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.