The Linked Movie Database (LinkedMDB) project provides a demonstration of the first open linked dataset connecting several major existing (and highly popular) movie web resources. The database exposed by LinkedMDB contains millions of RDF triples with hundreds of thousands of RDF links to existing web data sources that are part of the growing Linking Open Data cloud, as well as to popular movierelated web pages such as IMDb. LinkedMDB uses a novel way of creating and maintaining large quantities of high quality links by employing state-of-the-art approximate join techniques for finding links, and providing additional RDF metadata about the quality of the links and the techniques used for deriving them.
Movies are highly popular on the Web. There are several web resources dedicated to movies and many others containing movie-related information. Creating a single source of information about movies that contains information from existing open web data sources and links to other related data sources is a challenging task and the goal of the Linked Movie Data Base (LinkedMDB) project. LinkedMDB provides a high quality source of RDF data about movies (http://linkedmdb.org) that appeals to a wide audience, enabling further demonstrations of the linked data capabilities. Furthermore, LinkedMDB demonstrates the value of a novel class of tool to facilitate high volume and dense interlinking of RDF datasets.
Figure 1 shows an example of the entities and the interlinking in LinkedMDB. There are several challenges involved in identification of the entities in different data sources that should be interlinked. In some cases, the access to the data in target data source is limited. For example, only the title of the movies with their associated URLs can be obtained from the data source. In such cases, matching only the titles may not be sufficient due to different representations of the same title. Matching the movie title “The Shining” in LinkedMDB would miss the owl:sameAs link to the movie title “The Shining (film)” in DBpedia. SimiMusicBrainz “Béla Bartók”
Geonames "United States (US)” foaf:based_ne"aGrreat Britain (GB)" foaf:based_near owl:sameAs
Lingvoj “English”
IMDb “The Shining” RottenTomatoes
“The Shining”
Wikipedia “The Shining (film)” Freebase “The Shining” owl:sameAs “The_Shining_(film)” owl:sameAs "Stanley_Kubrick" “Béla_Bartók” larly, movie titles “A Thousand and One Nights” and “1001 Nights” would not match. Many non-English movie titles have different spellings in English, e.g., the titles “Adu Puli Attam” and “Sacco and Vanzetti” in LinkedMDB are written as “Aadu Puli Aattam” and “Sacco e Vanzetti” in DBpedia. This calls for approximate (or fuzzy) string matching for finding owl:sameAs links between the two sources.
However, exact or approximate matching of movie titles
could results in false matches. The movie “Chicago” (1927
movie) would link to the movie “Chicago” (2002 movie)
using exact matching. By approximate matching, movie titles
“Spiderman 1” and “Spiderman 2” have similar titles but are
not the same. There is a similar case for the movies “Face to
Face” and “Face to Fate”, and some adult movies that have
names very similar to popular Hollywood movies. Although
using proper string similarity function and specific record
matching techniques (e.g., using additional structural and
co-occurrence information as in [
In this paper, we present an overview of the movie data triplification effort showcased in LinkedMDB (Section 2). We then overview the interlinking of the data sources (Section 3), and provide a brief overview the approximate string matching techniques used for link discovery in relational data and present an evaluation of the performance of some of the techniques in LinkedMDB (Section 4). The need for linkage metadata and our approach for providing such data in LinkedMDB is discussed (Section 5). We conclude the paper by a brief discussion of a few future directions (Section 6).
Currently there are several sources of information on the web of documents: • IMDb is the biggest database of movies on the Web that provides a huge variety of up-to-date information about movies. Although IMDb data is available for download and personal use, it is strongly protected by copyright laws. Although we did transform the IMDb data to RDF, we could not get permission for publishing it and therefore our implementation does not include any information from IMDb although we include external links to IMDb pages whenever possible.. • FreeBase is an open, shared database of the world’s knowledge. The “film” category of freebase is one of the biggest and most complete domains in this database with more than 38,000 movies and thousands of other data items related to movies. Freebase has open data and has recently made its data available for download. Therefore, we use freebase as the nucleus of our database, although we do not limit our data source to the information available on freebase. • OMDB is another open data source of movies. The dataset currently contains information about more than 9,000 movies, and its data is available for public use. • DBpedia (Wikipedia) Movies: DBpedia contains a huge amount of information about more than 36,000 movies and thousands of related data items. We provide owl:sameAs links to DBpedia. Apart from extra information available in freebase such as movie characters and many other user-contributed data, we hope to serve additional information about movies and links to other data sources. This can be achieved due to the fact
• RottenTomatoes.com is another movie website with information about movies. RottenTomatoes data is not available for download and public use, however, we include foaf:page links to RottenTomatoes website as well. • Stanford Movie Database is a free database of movie information initially provided as a real test data for students. This database is relatively old, last updated in November 1999. Therefore it includes only a few data items that are not present in FreeBase. We however plan to extend our database with the additional information that can be obtained from this source. 2.2
Our database currently contains information about several entities including but not limited to movies, actors, movie characters, directors, producers, editors, writers, music composers and soundtracks, movie ratings and festivals. Table 2 shows the statistics for major entities in LinkedMDB. A list of all entities and facts in LinkedMDB will be made available in the extended version of this paper.
LinkedMDB provides links to several Linking Open Data (LOD) cloud datasets. Among these links are links to DBpedia, YAGO, flickr-wrapper, Geonames and lingvoj. Moreover, several data items are linked to external web pages such as pages on freebase, IMDb, OMDB, RottenTomotoes and Wikipedia.
LinkedMDB is connected to the following LOD data sources: • DBpedia/YAGO: Apart from the movie titles, person names (such as actors, writers and composers) are linked the related resources in DBpedia and YAGO data sources with owl:sameAs links. • Geonames: We interlink the countries of the movies to Geonames dataset by foaf:based near type of links. This is done by matching name of the countries in the two datasets. These links could be extended by matching featured locations of the movies to Geonames items. • FlickrWrapper: The moviess are linked to their photo collections using FlickrWrapper web service. These links are derived from the corresponding DBpedia URIs of the movies. • RDF book mashup: Movies are linked to their related stories.
Apart from links to LOD datasets, we also have setup foaf:page links to external webpages: • Freebase.com pages. • IMDb.com movies and actor profiles. • RottenTomatoes.com movie information and reviews.
Other potential links include links to external webpages from OMDB, boxoffice and movie show-times website and also homepages of the movies.
As mentioned earlier, link discovery often requires approximate matching of strings. In LinkedMDB, several links to other data sources are found using string matching. In this Section, we first briefly overview a set of string similarity functions and state-of-the art approximate string join techniques that are used (or can be used) in link discovery in LinkedMDB and similar link discovery settings. We then present the results of the evaluation of the quality of the links found using different similarity functions. 4.1
There exists a wide variety of similarity functions for
comparing similarity of the strings. The similarity measures we
discuss here share one or both of the following properties:
• High scalability: There are various techniques
proposed in the literature as described in Section 4.2 for
enhancing the performance of the similarity join
operation using q-grams along with these measures.
• High accuracy: Previous work has proved that in most
scenarios these measures perform better or equally well
in terms of accuracy comparing with other string
similarity measures. Specifically, these measures have shown
good accuracy in name-matching tasks [
Let r be the set of q-grams (i.e., sequences of length q of consecutive characters of a string) in string record r. For example, for r = ‘dblab0, r = {‘d0, ‘db0, ‘b0, ‘l0, ‘la0, ‘ab0, ‘b0} for tokenization using 2-grams . In certain cases, a weight may be associated with each token. 4.1.1
Edit distance between two string records r1 and r2 is defined as the transformation cost of r1 to r2, tc(r1, r2), which is equal to the minimum cost of edit operations applied to r1 to transform it to r2. Edit operations include character copy, insert, delete and substitute. The edit similarity is defined as: tc(r1, r2) simedit(r1, r2) = 1 − max{|r1|, |r2|} There is a cost associated with each edit operation. There are several cost models proposed for edit operations for this measure. In the most commonly used measure called Levenshtein edit distance, which we will refer to as edit distance in this paper, uses unit cost for all operations except copy which has cost zero. 4.1.2
Jaccard similarity is the fraction of tokens in r1 and r2 that are present in both. Weighted Jaccard similarity is the weighted version of Jaccard similarity, i.e., simW Jaccard(r1, r2) = Pt∈r1∪r2 wR(t)
Pt∈r1∩r2 wR(t)
where w(t, R) is a weight function that reflects the
commonality of the token t in the relation R. We choose RSJ
(Robertson-Sparck Jones) weight for the tokens which was
shown to be more effective than the commonly-used Inverse
Document Frequency (IDF) weights [
N − nt + 0.5 nt + 0.5 where N is the number of tuples in the base relation R and nt is the number of tuples in R containing the token t. 4.1.3
A well-studied problem in information retrieval is that
given a query and a collection of documents, return the
most relevant documents to the query. In the measures in
this part, records are treated as documents and q-grams are
seen as words (tokens) of the documents. Therefore same
techniques for finding relevant documents to a query can
be used to return similar records to a query string. In the
rest of this section, we present three measures that previous
work has shown their higher performance for approximate
selection problem [
Cosine w/tf-idf The tf-idf cosine similarity is a well established measure in the IR community which leverages the vector space model. This measure determines the closeness of the input strings r1 and r2 by first transforming the strings into unit vectors and then measuring the angle between their corresponding vectors. The cosine similarity with tf-idf weights is given by: (1) (2) (3)
X where wr1 (t) and wr2 (t) are the normalized tf-idf weights for each common token in r1 and r2 respectively. The normalized tf-idf weight of token t in a given string record r is defined as follows: wr(t) = qP wr0(t) , wr0(t) = tfr(t) · idf (t) where tfr(t) is the term frequency of token t within string r and idf (t) is the inverse document frequency with respect to the entire relation R. 4.1.4
BM25
The BM25 similarity score for a query r1 and a string record r2 is defined as follows:
X
where tfr(t) is the frequency of the token t in string record
r, |r| is the number of tokens in r, avgrl is the average
number of tokens per record, N is the number of records in
the relation R, nt is the number of record containing the
token t and k1, k3 and b are set of independent parameters.
We set these parameters as described in [
The approximate string matching could be modeled by
a discrete Hidden Markov process which has shown better
performance than Cosine w/tf-idf in the IR literature, and
high accuracy and running time for approximate selection
[
Y (a0P (t|GE) + a1P (t|r2)) t∈r1 (6) where a0 and a1 = 1 − a0 are the transition states probabilities of the Markov model and P (t|GE) and P (t|r2) is given by:
P (t|GE) =
P (t|r2) = number of times t appears in r2
|r2| Pr∈R number of times t appears in r
P r∈R |r| 4.2
An advantage of the similarity predicates described above
is that they can be implemented declaratively using
standard SQL queries over any relational DBMS. This is in
particular useful considering the fact that many existing linked
data sources are published using linked data publication
tools that operate over relational data sources, such as D2R
server, Triplify or OpenLink Vituoso. Some of the similarity
predicates can be made scalable to huge web data sources
using some of the specialized, high performance,
approximate join algorithms. Specifically, Enumeration (Enum)
and Weighted Enumeration (WtEnum) signature generation
algorithm can be used to significantly improve the running
time of the join with Jaccard and weighted Jaccard
predicates [
In this Section, we provide a summary of the evaluation of the accuracy of the linkage in only one of the linkage scenarios. More detailed comparison of the techniques in other scenarios (including other type of links such as rdfs:seeAlso links) will be made available in the extended version of this paper. • For each string in the query source, we find all those strings in the base source which have similarity score above a threshold θ by performing an approximate selection. • If there is only one string matched, then we output the query and base strings as certain matches. If there is more than one string or no string with similarity score above θ with the query string, then we do not output a match for the query string. 4.3.2
In our experiments we used q = 2 for generating q-grams as it showed better performance comparing with other values of q. Here, we present brief accuracy results for matching movie titles from DBpedia to movie titles in our database. We matched 38,064 movie titles in our database with 25,424 movie titles from DBpedia using the similarity predicates described above. We need to inspect different thresholds to see find the optimal threshold. Table 4 shows the number of matches obtained with different values of threshold as well as the accuracy obtained. Note that accuracy reported is the precision of the links, i.e., percentage of the output links that are correct. The recall is hard to find since the correct number of matches is not known. However, the number of links returned reflects the value of recall. The ground truth is obtained by manually finding all the rules for matching in this scenario. For example, all underscores are replaced with whitespaces, and the substring “(film)” is removed from the DBpedia movie titles. These rules themselves are discovered by running the similarity join and manually inspecting thousands of the links returned.
The results in Table 4 show that the weighted Jaccard similarity outperforms other predicates in this scenario in terms of the number of correct links found. Based on these results we chose threshold θ = 0.7 with weighted Jaccard similarity for the existing links in our database.
BM25
As shown in the accuracy evaluation in previous Section, although using proper string matching techniques and string similarity function could significantly reduce the amount of false matches, achieving 100% accuracy is not always possible or may result in fewer correct links. Therefore, it is plausible for the publisher to include metadata about the links and how are they are obtained. In LinkedMDB, we provide to entities, namely interlink and linkage run for this purpose. Figures 2 and 3 show examples of these entities. This approach has several exciting advantages. The users will be able to determine the type of the links and level of accuracy depending on the application. Furthermore, this will facilitate the process of judging the quality of the links by the users and therefore allowing the users to provide feedback on the quality of the links. 6.
LinkedMDB provides a high quality source of RDF data
about movies that appeals to a wide audience, enabling
further demonstrations of the linked data capabilities.
Furthermore, LinkedMDB demonstrates the value of a novel way of
link discovery and publishing linkage metadata to facilitate
high volume and dense interlinking of RDF datasets. We
plan to extend LinkedMDB in several aspects. Our plan
is to provide an easy-to-use interface to allow the users to
provide feedback on the quality of the links. In this way,
users will only need to report the quality of the links as
opposed to manually providing the links, as proposed in User
Contributed Interlinking framework of [