@incollection{sleb07,
	title = {Complex systems approach to the emergence of language},
	author = {A. Baronchelli and C. Cattuto and V. Loreto and A. Puglisi},
	booktitle = {Language, Evolution and the Brain},
	editor = {J. W. Minett & W. S-Y. Wang},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2b5f5ffae5b1a52c77345881215824131/vittorio.loreto},
	keywords = {2007 baronchelli cattuto complex emergence language loreto minett puglisi sleb systems tagorapub wang }
}

@inproceedings{Wang/2007/Multi-Concept,
	title = {Multi-Concept Alignment and Evaluation},
	author = {Shenghui Wang and Antoine Isaac and Lourens Van der Meij and Stefan Schlobach},
	booktitle = {Proceedings of the Workshop on Ontology Matching (OM2007) at ISWC/ASWC2007, Busan, South Korea},
	crossref = {http://data.semanticweb.org/workshop/om/2007/proceedings},
	editor = {Pavel Shvaiko and Jérôme Euzenat and Fausto Giunchiglia and Bin He},
	month = {November},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/251df320eb9dd128fd3c7f0867b61bc23/iswc2007},
	abstract = {In this paper we discuss a book annotation translation application scenario that requires multi-concept alignment - where one set of concepts is aligned to another set. Using books annotated by concepts from two vocabularies which are to be aligned, we explore two statistically-grounded measures (Jaccard and LSA) to build conversion rules which aggregate similar concepts. Different ways of learning and deploying the multi-concept alignment are evaluated, which enables us to assess the usefulness of the approach for this scenario. This usefulness is low at the moment, but the experiment has given us the opportunity to learn some important lessons.},
	keywords = {2007 alignment evaluation iswc multi-concept workshop_om }
}

@inproceedings{Zhang/2007/Semplore:,
	title = {Semplore: An IR Approach to Scalable Hybrid Query of Semantic Web Data},
	address = {Berlin, Heidelberg},
	author = {Lei Zhang and QiaoLing Liu and Jie Zhang and Haofen Wang and Yue Pan and Yong Yu},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {645--658},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/645.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/242031598526676c9798f1210d9cf74cb/iswc2007},
	abstract = {As an extension to the current Web, Semantic Web will not only contain structured data with machine understandable semantics but also textual information.  While structured queries can be used to find information more precisely on the Semantic Web, keyword searches are still needed to help exploit textual information.  It thus becomes very important that we can combine precise structured queries with imprecise keyword searches to have a hybrid query capability. In addition, due to the huge volume of information on the Semantic Web, the hybrid query must be processed in a very scalable way. In this paper, we define such a hybrid query capability that combines unary tree-shaped structured queries with keyword searches. We show how existing information retrieval (IR) index structures and functions can be reused to index semantic web data and its textual information, and how the hybrid query is evaluated on the index structure using IR engines in an efficient and scalable manner. We implemented this IR approach in an engine called Semplore. Comprehensive experiments on its performance show that it is a promising approach. It leads us to believe that it may be possible to evolve current web search engines to query and search the Semantic Web. Finally, we breifly describe how Semplore is used for searching Wikipedia and an IBM customer's product information.},
	keywords = {2007 application_software approach data_management datum hybrid ir iswc query research_13 scalable semantic semantic_web web }
}

@inproceedings{Wang/2007/PORE:,
	title = {PORE: Positive-Only Relation Extraction from Wikipedia Text},
	address = {Berlin, Heidelberg},
	author = {Gang Wang and Yong Yu and Haiping Zhu},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {575--588},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/575.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/217924e4426a88a55c12154b5b70676e0/iswc2007},
	abstract = {Extracting semantic relations is of great importance for the creation of the Semantic Web content. It is of great benefit to semi-automatically extract relations from the free text of Wikipedia using the structured content readily available in it. Pattern matching methods that employ information redundancy cannot work well since there is not much redundancy information in Wikipedia, compared to the Web. Multi-class classification methods are not reasonable since no classification of relation types is available in Wikipedia. In this paper, we propose PORE (Positive-Only Relation Extraction), for relation extraction from Wikipedia text. The core algorithm B-POL extends a state-of-the-art positive-only learning algorithm using bootstrapping, strong negative identification, and transductive inference to work with fewer positive training examples. We conducted experiments on several relations with different amount of training data. The experimental results show that B-POL can work effectively given only a small amount of positive training examples and it significantly outperforms the original positive learning approaches and a multi-class SVM. Furthermore, although PORE is applied in the context of Wikipedia, the core algorithm B-POL is a general approach for Ontology Population and can be adapted to other domains.},
	keywords = {2007 extraction information_extraction iswc natural_language_processing relation research_04 semantic_web text web_annotation wikipedia }
}

@inproceedings{Zhao/2007/Semantic,
	title = {Semantic Cooperation and Knowledge Reuse by Using Autonomous Ontologies},
	address = {Berlin, Heidelberg},
	author = {Yuting Zhao and Kewen Wang and Rodney Topor and Jeff Pan and Fausto Giunchiglia},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {659--672},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/659.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/20133b444332d9d7cefc865549891ffa0/iswc2007},
	abstract = {Several proposals have been put forward to support distributed agent cooperation in the Semantic Web, by allowing concepts and roles in one ontology be reused in another ontology. In general, these proposals reduce the autonomy of each ontology by defining the semantics of the ontology to depend on the semantics of the other ontologies.
We propose a new framework for managing autonomy in a set of cooperating ontologies (or ontology space). In this framework, each language entity (concept/role/individual) in an ontology may have its meaning assigned either locally with respect to the semantics of its own ontology, to preserve the autonomy of the ontology, or globally with respect to the semantics of any neighbouring ontology in which it is defined, thus enabling semantic cooperation between multiple ontologies.
In this way, each ontology has a "subjective semantics" based on local interpretation and a "foreign semantics" based on semantic binding to neighbouring ontologies.  We study the properties of these two semantics and describe the conditions under which entailment and satisfiability are preserved.  We also introduce two reasoning mechanisms under this framework: "cautious reasoning" and "brave reasoning". Cautious reasoning is done with respect to a local ontology and its neighbours (those ontologies in which its entities are defined); brave reasoning is done with respect to the transitive closure of this relationship. This framework is independent of ontology languages. As a case study, for Description Logic ALCN we present two tableau-based algorithms  for performing each form of reasonings and prove their correctness.},
	keywords = {2007 autonomous cooperation iswc knowledge ontology ontology_(computer_science) ontology_alignment research_02 semantic semantic_web software_agent using web_service }
}

@inproceedings{Wang/2007/Integrating,
	title = {Integrating Uncertainty Into Ontology Mapping},
	address = {Berlin, Heidelberg},
	author = {Ying Wang},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {955--959},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/955.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/20ee085d1ee00efa73ac690b0ba04b7ff/iswc2007},
	abstract = {This paper gives an outline of my PhD thesis which describes the integration of managing uncertainty into ontology mapping. Ontology mapping is one of the most important tasks for ontology interoperability and its main aim is to find semantic relationships between entities (i.e. concept, attribute, and relation) of two ontologies, However, in the process of mapping, uncertainty and incompleteness of semantics in the syntactic representation and description of relations between entities in ontologies will lead to imprecise results. If we want to obtain better results, it becomes more significant for the ontology mapping to be able to deal with uncertainty.},
	keywords = {2007 doctoral_consortium integrating iswc mapping ontology uncertainty }
}

@inproceedings{Isaac/2007/empirical,
	title = {An empirical study of instance-based ontology matching},
	address = {Berlin, Heidelberg},
	author = {Antoine Isaac and Lourens Van der Meij and Stefan Schlobach and Shenghui Wang},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {252--266},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/253.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2dc0f2e92eb694f1833568feb66218641/iswc2007},
	abstract = {Instance-based ontology mapping is a promising family of solutions to a class of ontology alignment problems. Instance-based ontology mapping crucially depends on measuring the similarity between sets of annotated instances. In this paper we study how the choice of co-occurrence measures affects the performance of instance-based mapping. To this end, we have implemented a number of different statistical co-occurrence measures. We have prepared an extensive test case using vocabularies of thousands of terms, millions of instances, and hundreds of thousands of co-annotated items, and we have obtained a human Gold Standard judgement for part of the mapping-space. We then study how the different co-occurrence measures and a number of algorithmic variations perform on our benchmark dataset, as compared against the GoldStandard. Our systematic study shows excellent results of instance-based matching in general, where the more simple measures often outperform more sophisticated statistical co-occurrence measures.},
	keywords = {2007 iswc matching ontology research_12 study }
}

@inproceedings{Fu/2007/Making,
	title = {Making More Wikipedians: Facilitating Semantics Reuse for Wikipedia Authoring},
	address = {Berlin, Heidelberg},
	author = {Linyun Fu and Haofen Wang and Haiping Zhu and Huajie Zhang and Yang Wang and Yong Yu},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {127--140},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/127.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2656d594393972bac3d912a5e4639593e/iswc2007},
	abstract = {Wikipedia, a killer application in Web 2.0, has embraced the power of collaborative editing to harness collective intelligence. It can also serve as an ideal Semantic Web data source due to its abundance, influence, high quality and well-structuring. However, the heavy burden of up-building and maintaining such an enormous and ever-growing online encyclopedic knowledge base still rests on a very small group of people. Many casual users may still feel difficulties in writing high quality Wikipedia articles. In this paper, we use RDF graphs to model the key elements in Wikipedia authoring, and propose an integrated solution to make Wikipedia authoring easier based on RDF graph matching, expecting making more Wikipedians. Our solution facilitates semantics reuse and provides users with: 1) a link suggestion module that suggests and auto-completes internal links between Wikipedia articles for the user; 2) a category suggestion module that helps the user place her articles in correct categories. A prototype system is implemented and experimental results show significant improvements over existing solutions to link and category suggestion tasks. The proposed enhancements can be applied to attract more contributors and relieve the burden of professional editors, thus enhancing the current Wikipedia to make it an even better Semantic Web data source.},
	keywords = {2007 collaboration iswc making research_04 semantic_web semantics wikipedia }
}

@inproceedings{Zhou/2007/SPARK:,
	title = {SPARK: Adapting Keyword Query to Semantic Search},
	address = {Berlin, Heidelberg},
	author = {Qi Zhou and Chong Wang and Miao Xiong and Haofen Wang and Yong Yu},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {687--700},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/687.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2ce56748004a2f073a3b016b4e7a4e54a/iswc2007},
	abstract = {Semantic search promises to provide more accurate result than present-day keyword search. However, progress with semantic search has been delayed due to the complexity of its query languages. In this paper, we explore a novel approach of adapting keywords to querying the semantic web: the approach automatically translates keyword queries into formal logic queries so that end users can use familiar keywords to perform semantic search. A prototype system named ‘SPARK’ has been implemented in light of this approach. Given a keyword query, SPARK outputs a ranked list of SPARQL queries as the translation result. The translation in SPARK consists of three major steps: term mapping, query graph construction and query ranking. Specifically, a probabilistic query ranking model is proposed to select the most likely SPARQL query. In the experiment, SPARK achieved an encouraging translation result.},
	keywords = {2007 application_software iswc ontology_(computer_science) query research_06 search semantic semantic_web }
}

@inproceedings{Wang/2007/Ontology,
	title = {Ontology Performance Profiling and Model Examination: First Steps},
	address = {Berlin, Heidelberg},
	author = {Taowei Wang and Bijan Parsia},
	booktitle = {Proceedings of the 6th International Semantic Web Conference and 2nd Asian Semantic Web Conference (ISWC/ASWC2007), Busan, South Korea},
	crossref = {http://data.semanticweb.org/conference/iswc-aswc/2007/proceedings},
	editor = {Karl Aberer and Key-Sun Choi and Natasha Noy and Dean Allemang and Kyung-Il Lee and Lyndon J B Nixon and Jennifer Golbeck and Peter Mika and Diana Maynard and Guus Schreiber and Philippe Cudré-Mauroux},
	month = {November},
	pages = {589--602},
	publisher = {Springer Verlag},
	series = {LNCS},
	url = {http://iswc2007.semanticweb.org/papers/589.pdf},
	volume = {4825},
	year = {2007},
	biburl = {http://www.bibsonomy.org/bibtex/2e68df0e3d7690be181bd4238318f47f2/iswc2007},
	abstract = {"[Reasoner] performance can be scary, so much so, that we cannot deploy the technology in our products." -- Michael Shepard (http://lists.w3.org/Archives/Public/public-owl-dev/2007JanMar/0047.html).  What are typical OWL users to do when their favorite reasoner never seems to return?  In this paper, we present our first steps considering this problem.  We describe the challenges and our approach, and present a prototype tool to help users identify reasoner performance bottlenecks with respect to their ontologies.  We then describe 4 case studies on synthetic and real-world ontologies.  While the anecdotal evidence suggests that the service can be useful for both ontology developers and reasoner implementors, much more is desired.},
	keywords = {2007 formal_language human-computer_interaction iswc model ontology ontology_(computer_science) performance profiling reasoning research_07 semantic_web step }
}