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LOD-Laundromat: Cleaning other people’s dirty data

Closed for maintenance! May be back soon

LOD Laundromat is a standard-compliant implementation for crawling and cleaning Linked Open Data (LOD). Since data cleaning is often reported as comprising 80% of a data analist’s workload, it would be great if we can automate at least a large part of that.

With the LOD Laundromat, data is cleaned in parallel by an arbitrary number of Washing Machine threads. The collection of washing machines can be monitored from a main Laundromat thread.

Data discovery


<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns="">

Semantic sitemap

<?xml version="1.0" encoding="UTF-8"?>
<urlset xmlns=""

Job sceduling

The cleaning process

Adding a seedpoint

Adding a new URI to the seedlist results in the following registration:

  relative: $(BOOL),
  status: added,
  uri: $(URI)

We are storing seedlist registrations in a SWI dictionary datastructure. SWI dictionaries are very similar to the JSON format for data exchange. The main difference is the $(HASH), which we will use to link seedlist registrations to one another.

The value of $(HASH) is computed in the following way:

  1. Take the value of $(URI)
  2. Map uppercase characters that appear in the scheme or host components to their corresponding lowercase characters[fn::See § of RFC 3986 (].
  3. Map lowercase characters that denote hexadecimal digits within a percent-encoded octet to their corresponding uppercase characters[fn::See § of RFC 3986 (].
  4. Decode percent-encoded octets that denote unreserved characters[fn::See § of RFC 3986 (].
  5. Remove the relative path references . and .. by applying reference resolution[fn::See § of RFC 3986 (].
  6. Take the MD5 hash.

We allow relative URIs to be added to the seedlist, denoted by the Boolean property relative. We cannot do anything useful with relative URIs, because their download location is unknown due to a missing host machine name. Still, we want to be able to quantify how often a dataset is erroneously denoted by a relative URI.

The status property is going to keep track of the URI throughout the data cleaning process. Its initial state is added, which means that it is added to the seedlist.

The uri property stores the URI itself. interval denotes the time in between consecutive crawls, expressed in seconds since the epoch. processed denotes the time of the last crawl.

  added: $(ADDED),
  child: $(HASH),
  interval: $(INTERVAL),
  processed: $(PROCESSED),
  uri: $(URI)

Downloading the file stream

This stage takes seeds that match the pattern in (1), and changes them to match pattern (2) during the download process. If the download fails we only have metadata $(HTTP_META) about the TCP and/or HTTP communication process, resulting in a seed record with pattern (3). If the download succeeds, there is also content metadata $(CONTENT_META), resulting in a seed record with pattern (4).

A seed is stale, and therefore a candicate for re-downloading, if $(PROCESSED) + $(INTERVAL) < $(NOW)

While downloading:

  parent: $(SEED_HASH),
  status: downloading

After downloading:

  http: [$(HTTP_META)],
  newline: $(NEWLINE),        %
  number_of_bytes: $(NONNEG), %
  number_of_chars: $(NONNEG), %
  number_of_lines: $(NONNEG)  %
  parent: $(SEED_HASH),
  status: filed,
  timestamp: $(BEGIN)-$(END)

The record includes the $(BEGIN)$ and $(END) times of the download.

$(HTTP_META) has the following form:

  headers: $(HTTP_HEADERS),
  status: $(STATUS_CODE),
  uri: $(URI),
  version: version{major: $(NONNEG), minor: $(NONNEG)},
  walltime: $(FLOAT)

Unpacking the file stream

This stage is started for each seed that matches [1]. If the seed denotes a downloaded file that is an archive, the resulting seed record will include pointer to each directly included ‘child’ file as in [3]. Status depleted denotes that no more files are enclosed within this file. For each child, a new seed record of the form [4] is added to the seedlist.

If the seed denotes a downloaded file that contains data, its seed record is updated to have status unarchived. We must determine the character encoding of the data file in order to be able to read it. Unfortunately, this can only be determined heuristically. We perform the following steps:

  1. We look for a Unicode Byte Order Marker (BOM), which indicates that the file has Unicode encoding.
  2. If not BOM is present, we use unchardet in order to guess the encoding. If the encoding is incompatible with Unicode[fn::An example of a common encoding that is compatible with Unicode is (US-)ASCII.], we recode the entire file using iconv.

Candidates for the unpacking stage have the following form:

$(ARCHIVE_HASH){status: filed}

While unpacking:

$(ENTRY_HASH){parent: $(ARCHIVE_HASH), status: unarchiving}

After unpacking:

$(ENTRY_HASH){status: unarchived} % leaf node
$(ARCHIVE_HASH){children: [$(ENTRY_HASH)], status: depleted} % non-leaf node
$(ENTRY_HASH){parent: $(ARCHIVE_HASH), status: filed} % future processing

Guess the Media Type / RDF serialization format

$(ENTRY_HASH){status: unarchived}
$(ENTRY_HASH){status: guessing}
$(ENTRY_HASH){format: $(FORMAT), status: guessed}

$(FORMAT) is one of the following values:

  1. JSON-LD
  2. N-Quads
  3. N-Triples
  4. RDF/XML
  5. RDFa
  6. TriG
  7. Turtle

Parsing the RDF

$(ENTRY_HASH){format: $(FORMAT), status: guessed}
$(ENTRY_HASH){status: parsing}
$(CLEAN_HASH){dirty: $(ENTRY_HASH), status: cleaned} % clean file
$(ENTRY_HASH){clean: $(CLEAN_HAHS), status: parsed} % dirty file


Cleaning other people's dirty data.




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