Flach, P

Price, S

Rawles, S

English

Accurately determining whether two Friend Of A Friend (FOAF) descriptions describe the same person is crucial to the success of FOAF on the Semantic Web. Unless this is possible, FOAF descriptions of the same individual originating from disparate sources can not be merged together, or "smushed", into a single description. Without smushing, data remains locked within the silos of the originating FOAF descriptions, the holistic view is lost and less can logically be stated about any specific individual. Current approaches to smushing correctly recognize that people do not have a URI unique identifier and instead rely on inverse-functional properties (IFP), such as an email address, to act as a weaker proxy for a globally unique identifier upon which to join descriptions. In this paper we point out the inadequacy of this approach in a broad class of FOAF application areas relating to data mining and information extraction. We describe a method capable of smushing numerous automatically extracted partial FOAF descriptions from disparate sources where suitable IFPs are unavailable.Accurately determining whether two Friend Of A Friend (FOAF) descriptions describe the same person is crucial to the success of FOAF on the Semantic Web. Unless this is possible, FOAF descriptions of the same individual originating from disparate sources can not be merged together, or "smushed", into a single description. Without smushing, data remains locked within the silos of the originating FOAF descriptions, the holistic view is lost and less can logically be stated about any specific individual. Current approaches to smushing correctly recognize that people do not have a URI unique identifier and instead rely on inverse-functional properties (IFP), such as an email address, to act as a weaker proxy for a globally unique identifier upon which to join descriptions. In this paper we point out the inadequacy of this approach in a broad class of FOAF application areas relating to data mining and information extraction. We describe a method capable of smushing numerous automatically extracted partial FOAF descriptions from disparate sources where suitable IFPs are unavailable

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                          Price, S., Rawles, S., & Flach, P. (2004). Estimating whether partial FOAFdescriptions describe the same individual. In Workshop on Friend of aFriend, Social Networking and the Semantic Web (FOAF2004), Galway,Ireland.Link to publication record in Explore Bristol ResearchPDF-documentUniversity of Bristol - Explore Bristol ResearchGeneral rightsThis document is made available in accordance with publisher policies. Please cite only the publishedversion using the reference above. Full terms of use are available:http://www.bristol.ac.uk/pure/about/ebr-terms.htmlTake down policyExplore Bristol Research is a digital archive and the intention is that deposited content should not beremoved. However, if you believe that this version of the work breaches copyright law please contactopen-access@bristol.ac.uk and include the following information in your message:• Your contact details• Bibliographic details for the item, including a URL• An outline of the nature of the complaintOn receipt of your message the Open Access Team will immediately investigate your claim, make aninitial judgement of the validity of the claim and, where appropriate, withdraw the item in questionfrom public view.POSITION PAPER SUBMISSION TO FOAF2004 WORKSHOPEstimating whether partial FOAF descriptionsdescribe the same individualSimon Price, Simon Rawles and Peter FlachDepartment of Computer Science, University of Bristol, Bristol BS8 1HH, UK{simon.price, simon.rawles, peter.flach}@bristol.ac.ukhttp://www.cs.bris.ac.ukAbstractAccurately determining whether two Friend Of A Friend (FOAF) descriptions describe the same personis crucial to the success of FOAF on the Semantic Web. Unless this is possible, FOAF descriptions ofthe same individual originating from disparate sources can not be merged together, or "smushed", into asingle description. Without smushing, data remains locked within the silos of the originating FOAFdescriptions, the holistic view is lost and less can logically be stated about any specific individual.Current approaches to smushing correctly recognize that people do not have a URI unique identifier andinstead rely on Inverse Functional Properties (IFP), such as an email address, to act as a weaker proxyfor a globally unique identifier upon which to join descriptions. In this paper we point out theinadequacy of this approach in a broad class of FOAF application areas relating to data mining andinformation extraction. We describe a method capable of smushing numerous automatically extractedpartial FOAF descriptions from disparate sources where suitable IFPs are unavailable.IntroductionThe initial wave of Friend Of A Friend (FOAF) [1] documents published on the Semantic Web [2] werelargely authored by the person described in the document's Resource Description Framework (RDF)metadata [3]. To a large extent, each person's own FOAF document (or documents in some cases)served as the definitive self-description of the document's author. Importantly, these purpose-writtenFOAF documents almost universally contain the necessary Inverse Functional Properties (IFP) [4], suchas an email address, upon which different RDF descriptions of an individual may be merged. So for thisinitial wave of FOAF data, leaving aside issues of trust and authenticity, smushing [5] is as trivial as arelational database join on key fields.Since the initial wave of FOAF self publishing it has become increasingly common for FOAF data to becreated automatically from traditional databases (e.g. PARIP membership database [6]) or as part of ahosted web application (e.g. TypePad Weblog service [7]). FOAF from sources such as these also tendsto have IFPs suitable for smushing. However, this is not necessarily the case for what we expect to bethe next wave of automatic FOAF generation using data mining and information extraction softwareagents and services. A broad class of FOAF applications is possible using machine learning and datamining techniques to extract information about people from web pages, newsgroups, instant messaging,emails, documents and other electronic resources. Separate FOAF descriptions of the same individual,originating from such diverse sources, are likely to be incomplete and only partially overlapping -frequently without a convenient IFP in common. Consequently, smushing these generated FOAFdescriptions is clearly non-trivial and must rely on a range of properties that are neither inversefunctional nor unique identifiers. Any solution to this problem must deal with uncertainty and be capableof expressing degrees of confidence in whether two descriptions refer to the same individual.Overview of Our ApproachOur approach defines a similarity metric across the space of FOAF descriptions and uses this inconjunction with a threshold to determine whether two descriptions should be merged. The overallsimilarity metric is itself a weighted linear combination of simpler similarity metrics that comparespecific combinations of:literal values (e.g. name and phone)URIs referenced (e.g. mbox IFP if available and interest)derived values (e.g. initials from name, top-level domain from mbox)relationships between objects (e.g. the knows and seeAlso graphs)properties and content of external documents retrieved over the Web (e.g. a web page made bythe person)metadata about the FOAF document (e.g. URL, date and referrer)A good proportion of these metrics go way beyond what can logically be concluded from the strictinterpretation of the RDF and OWL semantics of FOAF. For instance, in calculating derived values wetreat literal values as non-atomic and also attempt to interpret most URIs as URLs of online resourcesrather than as simple identifiers. Our metrics are calculated by hand-crafted, domain-specific heuristicsmodelled on our own intuition of how we might look for clues about identity in the absence of IFPs. Forexample, given two FOAF descriptions d1 and d2, it is not legal to conclude that a foaf:mbox literal of"mailto:simon.price@bristol.ac.uk" in d1 is quite probably co-referent with the person in d2 with afoaf:name of "Simon Price" and foaf:workplaceHomepage of "http://www.bristol.ac.uk". Our metricsaim to capture this common-sense intuition and express any associated uncertainty in the metric valuethrough the weighted combination of individual similarity metrics into a single overall metric.IFPs can be accommodated and exploited where available by assigning their similarity metric (e.g. astring comparison predicate) a near maximal confidence weighting. However, depending on how theweights are combined, such a scheme could still see an IFP-based metric masked by a less reliablemetric (e.g. based on name). For this reason, we use a "defaulting list" of procedures depending on theavailability of data, both from internal and external sources, and each procedure has a confidenceassociated with it, which decreases for each successive procedure.ImplementationAfter investigation we concluded that it was impractical to calculate the similarity metrics directly fromthe raw FOAF RDF triples. There are many ways to express the same thing in FOAF and someproperties, such as those relating to a person's name, are not independent of each other. We decided tonormalise and clean the data into our own intermediate 'internal' representation first. Besides, if this pre-processing were not done then normalisation and cleaning would have to be performed within thesimilarity functions themselves, considerably adding to their complexity. Given the highly structurednature of FOAF data we opted for an object-orientated representation, preferring strongly typed accessorand similarity methods for computational convenience. This intermediate representation is implementedas an SWI-Prolog module that provides a cleaned and normalised object-orientated view of the rawRDF triples. In the current implementation this is a view rather than a true pre-processing of the data andso has advantages in terms of currency but at the expense of increased computation.Our test data is based on aggregated FOAF files from the Web collected using a purpose-written RDFcrawler in SWI-Prolog. Clearly this is not representative of the fragmented partial descriptions that weare interested in; rather it is what we might expect to be the end-result of a successful smushing of partialdata into more complete descriptions. Therefore, we artificially fragment the data using software,removing the IFP and other selective properties in a controlled way. This gives us precise control overthe distribution and range of properties in FOAF fragments. At the same time it avoids the need tomanually label positive and negative examples of smushing.Future WorkAt the time of writing, we are continuing to develop the implementation to include a broad range ofsimilarity metrics for comparing heterogeneous values. We plan to use the semi-artifically generateddatasets as training data for machine learning algorithms in order to learn metric weights for combininginto the single overall metric and to determine the threshold for smushing two descriptions.References[1] Dan Brickley, Libby Miller, FOAF Vocabulary Specification, http://xmlns.com/foaf/0.1/[2] Eric Miller et al, W3C Semantic Web Activity, http://www.w3.org/2001/sw/[3] Frank Manola, Eric Miller, eds., Resource Description Framework (RDF),http://www.w3.org/RDF/[4] Eric Miller, Jim Hendler eds., Web Ontology Language (OWL), http://www.w3.org/2004/OWL/[5] Dan Brickley, Smushing, http://rdfweb.org/topic/Smushing[6] Angela Piccini, PARIP, http://www.bris.ac.uk/parip[7] TypePad, http://www.typepad.com/

Eric Miller et al, W3C Semantic Web Activity,

Estimating whether partial FOAF descriptions describe the same individual

https://research-information.bris.ac.uk/files/8424107/partial_foaf_descriptions.pdf

Estimating whether partial FOAF descriptions describe the same individual

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