Spiliopoulou et al. (Eds.): From Data and Information Analysis to Knowledge Engineering
`Selected Papers from the 29th Annual Conference of the German Classification Society (GfKl)
`Magdeburg, ISBN 1431-8814, pp. 430-437, c(cid:2)Springer 2006.
`
`Near Similarity Search and
`Plagiarism Analysis
`
`Benno Stein1 and Sven Meyer zu Eissen2
`
`1 Faculty of Media, Media Systems
`Bauhaus University Weimar, 99421 Weimar, Germany
`2 Faculty of Computer Science,
`Paderborn University, 33098 Paderborn, Germany
`
`Abstract. Existing methods to text plagiarism analysis mainly base on “chunk-
`ing”, a process of grouping a text into meaningful units each of which gets encoded
`by an integer number. Together theses numbers form a document’s signature or fin-
`gerprint. An overlap of two documents’ fingerprints indicate a possibly plagiarized
`text passage. Most approaches use MD5 hashes to construct fingerprints, which is
`bound up with two problems: (i) it is computationally expensive, (ii) a small chunk
`size must be chosen to identify matching passages, which additionally increases the
`effort for fingerprint computation, fingerprint comparison, and fingerprint storage.
`This paper proposes a new class of fingerprints that can be considered as an ab-
`straction of the classical vector space model. These fingerprints operationalize the
`concept of “near similarity” and enable one to quickly identify candidate passages
`for plagiarism. Experiments show that a plagiarism analysis based on our finger-
`prints leads to a speed-up by a factor of five and higher—without compromising
`the recall performance.
`
`1 Plagiarism Analysis
`
`Plagiarism is the act of claiming to be the author of material that someone
`else actually wrote (Encyclopædia Britannica, 2005). This definition relates
`to text documents, which is also the focus of this paper. Clearly, a question of
`central importance is to what extent such and similar tasks can be automated.
`Several techniques for plagiarism analysis have been proposed in the past—
`most of them rely on one of the following ideas.
`
`Substring Matching. Substring matching approaches try to identify maximal
`matches in pairs of strings, which then are used as plagiarism indicators
`(Gusfield (1997)). Typically, the substrings are represented in suffix trees, and
`graph-based measures are employed to capture the fraction of the plagiarized
`sections (Baker (1993); Monostori et al. (2002, 2000)). However, Finkel et al.
`(2002) as well as Baker (1993) propose the use of text compression algorithms
`to identify matches.
`Keyword Similarity. The idea here is to extract and to weight topic-identifying
`keywords from a document and to compare them to the keywords of other
`documents. If the similarity exceeds a threshold, the candidate documents
`
`EX1056
`Roku V. Media Chain
`U.S. Patent No. 9,715,581
`
`0001
`
`

`

`are divided into smaller pieces, which then are compared recursively (Si et al.
`(1997); Fullam and Park (2002)). Note that this approach assumes that pla-
`giarism usually happens in topically similar documents.
`Fingerprint Analysis. The most popular approach to plagiarism analysis is
`the detection of overlapping text sequences by means of fingerprinting: Docu-
`ments are partitioned into term sequences, called chunks, from which digital
`digests are computed that form the document’s fingerprint. When the digests
`are inserted into a hashtable, collisions indicate matching sequences. Recent
`work that describes details and variants of this approach include Brin et al.
`(1995); Shivakumar and Garcia-Molina (1996); Finkel et al. (2002).
`
`1.1 Contributions of this Paper
`
`The overall contribution of this paper relates to the usage of fuzzy-fingerprints
`as an effective tool for plagiarism analysis. To understand different intentions
`for similarity search and plagiarism analysis we first introduce the distinction
`of local and global similarity. In fact, fuzzy-fingerprints can be understood
`as a combination of both paradigms, where the parameter “chunk size” con-
`trols the degree of locality. In particular, we use this distinction to develop a
`taxonomy of methods for plagiarism analysis. These considerations are pre-
`sented in the following section. Section 3 reports on experiments that quantify
`interesting properties of our approach.
`
`2 Fingerprinting, Similarity, and Plagiarism Analysis
`
`In the context of information retrieval a fingerprint h(d) of a document d
`can be considered as a set of encoded substrings taken from d, which serve to
`identify d uniquely.1 Following Hoad and Zobel (2003), the process of creating
`a fingerprint comprises four areas that need consideration.
`
`1. Substring Selection. From the original document substrings (chunks) are
`extracted according to some selection strategy. Such a strategy may con-
`sider positional, frequency-based, or structural information.
`2. Substring Number. The substring number defines the fingerprint resolu-
`tion. There is an obvious trade-off between fingerprint quality, processing
`effort, and storage requirements, which must be carefully balanced. The
`more information of a document is encoded in the fingerprint, the more
`reliably a possible collision of two fingerprints can be interpreted.
`3. Substring Size. The substring size defines the fingerprint granularity. A
`fine granularity makes a fingerprint more susceptible to false matches,
`while with a coarse granularity fingerprinting becomes very sensitive to
`changes.
`
`1 The term “signature” is sometimes also used in this connection.
`
`0002
`
`

`

`4. Substring Encoding. The selected substrings are mapped onto integer
`numbers. Substring conversion establishes a hash operation where—aside
`from uniqueness and uniformity—also efficiency is an important issue
`(Ramakrishna and Zobel (1997)). For this, the popular MD5 hashing
`algorithm is often employed (Rivest (1992)).
`
`If the main issue is similarity analysis and not unique identification, the
`entire document d is used during the substring formation step—i. e., the union
`of all chunks covers the entire document. The total set of integer numbers
`represents the fingerprint h(d). Note that the chunks may not be of uniform
`length but should be formed with the analysis task in mind.
`
`2.1 Local and Global Similarity Analysis
`
`For two documents A and B let h(A) and h(B) be their fingerprints with
`the respective resolutions |h(A)| and |h(B)|. Following Finkel et al. (2002), a
`similarity analysis between A and B that is based on h(A) and h(B) measures
`the portion of the fingerprint intersection:
`|h(A) ∩ h(B)|
`|h(A) ∪ h(B)|
`
`ϕlocal (A, B) =
`
`We call such a kind of similarity measure local similarity or overlap simi-
`larity, because it directly relates to the number of identical regions. By con-
`trast, the vector space model along with the cosine measure does not depend
`on identical regions: Two documents may have a similarity of 1 though they
`may not share any 2-gram. The vector space model along with the cosine mea-
`sure receives a global characteristic because it quantifies the term frequency
`of the entire document; in particular, the model neglects word order. Figure 1
`contrasts the principles of local and global similarity analysis pictorially.
`Basically, a fingerprint h(d) of a document d is nothing more than a
`special document model of d. In this sense, every information retrieval task
`that is based on a standard document model can also be operationalized with
`fingerprints. However, fingerprint methods are more flexible since they can
`be targeted specifically towards one of the following objectives:
`
`1. compactness—with respected to the document length
`2. fidelity—with respected to a local similarity analysis
`
`It is difficult to argue whether a fingerprint should be preferred to a
`standard document model in order to tackle a given information retrieval
`task. To better understand this problem of choosing an adequate document
`model we have developed a taxonomy of approaches to plagiarism analysis,
`which is shown in Figure 2. The approaches as well as the methods can be
`divided into local and global strategies. Note that in the literature on the
`subject local plagiarism analysis methods are encountered more often than
`global analysis methods. This is in the nature of things, since expropriating
`
`0003
`
`

`

`A
`
`Drawing the conclusion ``knowledge over search'' is obvious on
`the one hand, but too simple on the other: Among others, the
`question remains what can be done if the resource ``design know-
`ledge'' is not available or cannot be elicited, or is too expensive,
`or must tediously be experienced? Obviously we can learn from
`human problem solvers where to spend search effort deliberately
`in order to gain the maximum impact for automated problem
`solving. The paper in hand gives such an example: In Subsection
`2.1 we introduce the paradigm of functional abstraction to address
`behavior-based design problems. It develops from the search-
`plus-simulation paradigm by untwining the roles of search and
`simulation; in this way it forms a synthesis of the aforementioned
`approaches.
`
`Local similarity
`analysis,
`based on the
`overlap of
`contiguous
`sequences.
`
`A
`
`Drawing the conclusion ``knowledge over search'' is obvious on
`the one hand, but too simple on the other: Among others, the
`question remains what can be done if the resource ``design know-
`ledge'' is not available or cannot be elicited, or is too expensive,
`or must tediously be experienced? Obviously we can learn from
`human problem solvers where to spend search effort deliberately
`in order to gain the maximum impact for automated problem
`solving. The paper in hand gives such an example: In Subsection
`2.1 we introduce the paradigm of functional abstraction to address
`behavior-based design problems. It develops from the search-
`plus-simulation paradigm by untwining the roles of search and
`simulation; in this way it forms a synthesis of the aforementioned
`approaches.
`
`B
`
`at the first sight ``knowledge over search'' is obvious on the one
`hand, but too simple on the other: Among others, the question
`remains whether or not he could believe the alleged claim.
`However, most of us think that it develops from the search-plus-
`simulation paradigm. This way one could gain the maximum
`impact for automated diagnosis problem solving, simply by un-
`twining the roles of search and simulation.
`Human problem solving expertise is highly effective but of heu-
`ristic nature; moreover, it is hard to elicit but rather easy to
`process. Successful implementations of knowledge-based design
`algorithms don´t search in a gigantic space of behavior models
`but operate in a well defined structure space instead, which is
`spanned by compositional and taxonomic relations.
`
`Global similarity
`analysis,
`based on the
`shared part of
`the global
`term vectors.
`
`B
`
`at the first sight ``knowledge over search'' is obvious on the one
`hand, but too simple on the other: Among others, the question
`remains whether or not he could believe the alleged claim.
`However, most of us think that it develops from the search-plus-
`simulation paradigm. This way one could gain the maximum
`impact for automated diagnosis problem solving, simply by un-
`twining the roles of search and simulation.
`Human problem solving expertise is highly effective but of heu-
`ristic nature; moreover, it is hard to elicit but rather easy to
`process. Successful implementations of knowledge-based design
`algorithms don´t search in a gigantic space of behavior models
`but operate in a well defined structure space instead, which is
`spanned by compositional and taxonomic relations.
`
`Fig. 1. Two documents A and B which are analyzed regarding their similarity.
`The left-hand side illustrates a measure of local similarity: All matching contiguous
`sequences (chunks) with a length ≥ 5 words are highlighted. The right-hand side
`illustrates a measure of global similarity: Here the common word stems (without
`stopwords) of document A and B are highlighted. Observe that both similarity
`analyses may lead to the same similarity assessment.
`
`the exact wording of another author often relates to text passages rather than
`to the entire text. At the second level our taxonomy differentiates the local
`approaches with respect to the comparison rigor, and the global approaches
`with respect to statistical analysis versus style analysis.
`Among the shown approaches, the chunk identity analysis—usually oper-
`ationalized with the MD5 hashing algorithm—is the most popular approach
`to plagiarism analysis. Nevertheless, the method comes along with inherent
`disadvantages: (i) it is computationally expensive, and (ii) a small chunk size
`must be chosen (3-10 words), which has a negative impact to both retrieval
`and storage performance. Observe that all mentioned problems can be coun-
`tered, if the chunk size is drastically increased. This, however, requires some
`kind of fingerprints that operationalize a “relaxed” comparison concept.
`The following subsection adresses this problem. It introduces fuzzy-
`fingerprints, which are specifically tailored to text documents and which pro-
`vide the desired feature: an efficient means for near similarity analysis.
`
`2.2 Fingerprints that Capture Near Similarity
`
`While most fingerprint approaches rely on the original document d, from
`which chunks are selected and given to a hashing algorithm, our approach is
`based on the vector space model representation of the chunks. Key idea is
`a comparison of the distribution of the index terms in each chunk regarding
`their expected term frequency classes.
`
`0004
`
`

`

`Plagiarism
`analysis
`
`Local
`similarity
`
`Global
`similarity
`
`Chunk similarity
`analysis
`
`Chunk identity
`analysis
`
`Term occurrence
`analysis
`
`Style
`analysis
`
`MD5 Hashing
`
`Shingling
`
`Fuzzy-Fingerprint
`
`order-
`preserving
`methods
`
`order-
`neglecting
`methods
`
`Text structure
`analysis
`
`Linguistic
`analysis
`
`Methods
`
`order-
`preserving
`methods
`
`order-
`neglecting
`methods
`
`Suffix-tree model
`with tree-cover
`
`Vector space model
`with cos-measure
`
`Fig. 2. A taxonomy of approaches and methods to plagiarism analysis.
`
`In particular, we abstract the concept of term frequency classes towards
`prefix frequency classes by comprising index terms into a small number of
`equivalence classes, such that all terms from the same equivalence class start
`with a particular prefix. Then, grounded on the analysis of large corpora a
`reference distribution of index term frequencies can be computed, and, for a
`predefined set of prefixes, the a-priory probability of a term being member in a
`certain prefix class can be stated. The deviation of a chunk’s term distribution
`from these a-priory probabilities forms a chunk-specific characteristic that can
`be encoded as small integer.
`The basic procedure for constructing a fuzzy-fingerprint hϕ(d) for a doc-
`ument d is as follows:2
`1. Formation of a set C of chunks for d such that the extracted substrings
`c ∈ C cover d.
`2. For each chunk c ∈ C:
`(a) Computation of the vector space model c of c.
`(b) Computation of pf , the vector of relative frequencies of the prefix
`classes for the index terms in c.
`(c) Computation of Δpf , the vector of relative deviations of pf wrt. the
`expected prefix class distribution in the British National Corpus.
`
`2 Actually, the procedure is technically much more involved. It includes an
`alogrithm for chunking, the determination of suited prefix classes, the compu-
`tation of a reference distribution, and the identification as well as application of
`fuzzification schemes. Details can be found in Stein (2005).
`
`0005
`
`

`

`(d) Fuzzyfication of Δpf by abstracting the exact deviations towards a
`fuzzy deviation scheme with r intervals, and computation of a hash
`value γ:
`k−1(cid:2)
`
`γ =
`
`i=0
`
`δi · ri,
`
`with δi ∈ {0, . . . , r − 1}
`
`k is the number of prefix classes, and δi is the fuzzified deviation of
`the frequency of prefix class i.
`3. Formation of hϕ(d) as the union of the hash values γc, c ∈ C.
`
`Remarks. The granularity of the fingerprint is controlled within three dimen-
`sions: By the number of chunks, |C|, in Step 1, by the number of equivalence
`classes, k, in Step 2b, and by the resolution of the fuzzy deviation scheme, r,
`in Step 2d.
`
`3 Runtime Performance and Classification Characteristic
`
`This section presents results from a comparative analysis of the fuzzy-fin-
`gerprinting approach. In particular we investigate the following questions:
`
`1. Runtime Performance. To which extent is plagiarism identification accel-
`erated compared to MD5 fingerprinting?
`2. Classification Characteristic. How does fuzzy-fingerprinting relate to
`other local (MD5 fingerprinting) and global (vector space model) sim-
`ilarity measures?
`
`To answer these questions we set up different plagiarism experiments. The
`following plots result from a setting where as basis the RFC collection of
`the Internet Society was chosen: It comprises about 3000 documents with a
`considerable part of versioned sections. From this collection 50 documents
`were drawn randomly and compared to eight collection subsets with sizes be-
`tween 100 and 800 documents. Since this comparison relied on the documents’
`fingerprint representations, the number of observed collisions corresponds di-
`rectly to the runtime of the plagiarism analysis. Figure 3 reflects this fact: It
`shows the developing of the hash collisions (left) as well as the entire analy-
`sis time (right). The main reason for the large performance difference stems
`from the fact that fuzzy-fingerprinting allows for chunk sizes of 100 words on
`average, while MD5 fingerprinting works acceptable only for chunk sizes of 3
`to 10 words.
`The plot on the left-hand side in Figure 4 gives an answer to the question
`of how a document’s local and global similarity analysis are related. It shows
`the deviation of fingerprint-based similarity values compared to the respec-
`tive cosine similarity values under the vector space model, averaged over all
`documents of the RFC collection; observe that fuzzy-fingerprints resemble
`the cosine similarity better than MD5 fingerprints do. Especially against this
`background the plot on the right-hand side in Figure 4 must be interpreted:
`
`0006
`
`

`

`y-Axis: Runtime (sec) of plagiarism analysis
`x-Axis: Size of collection
`
`MD5 Fingerprint
`
`Fuzzy-Fingerprint
`
`12345678
`
`y-Axis: Number of collisions (* 1000)
`x-Axis: Size of collection
`
`MD5 Fingerprint
`
`Fuzzy-Fingerprint
`
` 20
` 18
` 16
` 14
` 12
` 10
`
`2468
`
` 100
`
` 200
`
` 300
`
` 400
`
` 500
`
` 600
`
` 700
`
` 800
`
` 100
`
` 200
`
` 300
`
` 400
`
` 500
`
` 600
`
` 700
`
` 800
`
`Fig. 3. Runtime performance of a plagiarism analysis task: 50 documents are com-
`pared to different subsets of the RFC collection. The figures show the runtime
`expressed in the number of fingerprint collisions (left) as well as in seconds (right).
`
`1.0
`
`0.8
`
`0.6
`
`0.4
`
`0.2
`
`
`
`y-Axis: Deviation from global similairty
`x-Axis: Global similarity (VSM + cos-similarity)
`
`MD5 Fingerprint
`
`Fuzzy-Fingerprint
`
`0.2
`
`0.4
`
`0.6
`
`0.8
`
` 1.0
`
`1.0
`
`0.8
`
`0.6
`
`0.4
`
`0.2
`
`
`
`y-Axis: Deviation from local similarity
`x-Axis: Local similarity (MD5 Fingerprint)
`
`Fuzzy-Fingerprint
`
`0.2
`
`0.4
`
`0.6
`
`0.8
`
` 1.0
`
`Fig. 4. Classification characteristics within the above plagiarism analysis task. Left:
`Similarity deviation of fingerprinting compared to the cosine similarity under the
`vector space model. Right: Similarity deviation of fuzzy-fingerprinting compared to
`optimum MD5 fingerprinting.
`
`The rather small deviation between fuzzy-fingerprints and the “optimum” fin-
`gerprint, which is a fine-grained MD5 fingerprint of chunk size 3, illustrates
`the robustness of fuzzy-fingerprinting.
`
`4 Summary
`
`To identify plagiarized versions of a document or of some parts of it, similar-
`ity analyses must be performed. In this connection the paper introduced the
`distinction of local and global similarity measures. Local similarity measures
`answer the question which percentage of two documents are equal; global
`similarity measures answer the question to which percentage the entire doc-
`uments are equal. This is a subtle but important difference, which leads to a
`taxonomy of methods for plagiarism analysis.
`Local methods for plagiarism analysis base on fingerprinting, and in this
`paper we propose a new class of fuzzy-fingerprints that can be considered as
`an abstraction of the classical vector space model. These fingerprints allow
`for chunk sizes that are an order of magnitude larger than the typical MD5
`
`0007
`
`

`

`digesting chunk sizes. As a consequence, the identification of plagiarism can-
`didates is advanced significantly (more than a factor of five)—while reducing
`the size of the fingerprint database at the same time.
`Our experiments also show the robustness of these fingerprints with re-
`spect to both large variations in the chunk size and the similarity range. Al-
`together, these properties make the concept of fuzzy-fingerprinting an ideal
`tool for plagiarism analysis and near similarity search in large document col-
`lections.
`
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