Web Document Compaction by Compressing URI references in RDF and OWL Data

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1 Web Document Compaction by Compressing URI references in RDF and OWL Data Kisung Lee Telematics USN Research Division Electronics and Telecommunications Research Institute 138, Gajeongno, Yuseong-gu Daejeon, Korea Jin Hyun Son Department of Computer Science & Engineering Hanyang University 1271, Sa-1 dong, Sangnok-gu Ansan, Kyeonggi-do, Korea Gun-Woo Kim Department of Computer Science & Engineering Hanyang University 1271, Sa-1 dong, Sangnok-gu Ansan, Kyeonggi-do, Korea Myoung Ho Kim Department of Computer Science Korea Advanced Institute of Science and Technology 373-1, Guseong dong, Yuseong-gu Daejeon, Korea ABSTRACT The enormous web documents in WWW have made it dramatically difficult to retrieve meaningful information which we really want to find out. The Semantic Web technology has been considered to solve the problem and RDF and OWL have emerged as standards for the Semantic Web documents by W3C. The RDF/XML, an XML representation of RDF and OWL, documents are very large as compared to their intrinsic data size because of their inherent verbosity from XML. These large-size RDF/XML documents are inefficient in data exchanging and archiving. In this paper, we propose an efficient method for compressing URI references which may cover very large portions of RDF/XML documents. Our compression method compresses URI references using two-level dictionary-based encoding: one for compressing URI parts of URI references and the other for compressing whole URI references. The performance evaluation using real RDF/XML documents shows that our compression method significantly reduces the size of the RDF/XML documents. Keywords Compression, URI References, RDF, OWL, Semantic Web. 1. INTRODUCTION The widespread of the World Wide Web (WWW) has dramatically increased the number of electronic documents. However, this enormous information has made it difficult to find out the meaningful information which we really want to get. One 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. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. of the main reasons for this weakness of WWW is that most current Web documents are manually generated by humans, not automatically by computers. HTML (Hypertext Markup Language), a predominant language in the current Web, is intended to display information. It provides neither the structure nor the semantics of the information. For the HTML document, computers can only recognize that one document includes some characters, links, and HTML tags for displaying the characters. On the other hand, they cannot understand that a word in the document indicates a name of a person or one link links to a rating document for home appliances. Therefore, current keyword-based Web search engines may show too many Web documents including irrelevant documents as their search results. After the search, a person has to browse the retrieved documents until she gets the information she really wants. The concept of the Semantic Web has been proposed to solve this problem. The Semantic Web is not a separate Web but an extension of the current one, in which information is given welldefined meaning, better enabling computers and people to work in cooperation [1]. The development of the Semantic Web will realize much more automated services such as intelligent search engines and agents in diverse areas. The Semantic Web will build on RDF (Resource Description Framework) [14]. RDF is a language for representing information about resources in the Web. RDF is intended for situations in which this information needs to be processed by computers, rather than being only displayed to people like in the HTML-style web documents. The first level above RDF required for the Semantic Web is an ontology language which can formally describe the meaning of terms used in Web documents and their relationships [5]. One representative ontology language is OWL (Web Ontology Language). OWL, like RDF, is also designed to process information in the Web using computers. Similar to the development of WWW, the scale of information in the Semantic Web will be increased dramatically as the Semantic Web has been developed and popularized. Therefore, effective methods for managing, maintaining and processing the large size of Semantic Web data have become more and more

2 important. Research for storing large Semantic Web data written in RDF or OWL to traditional DBMS has already studied [2, 3, 13]. However, currently most RDF and OWL data are written in the form of RDF/XML, an XML representation for RDF and OWL data, and stored in the traditional file systems. Recently, Google returns over one million documents which have RDF extension and over 40 thousands documents which have OWL extension. From this result, we identify that the number of Semantic Web documents in the file systems is increasing rapidly. Thus we need effective methods to store, transmit, archive, and process RDF and OWL documents, stored in the file systems, in the form of RDF/XML. In general, the size of RDF/XML documents is larger than that of their intrinsic documents because those inherit XML verbosity which may use repeatedly same tags to represent information. It causes some critical problems: it wastes disk space required for data storing and archiving, it wastes network bandwidth required for data transmission, and it decreases system performance such as a disk seek-time or a transmission-time. General purpose compressors (e.g. gzip [9]) or special purpose compressors for XML [4, 6, 12] can be used to solve this kind of problems. However, these compression mechanisms do not consider the characteristics of RDF/XML documents, for example, URI(Uniform Resource Identifier) information cover very large portions in RDF/XML documents, RDF/XML documents repeatedly utilize some information by referencing, and so on. In this paper, we propose an efficient method for compressing URI references in RDF/XML documents. Our compaction method compresses URI references using two-level dictionary-based encoding: one for compressing URI itself in a URI reference and the other for compressing whole URI references. The rest of this paper is organized as follows. In Section 2, we present some basic knowledge about URI references and dictionary-based encoding and features of existing XML compressors. Section 3 describes our compression method and Section 4 shows and analyzes the result of performance evaluation. Finally, Section 5 provides concluding remarks. 2. RELATED WORK RDF/XML documents use URIs as the basis for identifying Web resources [14]. Unlike the well-known URL (Uniform Resource Locator) which identifies only directly retrievable resources in the Web, URIs can be used to identify any Web resources such as creators of Web documents and human beings described in Web documents. In this sense, RDF/XML documents use URI references (URIrefs) to identify Web resources. An URIref is an URI with an optional fragment identifier, preceded by #, at the end. URIref for instance, consists of the URI and the fragment identifier item1. Unlike absolute URIrefs as mentioned above, URIrefs can be used as relative form, where some prefix of the URIrefs can be omitted by using a base URI. The base URI is usually an URI that identifies the document which contains the URIrefs. As an example, a relative URIref item2 with its base URI is equivalent to the absolute URIref Dictionary-based encoding methods select strings of symbols from the input document and encode each string as a token (or index) using a dictionary [7]. The dictionary holds strings of symbols predetermined or previously found in the input stream, allowing for additions and deletions of strings as new input is being read. The simplest example of a dictionary is a dictionary of the English language used to compress English text. A word is read from the input stream and the dictionary is searched. If a match is found, an index to the dictionary is written into the output stream. Otherwise, the uncompressed word itself is written. Some famous general purpose compressors (e.g. gzip) use this dictionary-based encoding. XML specific compressors have been proposed since general purpose compressors have shown insufficient performance for compressing XML documents. The most remarkable compressors for XML documents are XMill [4], XGrind [12] and XPress [6]. XMill shows the greatest compression rates among above XML specific compressors and general purpose compressors. It separates the structure, consisting of XML tags and attributes, from data items. The structure is compressed by dictionary-based encoding, i.e. tags and attributes are replaced by integer values. The data items are grouped by their relevancy: for instance, all data items which have Name tags are grouped together. Users are also able to specify the grouping criteria. Data items in the same group can be compressed by specialized compressors if users specify the compressors. Finally, the structure and each group of data items are separately compressed by gzip and merged into one file. XMill is not designed to be used in a query processor and user intervention is necessarily needed to get much better performance. 3. URI REFERENCE COMPRESSION In this section, we propose an efficient method for compressing URIrefs which cover very large portions of RDF/XML documents. We exclude the URIrefs used as element names and attribute names in our method since these URIrefs are already compressed well by the existing dictionary-based encoding mechanisms. Therefore, we consider only URIrefs used as attribute values in this paper. In RDF/XML documents, there are some attributes whose value should be an URIref such as rdf:about, rdf:id, rdf:resource, rdf:datatype and so on. These attributes profusely appear in RDF/XML documents. We call these attributes URIref attribute. Therefore, the purpose of our compression method is to compress values of URIref attributes efficiently. 1. <?xml version="1.0"?> 2. <!DOCTYPE rdf:rdf [<!ENTITY food 3. <rdf:rdf 4. xml:base = " 5. xmlns:food= " 6. xmlns:owl = " 7. xmlns:rdf = " 8. xmlns:rdfs= " 9. <owl:class rdf:id="winegrape"> 10. <rdfs:subclassof rdf:resource="&food;grape" /> 11. </owl:class> 12. <owl:objectproperty rdf:id="madefromgrape"> 13. <rdfs:subpropertyof rdf:resource=" /> 14. <rdfs:domain rdf:resource="#wine" /> 15. <rdfs:range rdf:resource="#winegrape" /> 16. </owl:objectproperty> 17. </rdf:rdf> Figure 1. Diverse representations of URIref attribute values

3 In RDF/XML documents, URIref representations of URIref attribute values are different from those of element and attribute names. In case of element and attribute names, URIrefs are represented in one of two forms: a combination of a namespace prefix and a local part such as <rdf:description> or a sole local part using the default namespace such as <title>. On the other hand, URIref representations of URIref attribute values are represented very diversely. Figure 1 illustrates how various URIref representations of URIref attribute values can be used. URIref attribute values at lines 9, 12, 14 and 15 are abbreviated (or relative) URIrefs which use base URI Therefore, the absolute URIref of WineGrape at line 9 is However, even though they are using the same base URI, their representations are slightly different from them based on the attribute types. For instance, WineGrape at line 9 and #WineGrape at line 15 are different even though they indicate the same URIref Furthermore, some URIrefs are represented by using an entity reference such as the one at line 10 and some URIrefs are represented as an absolute form as line 13. Like these examples, URIref attribute values can be represented as diverse forms. Therefore, for compressing URIref attribute values, if we use simple dictionary-based encoding used to compress element and attribute names, the URIref attribute values cannot be compressed efficiently because the size of the dictionary can be dramatically increased. Our compression method consists of two levels based on the dictionary-based encoding. The first level is to find an URI index of an URIref to be compressed in the URI dictionary. The second level is to find an URIref index in the URIref dictionary by using the URI index, which was already found at the first level, and the fragment identifier of the URIref and, finally, replace the URIref with the URIref index. URI dictionary and URIref dictionary are used for the dictionary-based encoding which compresses URI parts of URIrefs and whole URIrefs, respectively. URI index and URIref index are used to uniquely identify each URI in the URI dictionary and each URIref in the URIref dictionary, respectively, which is similar to the primary key in the relational database. Before the first level compression, we construct empty URI dictionary and URIref dictionary. One element of URI dictionary consists of an URI index, an abbreviated name, and an URI. One element of URIref dictionary consists of an URIref index, an URI index, and a fragment identifier. Initially, we insert URIs of namespace prefix declarations and the base URI in the rdf:rdf element, which is a topmost element in RDF/XML documents, into the URI dictionary. The reason is that these URIs are more likely to be used as URI parts of URIref attribute values. Each time a new URI is inserted into the URI dictionary, a unique integer, as the URI index, is assigned to that URI. We, especially, assigned zero as the URI index for the base URI. Therefore, we can save the time to scan the URI dictionary when an URIref uses the base URI as its URI part. Whenever an URIref attribute value appears in the RDF/XML document, the first level of our compression method is to find the URI index of the URIref. A process of finding the URI index is classified into three cases as follows: (1) When the URIref attribute value uses the base URI: the URI index of the URIref is, of course, zero without accessing to the URI dictionary because the URI index for the base URI is always zero as we mentioned above. (2) When there is already an element in the URI dictionary which indicates the URI of the URIref: the URI index of the URIref is the URI index of that element. (3) When there is no matching element in the URI dictionary for the URI of the URIref: the URI part is extracted from the URIref because the URIref cannot be compressed without the URI index of that. The extraction process is as follows. If the URIref consists of the URI part and the fragment identifier part explicitly, the URI part of the URIref is, of course, extracted as the URI. On the other hand, if the URIref does not include the fragment identifier, we extract the URI by splitting the URIref into two parts based on the last / symbol. After the extraction of the URI part, the extracted URI is inserted into the URI dictionary as a new element and then a unique integer, as the URI index, is assigned to that URI. The newly assigned integer is the URI index of the URIref. The second level of our compression method is to replace the URIref attribute value with the URIref index after finding the URIref index in the URIref dictionary by using the URI index found at the first level and the fragment identifier. A process for finding the URIref index is classified into two cases based on whether there is an element in the URIref dictionary which matches the URI index and fragment identifier of the URIref. (1) When there is already an element in the URIref dictionary which matches the current URIref: we replace the current URIref attribute value with the URIref index of that element. (2) When there is no matching element in the URIref dictionary for the current URIref: a new element, for the current URIref, which includes the URI index found at the first level and the fragment identifier is inserted into the URIref dictionary and then a unique integer, as the URIref index, is assigned for the current URIref. After getting the URIref index, we replace the current URIref attribute value with the newly assigned URIref index. However, if we found the URI index through the case 3 at the first level, we could omit to scan the URIref dictionary for finding the matching element because there would be no matching element for the current URIref in the URIref dictionary. As we mentioned above, during the process of finding the matching element in the URIref dictionary, if the current URIref uses the base URI, we consider that URIref WineGrape of attribute rdf:id and URIref #WineGrape of attribute rdf:resource are completely same URIrefs. The RDF/XML document in Figure 1 is compressed to the XML document in Figure 2 by our compression method. The generated URI dictionary and URIref dictionary after the compression are Table 1 and Table 2, respectively. Similar to the URIref attribute values, URIs of namespace prefix declarations and the base URI in the rdf:rdf element are also compressed by using the URI indexes of the URI dictionary (line 2 in Figure 2).

4 1. <?xml version="1.0"?> 2. <rdf:rdf xml:base="0" xmlns:food="1" xmlns:owl="2" xmlns:rdf="3" xmlns:rdfs="4"> 3. <owl:class rdf:id="1"> 4. <rdfs:subclassof rdf:resource="2"/> 5. </owl:class> 6. <owl:objectproperty rdf:id="3"> 7. <rdfs:subpropertyof rdf:resource="4"/> 8. <rdfs:domain rdf:resource="5"/> 9. <rdfs:range rdf:resource="1"/> 10. </owl:objectproperty> 11.</rdf:RDF> Figure 2. Compressed version of Figure 1 URIref WineGrape (line 9) in Figure 1 is the first URIref attribute value in the process of the compression. Because it uses the base URI, we scan the URIref dictionary, without accessing to the URI dictionary, to find an element which has 0 and WineGrape as the URI index and the fragment identifier, respectively. However, there is no such an element because the URIref is the first URIref attribute value in the document. Therefore, a new element which has 0 and WineGrape is inserted into the URIref dictionary, and URIref index 1 is assigned to that element. The URIref is compressed by replacing it with 1 (at line 3 in Figure 2). Table 1. Generated URI dictionary after the compression URI index abbr. name URI 0 xml:base 1 food 2 owl 3 rdf 4 rdfs Table 2. Generated URIref dictionary after the compression URIref index URI index fragment identifier 1 0 WineGrape 2 1 Grape 3 0 madefromgrape 4 5 madefromfruit 5 0 Wine The absolute URIref of URIref &food;grape (line 10) in Figure 1 is The URI part of that, is already included in the URI dictionary in which an element of URI index 1 indicates that URI. After finding 1 as the URI index, we scan the URIref dictionary to find an element which has 1 and Grape as the URI index and the fragment identifier, respectively. Because there is no such an element in the URIref dictionary, a new element for that URIref is inserted into the URIref dictionary and URIref index 2 is assigned to that element. The URIref is compressed by replacing it with 2 (line 4 in Figure 2). For URIref (line 13) in Figure 1, since there is no matching element in the URI dictionary for the URI part of that URIref, we extract as the URI of that URIref through the extraction process of the first level s case 3. The extracted URI is inserted into the URI dictionary as a new element and URI index 5 is assigned to that element. After that, we insert a new element which has 5 and madefromfruit as the URI index and the fragment identifier, respectively, into the URIref dictionary without scanning the URIref dictionary as we already mentioned above. URIref index 4 is assigned to the new element and then the URIref is compressed by replacing it with the index (at line 7 in Figure 2). Because URIref #WineGrape (line 15) in Figure 1 uses the base URI, we first scan the URIref dictionary to find an element which has 0 and WineGrape as the URI index and the fragment identifier, respectively. Note that, as we already mentioned, we use WineGrape instead of #WineGrape to find a matching element. After we find a matching element whose URIref index is 1, the URIref is compressed by replacing it with URIref index 1 (at line 9 in Figure 2). After the above compression process, the URI dictionary and URIref dictionary of Table 1 and Table 2 are also stored in the compressed file because these dictionaries are required to decompress the compressed document. 4. PERFORMANCE EVALUATION 4.1 RDF/XML Documents To evaluate our compression method, we used 6 real RDF/XML documents. The characteristics of the documents are shown in Table 3. Size denotes the size of the document in kilobytes. We use documents of various sizes from 44.3KB to 75.8MB. Frequency/URI denotes the average occurrences of one URIref in the document. Except pizza document in which an URIref is occurred, on the average, more than 13 times, Frequency/URI values of the documents are around 4. #URIs/KB denotes the average number of URIref attribute values in the portions of 1 KB in the document. We extracted this characteristic to identify the portions of the URIref attribute values in the document. However, it could not show, of course, the exact size of URIref portions because we only counted the occurrences, not the size in bytes of URIref attribute values. Table 3. RDF/XML documents Document Name Size(KB) Frequency/URI #URIs/KB food wine pizza tambis csdgm thesaurus The food and wine documents of [8] are representative RDF/XML documents used as sample ontologies in the OWL specifications. The pizza document is a sample ontology used in the protégé, a representative ontology editor, tutorial. The tambis is an ontology of biological science domain. The csdgm is an ontology for digital geospatial metadata. The thesaurus is a cancer focused terminology of NCI(National Cancer Institute) [10, 11]. 4.2 Experimental Results First, we compared the compressed file, compressed only by our compression method, with the original file. Figure 3 shows the original file size and the compressed file size of above six RDF/XML documents. We normalized the file sizes by setting the

5 original file size to 1. The URI compressed file includes the generated XML file in which URIref attribute values are replaced with URIref indexes, the URI dictionary file, and the URIref dictionary file. Normalized File Size original URI compressed food wine pizza tambis csdgm thesaurus Figure 3. Compression results after applying only URIref compression gzip XMill URI compression with XMill among them. That is the ratio of the original file size to the compressed file size. The metric is denoted as equation (1): size of the original file compressio n factor = (1) size of the compressed file Figure 4 shows compression factors of above six RDF/XML documents. The average compression factor of our compressor is about 19.5, 39.5% better than that of gzip and 21.2% better than that of XMill, on the average. This result indicates that our compression method provides better compression factors than existing compressors by compressing URIref attribute values efficiently. 5. CONCLUSIONS In this paper, we propose a method for compressing URIref attribute values efficiently, which is based on the dictionary-based encoding. We have also implemented a compressor for the proposed compression method and evaluated its performance. On the average, the file size compressed by our compressor is about four-fifths of that of the original file. It shows that we are able to reduce the document size considerably by compressing only URIref attribute values efficiently. We also integrated our compressor with XMill and then compared it with existing compressors. On the average, the compression factor of our compressor is 19.5, 39.5% & 21.2% better than that of gzip and XMill, respectively. Compression Factor food wine pizza tambis csdgm thesaurus 6. ACKNOWLEDGMENTS This research was supported by the Ministry of Information and Communication, Korea, under the College Information Technology Research Center Support Program, grant number IITA-2006-C This work was supported by the Korea Science and Engineering Foundation (KOSEF) grant funded by the Korea government (MOST) (No. R ) Figure 4. Compression results The compressed file size reduced to, on the average, under four-fifths of the original file size. Except the thesaurus document which has much smaller portions of URIref attribute values than the others, the size of compressed documents is about three quarters of the original file size. This result shows the potential that we are able to reduce the size of RDF/XML documents significantly by only managing URIref attribute values efficiently because that s a result of applying only our URIref compression method. Even, the compressed file, except the URI and URIref dictionary files, is a well-formed XML file: it means that we did not apply simple encoding such as the dictionary-based encoding for the element names and attribute names or type-specific encoding for the element data values. As a second experiment, we integrated our compressor with XMill, known as the best XML compressor for reducing document size, to compare our compression performance with that of existing compressors: we chose XMill and gzip, a representative general purpose compressor. We used the compression factor [7] to compare the compression performance 7. REFERENCES [1] Tim Berners-Lee, James Hendler and Ora Lassila. The Semantic Web. Scientific American, May [2] Jeen Broekstra, Arjohn Kampman and Frank van Harmelen. Sesame: A generic Architecture for Storing and Querying RDF and RDF Schema. In First International Semantic Web Conference (ISWC), [3] Juhnyoung Lee and Richard Goodwin. Ontology Management for Large-Scale E-Commerce Applications. In International Workshop on Data Engineering Issues in E- Commerce (DEEC 2005), April [4] Hartmut Liefke and Dan Suciu. XMill: An Efficient Compressor for XML Data. In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, pages , May [5] Deborah L. McGuinness and Frank van Harmelen. OWL Web Ontology Language Overview. World Wide Web Consortium, Recommendation REC-owl-features , February 2004.

6 [6] Jun-Ki Min, Myung-Jae Park and Chin-Wan Chung. XPRESS: A Queriable Compression for XML Data. In Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, [7] David Salomon. Data Compression: The Complete Reference (Fourth Edition), Springer-London, [8] Michael K. Smith, Chris Welty and Deborah L. McGuinness. OWL Web Ontology Language Guide. World Wide Web Consortium, Recommendation REC-owl-guide , February [9] The gzip home page, [10] The NCI Center for Bioinformatics, [11] The Protégé Ontology Editor and Knowledge Acquisition System, [12] Pankaj M. Tolani and Jayant R. Haritsa. XGrind: A Queryfriendly XML Compressor. In Proceedings of the 18th International Conference on Database Engineering, [13] Kevin Wilkinson, Craig Sayers, Harumi Kuno and Dave Reynolds. Efficient RDF storage and retrieval in Jena2. In First International Workshop on Semantic Web and Databases (SWDB 03, with VLDB03), September [14] World Wide Web Consortium. Resource Description Framework (RDF).