In this paper, we present a new framework to extract knowledge
from today’s non-semantic web. It associates semantics with the
information extracted, which improves agent interoperability; it can also
deal with changes to the structure of a web page, which improves adaptability;
furthermore, it achieves to delegate the knowledge extraction
procedure to specialist agents, easing software development and promoting
software reuse and maintainability.Comisión Interministerial de Ciencia y Tecnología TIC 2000–1106–C02–01Comisión Interministerial de Ciencia y Tecnología FIT-150100-2001-7

Arjona Fernández, José Luis

Corchuelo Gil, Rafael

Peña Siles, Joaquín

Ruiz Cortés, David

Toro Bonilla, Miguel

English

idUS. Depósito de Investigación Universidad de Sevilla

Knowledge ChannelsBringing the Knowledge on the Web to Software AgentsJ.L. Arjona, R. Corchuelo, D. Ruiz, J. Peña, and M. ToroEscuela Técnica Superior de Ingenieŕıa Informática de la Universidad de SevillaDepartamento de Lenguajes y Sistemas InformáticosAvda. de la Reina Mercedes, s/n, Sevilla (SPAIN){arjona}@lsi.us.esAbstract. In this paper, we present a new framework to extract knowl-edge from today’s non-semantic web. It associates semantics with theinformation extracted, which improves agent interoperability; it can alsodeal with changes to the structure of a web page, which improves adapt-ability; furthermore, it achieves to delegate the knowledge extractionprocedure to specialist agents, easing software development and promot-ing software reuse and maintainability.Keywords: knowledge extraction, wrappers, and ontologies.1 IntroductionIn recent years, the web has consolidated as one of the most important knowl-edge repositories. Furthermore, the technology has evolved to a point in whichsophisticated new generation web agents proliferate. A major challenge for themhas become sifting through an unwieldy amount of data to extract meaningfulinformation. This process is difficult because of the following reasons: first, theinformation on the web is mostly available in human-readable forms that lackformalised semantics that would help agents use it [1]; second, the informationsources are likely to change their structure, which usually has an impact on theirpresentation but not on their semantics [2, 11, 14].Our proposal provides agent developers with a framework in which theycan have access to semantically-meaningful data that resides on heterogeneous,user-friendly web pages. It relies on using a number of agents [16] that we callknowledge channels, or KCs for short. KC’s allow to separate the extraction ofknowledge from the logic of an agent, and they are able to react to knowledgeinquiries (reactivity) from other agents (social ability), and act in the background(autonomy) to maintain a local knowledge base (KB) with knowledge extractedfrom a web site (proactivity). In order to allow for semantic interoperability, theknowledge they manage references a number of concepts in a given applicationdomain that are described by means of ontologies [4]. The work reported in this article was supported by the Spanish Inter-ministerialCommission on Science and Technology under grants TIC2000-1106-C02-01 andFIT-150100-2001-782 Related workSeveral authors have worked on techniques for extracting information from to-day’s non-semantic web, and inductive wrappers are amongst the most popularones [3, 9, 10, 12]. They are components that use automated learning techniquesto extract information from similar pages automatically. Although inductionwrappers are suited to extract information from the web, they do not associatesemantics with the data extracted, this being their major drawback.The Web-KB (World Wide Knowledge Base) [6] project at CMU aims todevelop a probabilistic, symbolic knowledge base that mirrors the content ofthe web. It uses several machine learning algorithms for this task. Adding thesealgorithms to logic that a web agent encapsulates, can produce tangled code anddoes not achieve a clear separation of concerns. Furthermore, the informationthat resides in a web page may change unexpectedly; this changes may invalidatethe knowledge stored in the KB.Our solution builds on the best of current inductive wrappers, and extendsthem with techniques that allow us to deal with web knowledge. Using inductivewrappers allows us take advantage of all the work developed in this arena, asboosted techniques or verification algorithms [10, 13] that detect if there arechanges in the layout of a web page that invalidate the wrapper.3 Knowledge ChannelsFigure 1 sketches the architecture of our proposal. KCs are core agents respon-sible for managing local knowledge base (KB). Knowledge is extracted from aweb site using semantic wrappers. Before storing the knowledge in the KB, it isverified using semantic verification to check the existing relations amongst thedifferent concepts that give semantics to the information extracted. Thus, a KCcan answer inquiries from other agents that need some knowledge to accomplishits goals.Knowledge channelWEBKnowledgeBaseComunicationModuleAgentSocietyKCControlSemantic VerificationSyntacticWrapperSemanticTranslatorSemantic WrapperFig. 1. A Knowledge channelThere are many formalisms to dealt with knowledge, namely: semantic net-works, frames systems, logic, decision trees, and so on. KCs use the DARPAAgent Markup Language (DAML+OIL) [15], and FaCT [8] provides1 reason-ing services on the TBox (assertions on concepts) and the ABox (assertions onindividuals) that represent the extracted knowledge.We use ACL [7] as a transport language to send messages amongst KC anduser software agents in the agent society. The content of the messages describeshow an agent wants to interact with another, and it is written in DAML+OILbased on an ontology that defines communication [5].3.1 Semantic WrappersA semantic wrapper takes a web page as input, and returns a set of instances ofconcepts defined in an ontology that represents the information of interest. It iscomposed of an inductive wrapper and a semantic translator. In order to extractknowledge from the web, it is necessary to feed the semantic wrapper with theweb page that contains the information. The inductive wrapper extracts thestructured information from that web page, and the semantic translator assignsthen meaning to it by means of an ontology.Using inductive wrappers allows us to apply syntactic verifiers to the in-formation extracted. They are algorithms that aims at decide if the wrapperworks correctly, or on the contrary, is it invalid because of changes in the webpage structure. For instance, the algorithm Rapture [10] defined by Kushmer-ick uses statistical features, such as length, number of words, number of specialcharacters etc. to characterize the extracted data. It learns the parameters ofnormal distributions describing the feature distributions of the extracted data.This information helps to decide if the Wrapper is valid by means of analysingthe statistical values of the information extracted.The semantic translator needs the user to specify a semantic description thatmaps the information to be extracted with assertions on individuals defined inthe TBox to perform this task.4 ConclusionsThe current web is mostly user–oriented. The semantic web shall help extractinformation with well–defined semantics, regardless of the way it is rendered,but it does not seem it is going to be adopted in the immediate future, whichargues for another solution to the problem in the meanwhile.In this article, we have presented a new framework to knowledge extractionfrom web sites based on semantic wrappers. It is based on specialised knowledgechannels agents that extract information from the web. It improves on otherproposals in that it associates semantics with the extracted information, and canalso deal with changes because the information is extracted by means of current1 A simple translation of DAML+OIL into SHIQ is previously required.wrappers. Furthermore, our proposal achieves a separation of the knowledgeextraction procedure from the base logic that web agents encapsulate, thus easingboth development and maintenance.References1. T. Berners-Lee, J. Hendler, and O. Lassila. The semantic Web. Scientific American,284(5):34–43, May 2001.2. B.E. Brewington and G. Cybenko. Keeping up with the changing web. Computer,33(5):52–58, May 2000.3. W.W. Cohen and L.S. Jensen. A structured wrapper induction system for extract-ing information from semi-structured documents. In Proceedings of the Workshopon Adaptive Text Extraction and Mining (IJCAI-2001), 2001.4. O. Corcho and A. Gómez-Pérez. A road map on ontology specification languages.In Workshop on Applications of Ontologies and Problem solving methods. 14thEuropean Conference on Artificial Intelligence (ECAI’00), 2000.5. S. Cranefield and M. Purvis. Generating ontology-specific content languages. InProceedings of Ontologies in Agent Systems Workshop (Agents’01), pages 29–35,2000.6. Mark Craven, Dan DiPasquo, Dayne Freitag, Andrew K. McCallum, Tom M.Mitchell, Kamal Nigam, and Seán Slattery. Learning to construct knowledge basesfrom the World Wide Web. Artificial Intelligence, 118(1/2):69–113, 2000.7. FIPA. FIPA specifications. Technical Report http://www.fipa.org/specifications,The Foundation for Intelligent Physical Agents, 2000.8. Ian Horrocks, Ulrike Sattler, and Stephan Tobies. Practical reasoning for ex-pressive description logics. In Harald Ganzinger, David McAllester, and AndreiVoronkov, editors, Proceedings of the 6th International Conference on Logic forProgramming and Automated Reasoning (LPAR’99), number 1705, pages 161–180.Springer-Verlag, 1999.9. C.A. Knoblock, K. Lerman, S. Minton, and I. Muslea. Accurately and reliably ex-tracting data from the web: A machine learning approach. IEEE Data EngineeringBulletin, 23(4):33–41, 2000.10. N. Kushmerick. Wrapper verification. World Wide Web Journal, 3(2):79–94, 2000.11. L. Lim, M. Wang, S. Padmanabhan, J.S. Vitter, and R. Agarwal. Characterizingweb document change. Lecture Notes in Computer Science, 2118:133–144, 2001.12. Ling Liu, Calton Pu, and Wei Han. XWRAP: An XML-enabled wrapper construc-tion system for web information sources. In ICDE, pages 611–621, 2000.13. I. Muslea, S. Minton, and C. Knoblock. STALKER: Learning extraction rules forsemistructured, web–based information sources. In Proceedings of the AAAI-98Workshop on AI and Information Integration, 1998.14. B. Starr, M.S. Ackerman, and M. Pazzani. Do-I-Care: Tell me what’s changed onthe Web. In Proceedings of the AAAI Spring Symposium on Machine Learning inInformation Access Technical Papers, March 1996.15. Frank van Harmelen, Peter F. Patel-Schneider, and Ian Horrocks. Reference de-scription of the DAML+OIL (March 2001) ontology markup language. Technicalreport, W3C, March 2001.16. M.J. Wooldridge and M.R. Jennings. Intelligent agents: Theory and practice. TheKnowledge Engineering Review, 10(2):115–152, 1995.

Knowledge Channels: Bringing the Knowledge on the Web to Software Agents

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Knowledge Channels: Bringing the Knowledge on the Web to Software Agents

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